var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://raw.githubusercontent.com/EECi/home/main/docs/publications/bryn.bib\",\"commas\":\"true\",\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Bröchin\"],\"firstnames\":[\"Manuel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tröndle\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"doi\":\"10.1186/s13705-024-00458-z\",\"issn\":\"2192-0567\",\"journal\":\"Energ Sustain Soc\",\"keywords\":\"Benchmark, Energy system models, Interior-point, Linear programming, Numerical issues, Scaling, Simplex\",\"language\":\"en\",\"month\":\"June\",\"number\":\"1\",\"pages\":\"27\",\"shorttitle\":\"Harder, better, faster, stronger\",\"title\":\"Harder, better, faster, stronger: understanding and improving the tractability of large energy system models\",\"url\":\"https://doi.org/10.1186/s13705-024-00458-z\",\"urldate\":\"2024-07-10\",\"volume\":\"14\",\"year\":\"2024\",\"bibtex\":\"@article{brochin_harder_2024,\\n abstract = {Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue.},\\n author = {Bröchin, Manuel and Pickering, Bryn and Tröndle, Tim and Pfenninger, Stefan},\\n doi = {10.1186/s13705-024-00458-z},\\n issn = {2192-0567},\\n journal = {Energ Sustain Soc},\\n keywords = {Benchmark, Energy system models, Interior-point, Linear programming, Numerical issues, Scaling, Simplex},\\n language = {en},\\n month = {June},\\n number = {1},\\n pages = {27},\\n shorttitle = {Harder, better, faster, stronger},\\n title = {Harder, better, faster, stronger: understanding and improving the tractability of large energy system models},\\n url = {https://doi.org/10.1186/s13705-024-00458-z},\\n urldate = {2024-07-10},\\n volume = {14},\\n year = {2024}\\n}\\n\",\"author_short\":[\"Bröchin, M.\",\"Pickering, B.\",\"Tröndle, T.\",\"Pfenninger, S.\"],\"key\":\"brochin_harder_2024\",\"id\":\"brochin_harder_2024\",\"bibbaseid\":\"brchin-pickering-trndle-pfenninger-harderbetterfasterstrongerunderstandingandimprovingthetractabilityoflargeenergysystemmodels-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1186/s13705-024-00458-z\"},\"keyword\":[\"Benchmark\",\"Energy system models\",\"Interior-point\",\"Linear programming\",\"Numerical issues\",\"Scaling\",\"Simplex\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chang\"],\"firstnames\":[\"Miguel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Thellufsen\"],\"firstnames\":[\"Jakob\",\"Zink\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zakeri\"],\"firstnames\":[\"Behnam\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lund\"],\"firstnames\":[\"Henrik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Østergaard\"],\"firstnames\":[\"Poul\",\"Alberg\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.apenergy.2021.116731\",\"issn\":\"0306-2619\",\"journal\":\"Applied Energy\",\"keywords\":\"Energy models,Energy system analysis,Energy system modelling tool,Review,Survey\",\"language\":\"en\",\"month\":\"May\",\"pages\":\"116731\",\"title\":\"Trends in Tools and Approaches for Modelling the Energy Transition\",\"volume\":\"290\",\"year\":\"2021\",\"bibtex\":\"@article{Chang_Trends_2021,\\n abstract = {Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.},\\n author = {Chang, Miguel and Thellufsen, Jakob Zink and Zakeri, Behnam and Pickering, Bryn and Pfenninger, Stefan and Lund, Henrik and Østergaard, Poul Alberg},\\n doi = {10.1016/j.apenergy.2021.116731},\\n issn = {0306-2619},\\n journal = {Applied Energy},\\n keywords = {Energy models,Energy system analysis,Energy system modelling tool,Review,Survey},\\n language = {en},\\n month = {May},\\n pages = {116731},\\n title = {Trends in Tools and Approaches for Modelling the Energy Transition},\\n volume = {290},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"Chang, M.\",\"Thellufsen, J. Z.\",\"Zakeri, B.\",\"Pickering, B.\",\"Pfenninger, S.\",\"Lund, H.\",\"Østergaard, P. A.\"],\"key\":\"Chang_Trends_2021\",\"id\":\"Chang_Trends_2021\",\"bibbaseid\":\"chang-thellufsen-zakeri-pickering-pfenninger-lund-stergaard-trendsintoolsandapproachesformodellingtheenergytransition-2021\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Energy models\",\"Energy system analysis\",\"Energy system modelling tool\",\"Review\",\"Survey\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Craig\"],\"firstnames\":[\"Michael\",\"T.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wohland\"],\"firstnames\":[\"Jan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stoop\"],\"firstnames\":[\"Laurens\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kies\"],\"firstnames\":[\"Alexander\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bloomfield\"],\"firstnames\":[\"Hannah\",\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Browell\"],\"firstnames\":[\"Jethro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Felice\"],\"firstnames\":[\"Matteo\",\"De\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dent\"],\"firstnames\":[\"Chris\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Deroubaix\"],\"firstnames\":[\"Adrien\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Frischmuth\"],\"firstnames\":[\"Felix\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gonzalez\"],\"firstnames\":[\"Paula\",\"L.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Grochowicz\"],\"firstnames\":[\"Aleksander\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gruber\"],\"firstnames\":[\"Katharina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Härtel\"],\"firstnames\":[\"Philipp\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kittel\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kotzur\"],\"firstnames\":[\"Leander\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Labuhn\"],\"firstnames\":[\"Inga\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lundquist\"],\"firstnames\":[\"Julie\",\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pflugradt\"],\"firstnames\":[\"Noah\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wiel\"],\"firstnames\":[\"Karin\",\"van\",\"der\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zeyringer\"],\"firstnames\":[\"Marianne\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brayshaw\"],\"firstnames\":[\"David\",\"J.\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.joule.2022.05.010\",\"issn\":\"2542-4785, 2542-4351\",\"journal\":\"Joule\",\"language\":\"English\",\"month\":\"July\",\"note\":\"Publisher: Elsevier\",\"number\":\"7\",\"pages\":\"1405–1417\",\"title\":\"Overcoming the disconnect between energy system and climate modeling\",\"url\":\"https://www.cell.com/joule/abstract/S2542-4351(22)00237-9\",\"urldate\":\"2024-07-10\",\"volume\":\"6\",\"year\":\"2022\",\"bibtex\":\"@article{craig_overcoming_2022,\\n author = {Craig, Michael T. and Wohland, Jan and Stoop, Laurens P. and Kies, Alexander and Pickering, Bryn and Bloomfield, Hannah C. and Browell, Jethro and Felice, Matteo De and Dent, Chris J. and Deroubaix, Adrien and Frischmuth, Felix and Gonzalez, Paula L. M. and Grochowicz, Aleksander and Gruber, Katharina and Härtel, Philipp and Kittel, Martin and Kotzur, Leander and Labuhn, Inga and Lundquist, Julie K. and Pflugradt, Noah and Wiel, Karin van der and Zeyringer, Marianne and Brayshaw, David J.},\\n doi = {10.1016/j.joule.2022.05.010},\\n issn = {2542-4785, 2542-4351},\\n journal = {Joule},\\n language = {English},\\n month = {July},\\n note = {Publisher: Elsevier},\\n number = {7},\\n pages = {1405--1417},\\n title = {Overcoming the disconnect between energy system and climate modeling},\\n url = {https://www.cell.com/joule/abstract/S2542-4351(22)00237-9},\\n urldate = {2024-07-10},\\n volume = {6},\\n year = {2022}\\n}\\n\",\"author_short\":[\"Craig, M. T.\",\"Wohland, J.\",\"Stoop, L. P.\",\"Kies, A.\",\"Pickering, B.\",\"Bloomfield, H. C.\",\"Browell, J.\",\"Felice, M. D.\",\"Dent, C. J.\",\"Deroubaix, A.\",\"Frischmuth, F.\",\"Gonzalez, P. L. M.\",\"Grochowicz, A.\",\"Gruber, K.\",\"Härtel, P.\",\"Kittel, M.\",\"Kotzur, L.\",\"Labuhn, I.\",\"Lundquist, J. K.\",\"Pflugradt, N.\",\"Wiel, K. v. d.\",\"Zeyringer, M.\",\"Brayshaw, D. J.\"],\"key\":\"craig_overcoming_2022\",\"id\":\"craig_overcoming_2022\",\"bibbaseid\":\"craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.cell.com/joule/abstract/S2542-4351(22)00237-9\"},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Forman\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mutschler\"],\"firstnames\":[\"Roberta\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Guthrie\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Soulti\"],\"firstnames\":[\"Eleni\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Byström\"],\"firstnames\":[\"Viktor\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lee\"],\"firstnames\":[\"Si\",\"Min\"],\"suffixes\":[]}],\"booktitle\":\"Handbook of Theory and Practice of Sustainable Development in Higher Education\",\"month\":\"January\",\"pages\":\"245–266\",\"publisher\":\"Springer\",\"shorttitle\":\"Improving Building Energy Performance in Universities\",\"title\":\"Improving Building Energy Performance in Universities: The Case Study of the University of Cambridge\",\"year\":\"2017\",\"bibtex\":\"@incollection{Forman_Improving_2017,\\n author = {Forman, Tim and Mutschler, Roberta and Guthrie, Peter and Soulti, Eleni and Pickering, Bryn and Byström, Viktor and Lee, Si Min},\\n booktitle = {Handbook of Theory and Practice of Sustainable Development in Higher Education},\\n month = {January},\\n pages = {245--266},\\n publisher = {Springer},\\n shorttitle = {Improving Building Energy Performance in Universities},\\n title = {Improving Building Energy Performance in Universities: The Case Study of the University of Cambridge},\\n year = {2017}\\n}\\n\\n\",\"author_short\":[\"Forman, T.\",\"Mutschler, R.\",\"Guthrie, P.\",\"Soulti, E.\",\"Pickering, B.\",\"Byström, V.\",\"Lee, S. M.\"],\"key\":\"Forman_Improving_2017\",\"id\":\"Forman_Improving_2017\",\"bibbaseid\":\"forman-mutschler-guthrie-soulti-pickering-bystrm-lee-improvingbuildingenergyperformanceinuniversitiesthecasestudyoftheuniversityofcambridge-2017\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Lombardi\"],\"firstnames\":[\"Francesco\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colombo\"],\"firstnames\":[\"Emanuela\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.joule.2020.08.002\",\"issn\":\"2542-4785, 2542-4351\",\"journal\":\"Joule\",\"keywords\":\"decarbonization,energy modeling,Italy,near-optimal solutions,policy alternatives,renewables,spatially explicit,SPORES,uncertainty,wind deployment\",\"language\":\"English\",\"month\":\"August\",\"number\":\"0\",\"publisher\":\"Elsevier\",\"title\":\"Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives\",\"volume\":\"0\",\"year\":\"2020\",\"bibtex\":\"@article{Lombardi_Policy_2020,\\n author = {Lombardi, Francesco and Pickering, Bryn and Colombo, Emanuela and Pfenninger, Stefan},\\n doi = {10.1016/j.joule.2020.08.002},\\n issn = {2542-4785, 2542-4351},\\n journal = {Joule},\\n keywords = {decarbonization,energy modeling,Italy,near-optimal solutions,policy alternatives,renewables,spatially explicit,SPORES,uncertainty,wind deployment},\\n language = {English},\\n month = {August},\\n number = {0},\\n publisher = {Elsevier},\\n title = {Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives},\\n volume = {0},\\n year = {2020}\\n}\\n\\n\",\"author_short\":[\"Lombardi, F.\",\"Pickering, B.\",\"Colombo, E.\",\"Pfenninger, S.\"],\"key\":\"Lombardi_Policy_2020\",\"id\":\"Lombardi_Policy_2020\",\"bibbaseid\":\"lombardi-pickering-colombo-pfenninger-policydecisionsupportforrenewablesdeploymentthroughspatiallyexplicitpracticallyoptimalalternatives-2020\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"decarbonization\",\"energy modeling\",\"Italy\",\"near-optimal solutions\",\"policy alternatives\",\"renewables\",\"spatially explicit\",\"SPORES\",\"uncertainty\",\"wind deployment\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study’s central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Lombardi\"],\"firstnames\":[\"Francesco\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.apenergy.2023.121002\",\"issn\":\"0306-2619\",\"journal\":\"Applied Energy\",\"keywords\":\"MGA, On-shore wind, Optimization, Spatial dissimilarity, SPORES\",\"month\":\"June\",\"pages\":\"121002\",\"shorttitle\":\"What is redundant and what is not?\",\"title\":\"What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0306261923003665\",\"urldate\":\"2024-07-10\",\"volume\":\"339\",\"year\":\"2023\",\"bibtex\":\"@article{lombardi_what_2023,\\n abstract = {Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study’s central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.},\\n author = {Lombardi, Francesco and Pickering, Bryn and Pfenninger, Stefan},\\n doi = {10.1016/j.apenergy.2023.121002},\\n issn = {0306-2619},\\n journal = {Applied Energy},\\n keywords = {MGA, On-shore wind, Optimization, Spatial dissimilarity, SPORES},\\n month = {June},\\n pages = {121002},\\n shorttitle = {What is redundant and what is not?},\\n title = {What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment},\\n url = {https://www.sciencedirect.com/science/article/pii/S0306261923003665},\\n urldate = {2024-07-10},\\n volume = {339},\\n year = {2023}\\n}\\n\",\"author_short\":[\"Lombardi, F.\",\"Pickering, B.\",\"Pfenninger, S.\"],\"key\":\"lombardi_what_2023\",\"id\":\"lombardi_what_2023\",\"bibbaseid\":\"lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0306261923003665\"},\"keyword\":[\"MGA\",\"On-shore wind\",\"Optimization\",\"Spatial dissimilarity\",\"SPORES\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Net-zero energy system configurations can be met in numerous ways, implying diverse economic effects. However, what is usually ignored in techno-economic and economy-wide analysis are the distinct social-political drivers and barriers, which might constrain certain elements of future energy systems. We thus apply a model ensemble that defines social-political storylines which constrain feasible net-zero configurations of the European energy system. Using these configurations in a macroeconomic general equilibrium model allows us to explore economy-wide effects and ultimately the cost-effectiveness of different systems. We find that social-political storylines provide valuable boundary conditions for feasible net-zero designs of the energy system and that the costliest energy sector configuration in fact leads to the highest European-wide welfare levels. This result originates in indirect effects, particularly positive employment effects, covered by the macroeconomic model. However, adverse public budget effects on the transition to net-zero energy may limit the willingness of policymakers who focus on shorter time-horizons to foster such a development. Our results highlight the relevance of considering the interaction of energy system-changes with labor, emission allowance and capital markets, as well as considering long-term perspectives.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Mayer\"],\"firstnames\":[\"Jakob\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Süsser\"],\"firstnames\":[\"Diana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bachner\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sanvito\"],\"firstnames\":[\"Francesco\",\"Davide\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.energy.2024.130425\",\"issn\":\"0360-5442\",\"journal\":\"Energy\",\"keywords\":\"Climate change mitigation, Computable general equilibrium, Cost-effectiveness, Energy system design, Social-political storylines\",\"month\":\"March\",\"pages\":\"130425\",\"title\":\"Economy-wide impacts of socio-politically driven net-zero energy systems in europe\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0360544224001968\",\"urldate\":\"2024-07-10\",\"volume\":\"291\",\"year\":\"2024\",\"bibtex\":\"@article{mayer_economy-wide_2024,\\n abstract = {Net-zero energy system configurations can be met in numerous ways, implying diverse economic effects. However, what is usually ignored in techno-economic and economy-wide analysis are the distinct social-political drivers and barriers, which might constrain certain elements of future energy systems. We thus apply a model ensemble that defines social-political storylines which constrain feasible net-zero configurations of the European energy system. Using these configurations in a macroeconomic general equilibrium model allows us to explore economy-wide effects and ultimately the cost-effectiveness of different systems. We find that social-political storylines provide valuable boundary conditions for feasible net-zero designs of the energy system and that the costliest energy sector configuration in fact leads to the highest European-wide welfare levels. This result originates in indirect effects, particularly positive employment effects, covered by the macroeconomic model. However, adverse public budget effects on the transition to net-zero energy may limit the willingness of policymakers who focus on shorter time-horizons to foster such a development. Our results highlight the relevance of considering the interaction of energy system-changes with labor, emission allowance and capital markets, as well as considering long-term perspectives.},\\n author = {Mayer, Jakob and Süsser, Diana and Pickering, Bryn and Bachner, Gabriel and Sanvito, Francesco Davide},\\n doi = {10.1016/j.energy.2024.130425},\\n issn = {0360-5442},\\n journal = {Energy},\\n keywords = {Climate change mitigation, Computable general equilibrium, Cost-effectiveness, Energy system design, Social-political storylines},\\n month = {March},\\n pages = {130425},\\n title = {Economy-wide impacts of socio-politically driven net-zero energy systems in europe},\\n url = {https://www.sciencedirect.com/science/article/pii/S0360544224001968},\\n urldate = {2024-07-10},\\n volume = {291},\\n year = {2024}\\n}\\n\",\"author_short\":[\"Mayer, J.\",\"Süsser, D.\",\"Pickering, B.\",\"Bachner, G.\",\"Sanvito, F. D.\"],\"key\":\"mayer_economy-wide_2024\",\"id\":\"mayer_economy-wide_2024\",\"bibbaseid\":\"mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0360544224001968\"},\"keyword\":[\"Climate change mitigation\",\"Computable general equilibrium\",\"Cost-effectiveness\",\"Energy system design\",\"Social-political storylines\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.\",\"archiveprefix\":\"arXiv\",\"author\":[{\"propositions\":[],\"lastnames\":[\"McKenna\"],\"firstnames\":[\"Russell\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Heinrichs\"],\"firstnames\":[\"Heidi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schmidt\"],\"firstnames\":[\"Johannes\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Staffell\"],\"firstnames\":[\"Iain\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gruber\"],\"firstnames\":[\"Katharina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hahmann\"],\"firstnames\":[\"Andrea\",\"N.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jansen\"],\"firstnames\":[\"Malte\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Klingler\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Landwehr\"],\"firstnames\":[\"Natascha\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Larsén\"],\"firstnames\":[\"Xiaoli\",\"Guo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lilliestam\"],\"firstnames\":[\"Johan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Robinius\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tröndle\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Turkovska\"],\"firstnames\":[\"Olga\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wehrle\"],\"firstnames\":[\"Sebastian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Weinand\"],\"firstnames\":[\"Jann\",\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wohland\"],\"firstnames\":[\"Jan\"],\"suffixes\":[]}],\"eprint\":\"2103.09781\",\"eprinttype\":\"arxiv\",\"journal\":\"arXiv:2103.09781 [econ, q-fin]\",\"keywords\":\"Economics - General Economics\",\"month\":\"March\",\"primaryclass\":\"econ, q-fin\",\"title\":\"Reviewing Methods and Assumptions for High-Resolution Large-Scale Onshore Wind Energy Potential Assessments\",\"year\":\"2021\",\"bibtex\":\"@article{McKenna_Reviewing_2021,\\n abstract = {The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.},\\n archiveprefix = {arXiv},\\n author = {McKenna, Russell and Pfenninger, Stefan and Heinrichs, Heidi and Schmidt, Johannes and Staffell, Iain and Gruber, Katharina and Hahmann, Andrea N. and Jansen, Malte and Klingler, Michael and Landwehr, Natascha and Larsén, Xiaoli Guo and Lilliestam, Johan and Pickering, Bryn and Robinius, Martin and Tröndle, Tim and Turkovska, Olga and Wehrle, Sebastian and Weinand, Jann Michael and Wohland, Jan},\\n eprint = {2103.09781},\\n eprinttype = {arxiv},\\n journal = {arXiv:2103.09781 [econ, q-fin]},\\n keywords = {Economics - General Economics},\\n month = {March},\\n primaryclass = {econ, q-fin},\\n title = {Reviewing Methods and Assumptions for High-Resolution Large-Scale Onshore Wind Energy Potential Assessments},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"McKenna, R.\",\"Pfenninger, S.\",\"Heinrichs, H.\",\"Schmidt, J.\",\"Staffell, I.\",\"Gruber, K.\",\"Hahmann, A. N.\",\"Jansen, M.\",\"Klingler, M.\",\"Landwehr, N.\",\"Larsén, X. G.\",\"Lilliestam, J.\",\"Pickering, B.\",\"Robinius, M.\",\"Tröndle, T.\",\"Turkovska, O.\",\"Wehrle, S.\",\"Weinand, J. M.\",\"Wohland, J.\"],\"key\":\"McKenna_Reviewing_2021\",\"id\":\"McKenna_Reviewing_2021\",\"bibbaseid\":\"mckenna-pfenninger-heinrichs-schmidt-staffell-gruber-hahmann-jansen-etal-reviewingmethodsandassumptionsforhighresolutionlargescaleonshorewindenergypotentialassessments-2021\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Economics - General Economics\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]}],\"doi\":\"10.21105/joss.00825\",\"journal\":\"The Journal of Open Source Software\",\"language\":\"en\",\"month\":\"September\",\"number\":\"29\",\"pages\":\"825\",\"shorttitle\":\"Calliope\",\"title\":\"Calliope: A Multi-Scale Energy Systems Modelling Framework\",\"volume\":\"3\",\"year\":\"2018\",\"bibtex\":\"@article{Pfenninger_Calliope_2018,\\n author = {Pfenninger, Stefan and Pickering, Bryn},\\n doi = {10.21105/joss.00825},\\n journal = {The Journal of Open Source Software},\\n language = {en},\\n month = {September},\\n number = {29},\\n pages = {825},\\n shorttitle = {Calliope},\\n title = {Calliope: A Multi-Scale Energy Systems Modelling Framework},\\n volume = {3},\\n year = {2018}\\n}\\n\\n\",\"author_short\":[\"Pfenninger, S.\",\"Pickering, B.\"],\"key\":\"Pfenninger_Calliope_2018\",\"id\":\"Pfenninger_Calliope_2018\",\"bibbaseid\":\"pfenninger-pickering-calliopeamultiscaleenergysystemsmodellingframework-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"San Diego, USA\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of the 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems\",\"month\":\"July\",\"pages\":\"1080–1092\",\"title\":\"Applying Piecewise Linear Characteristic Curves in District Energy Optimisation\",\"year\":\"2017\",\"bibtex\":\"@inproceedings{Pickering_Applying_2017,\\n address = {San Diego, USA},\\n author = {Pickering, Bryn and Choudhary, Ruchi},\\n booktitle = {Proceedings of the 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems},\\n month = {July},\\n pages = {1080--1092},\\n title = {Applying Piecewise Linear Characteristic Curves in District Energy Optimisation},\\n year = {2017}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Choudhary, R.\"],\"key\":\"Pickering_Applying_2017\",\"id\":\"Pickering_Applying_2017\",\"bibbaseid\":\"pickering-choudhary-applyingpiecewiselinearcharacteristiccurvesindistrictenergyoptimisation-2017\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Aalborg, Denmark\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ikeda\"],\"firstnames\":[\"Shintaro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ooka\"],\"firstnames\":[\"Ryozo\"],\"suffixes\":[]}],\"booktitle\":\"12th REHVA World Congress\",\"isbn\":\"87-91606-31-4\",\"month\":\"May\",\"publisher\":\"Aalborg University, Department of Civil Engineering\",\"title\":\"Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule\",\"volume\":\"6\",\"year\":\"2016\",\"bibtex\":\"@inproceedings{Pickering_Comparison_2016,\\n address = {Aalborg, Denmark},\\n author = {Pickering, Bryn and Ikeda, Shintaro and Choudhary, Ruchi and Ooka, Ryozo},\\n booktitle = {12th REHVA World Congress},\\n isbn = {87-91606-31-4},\\n month = {May},\\n publisher = {Aalborg University, Department of Civil Engineering},\\n title = {Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule},\\n volume = {6},\\n year = {2016}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Ikeda, S.\",\"Choudhary, R.\",\"Ooka, R.\"],\"key\":\"Pickering_Comparison_2016\",\"id\":\"Pickering_Comparison_2016\",\"bibbaseid\":\"pickering-ikeda-choudhary-ooka-comparisonofmetaheuristicandlinearprogrammingmodelsforthepurposeofoptimisingbuildingenergysupplyoperationschedule-2016\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"R.\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.apenergy.2018.12.037\",\"issn\":\"0306-2619\",\"journal\":\"Applied Energy\",\"keywords\":\"Data-driven demand,District energy systems,Mixed integer linear optimisation,Scenario optimisation,Scenario reduction\",\"month\":\"February\",\"pages\":\"1138–1157\",\"shorttitle\":\"District Energy System Optimisation under Uncertain Demand\",\"title\":\"District Energy System Optimisation under Uncertain Demand: Handling Data-Driven Stochastic Profiles\",\"volume\":\"236\",\"year\":\"2019\",\"bibtex\":\"@article{Pickering_District_2019,\\n abstract = {Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.},\\n author = {Pickering, B. and Choudhary, R.},\\n doi = {10.1016/j.apenergy.2018.12.037},\\n issn = {0306-2619},\\n journal = {Applied Energy},\\n keywords = {Data-driven demand,District energy systems,Mixed integer linear optimisation,Scenario optimisation,Scenario reduction},\\n month = {February},\\n pages = {1138--1157},\\n shorttitle = {District Energy System Optimisation under Uncertain Demand},\\n title = {District Energy System Optimisation under Uncertain Demand: Handling Data-Driven Stochastic Profiles},\\n volume = {236},\\n year = {2019}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Choudhary, R.\"],\"key\":\"Pickering_District_2019\",\"id\":\"Pickering_District_2019\",\"bibbaseid\":\"pickering-choudhary-districtenergysystemoptimisationunderuncertaindemandhandlingdatadrivenstochasticprofiles-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Data-driven demand\",\"District energy systems\",\"Mixed integer linear optimisation\",\"Scenario optimisation\",\"Scenario reduction\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Disagreements persist on how to design a self-sufficient, carbon-neutral European energy system. To explore the diversity of design options, we develop a high-resolution model of the entire European energy system and produce 441 technically feasible system designs that are within 10% of the optimal economic cost. We show that a wide range of systems based on renewable energy are feasible, with no need to import energy from outside Europe. Model solutions reveal considerable flexibility in the choice and geographical distribution of new infrastructure across the continent. Balanced renewable energy supply can be achieved either with or without mechanisms such as biofuel use, curtailment, and expansion of the electricity network. Trade-offs emerge once specific preferences are imposed. Low biofuel use, for example, requires heat electrification and controlled vehicle charging. This exploration of the impact of preferences on system design options is vital to inform urgent, politically difficult decisions for eliminating fossil fuel imports and achieving European carbon neutrality.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lombardi\"],\"firstnames\":[\"Francesco\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.joule.2022.05.009\",\"issn\":\"2542-4785, 2542-4351\",\"journal\":\"Joule\",\"language\":\"English\",\"month\":\"June\",\"note\":\"Publisher: Elsevier\",\"number\":\"6\",\"pages\":\"1253–1276\",\"title\":\"Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system\",\"url\":\"https://www.cell.com/joule/abstract/S2542-4351(22)00236-7\",\"urldate\":\"2024-07-10\",\"volume\":\"6\",\"year\":\"2022\",\"bibtex\":\"@article{pickering_diversity_2022,\\n abstract = {Disagreements persist on how to design a self-sufficient, carbon-neutral European energy system. To explore the diversity of design options, we develop a high-resolution model of the entire European energy system and produce 441 technically feasible system designs that are within 10% of the optimal economic cost. We show that a wide range of systems based on renewable energy are feasible, with no need to import energy from outside Europe. Model solutions reveal considerable flexibility in the choice and geographical distribution of new infrastructure across the continent. Balanced renewable energy supply can be achieved either with or without mechanisms such as biofuel use, curtailment, and expansion of the electricity network. Trade-offs emerge once specific preferences are imposed. Low biofuel use, for example, requires heat electrification and controlled vehicle charging. This exploration of the impact of preferences on system design options is vital to inform urgent, politically difficult decisions for eliminating fossil fuel imports and achieving European carbon neutrality.},\\n author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},\\n doi = {10.1016/j.joule.2022.05.009},\\n issn = {2542-4785, 2542-4351},\\n journal = {Joule},\\n language = {English},\\n month = {June},\\n note = {Publisher: Elsevier},\\n number = {6},\\n pages = {1253--1276},\\n title = {Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system},\\n url = {https://www.cell.com/joule/abstract/S2542-4351(22)00236-7},\\n urldate = {2024-07-10},\\n volume = {6},\\n year = {2022}\\n}\\n\",\"author_short\":[\"Pickering, B.\",\"Lombardi, F.\",\"Pfenninger, S.\"],\"key\":\"pickering_diversity_2022\",\"id\":\"pickering_diversity_2022\",\"bibbaseid\":\"pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.cell.com/joule/abstract/S2542-4351(22)00236-7\"},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"Master's Thesis\",\"address\":\"Cambridge, UK\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]}],\"month\":\"August\",\"school\":\"University of Cambridge\",\"title\":\"Electricity Infrastructure in Rapidly Developing Cities\",\"year\":\"2015\",\"bibtex\":\"@phdthesis{Pickering_Electricity_2015,\\n address = {Cambridge, UK},\\n author = {Pickering, Bryn},\\n month = {August},\\n school = {University of Cambridge},\\n title = {Electricity Infrastructure in Rapidly Developing Cities},\\n type = {Master's Thesis},\\n year = {2015}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\"],\"key\":\"Pickering_Electricity_2015\",\"id\":\"Pickering_Electricity_2015\",\"bibbaseid\":\"pickering-electricityinfrastructureinrapidlydevelopingcities-2015\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.\",\"address\":\"Cambridge, UK\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of BSO 2018\",\"language\":\"en\",\"month\":\"September\",\"pages\":\"38–45\",\"title\":\"Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation\",\"year\":\"2018\",\"bibtex\":\"@inproceedings{Pickering_Mitigating_2018,\\n abstract = {Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.},\\n address = {Cambridge, UK},\\n author = {Pickering, Bryn and Choudhary, Ruchi},\\n booktitle = {Proceedings of BSO 2018},\\n language = {en},\\n month = {September},\\n pages = {38--45},\\n title = {Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation},\\n year = {2018}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Choudhary, R.\"],\"key\":\"Pickering_Mitigating_2018\",\"id\":\"Pickering_Mitigating_2018\",\"bibbaseid\":\"pickering-choudhary-mitigatingriskindistrictlevelenergyinvestmentdecisionsbyscenariooptimisation-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"techreport\",\"type\":\"Deliverable\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chang\"],\"firstnames\":[\"Miguel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Thellufsen\"],\"firstnames\":[\"Jakob\",\"Zinck\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Roelfsema\"],\"firstnames\":[\"Mark\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mikropoulos\"],\"firstnames\":[\"Stratos\"],\"suffixes\":[]},{\"propositions\":[\"van\"],\"lastnames\":[\"Vuuren\"],\"firstnames\":[\"Detlef\"],\"suffixes\":[]}],\"month\":\"February\",\"number\":\"Deliverable 4.2 of the SENTINEL project funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 837089\",\"title\":\"Modelling in Support to the Transition to a Low-Carbon Energy System in Europe: Model Development to Match System Design Models to User Needs\",\"year\":\"2021\",\"bibtex\":\"@techreport{Pickering_Modelling_2021,\\n author = {Pickering, Bryn and Chang, Miguel and Thellufsen, Jakob Zinck and Roelfsema, Mark and Mikropoulos, Stratos and van Vuuren, Detlef},\\n month = {February},\\n number = {Deliverable 4.2 of the SENTINEL project funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 837089},\\n title = {Modelling in Support to the Transition to a Low-Carbon Energy System in Europe:  Model Development to Match System Design Models to User Needs},\\n type = {Deliverable},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Chang, M.\",\"Thellufsen, J. Z.\",\"Roelfsema, M.\",\"Mikropoulos, S.\",\"van Vuuren, D.\"],\"key\":\"Pickering_Modelling_2021\",\"id\":\"Pickering_Modelling_2021\",\"bibbaseid\":\"pickering-chang-thellufsen-roelfsema-mikropoulos-vanvuuren-modellinginsupporttothetransitiontoalowcarbonenergysystemineuropemodeldevelopmenttomatchsystemdesignmodelstouserneeds-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"PhD Thesis\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Brynmor\",\"Caradog\"],\"suffixes\":[]}],\"month\":\"May\",\"school\":\"University of Cambridge\",\"shorttitle\":\"Practical Optimisation of District Energy Systems\",\"title\":\"Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness\",\"year\":\"2019\",\"bibtex\":\"@phdthesis{Pickering_Practical_2019,\\n author = {Pickering, Brynmor Caradog},\\n month = {May},\\n school = {University of Cambridge},\\n shorttitle = {Practical Optimisation of District Energy Systems},\\n title = {Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness},\\n type = {PhD Thesis},\\n year = {2019}\\n}\\n\\n\",\"author_short\":[\"Pickering, B. C.\"],\"key\":\"Pickering_Practical_2019\",\"id\":\"Pickering_Practical_2019\",\"bibbaseid\":\"pickering-practicaloptimisationofdistrictenergysystemsrepresentationoftechnologycharacteristicsdemanduncertaintyandsystemrobustness-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO2 emissions, and aversion to risk. We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO2 emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]}],\"doi\":\"10.1016/j.apenergy.2021.116465\",\"issn\":\"0306-2619\",\"journal\":\"Applied Energy\",\"keywords\":\"District energy systems,Mixed integer linear optimisation,Out-of-sample testing,Resilient systems,Scenario optimisation,Two-stage stochastic programming\",\"language\":\"en\",\"month\":\"March\",\"pages\":\"116465\",\"title\":\"Quantifying Resilience in Energy Systems with Out-of-Sample Testing\",\"volume\":\"285\",\"year\":\"2021\",\"bibtex\":\"@article{Pickering_Quantifying_2021,\\n abstract = {The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO2 emissions, and aversion to risk. We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO2 emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.},\\n author = {Pickering, Bryn and Choudhary, Ruchi},\\n doi = {10.1016/j.apenergy.2021.116465},\\n issn = {0306-2619},\\n journal = {Applied Energy},\\n keywords = {District energy systems,Mixed integer linear optimisation,Out-of-sample testing,Resilient systems,Scenario optimisation,Two-stage stochastic programming},\\n language = {en},\\n month = {March},\\n pages = {116465},\\n title = {Quantifying Resilience in Energy Systems with Out-of-Sample Testing},\\n volume = {285},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Choudhary, R.\"],\"key\":\"Pickering_Quantifying_2021\",\"id\":\"Pickering_Quantifying_2021\",\"bibbaseid\":\"pickering-choudhary-quantifyingresilienceinenergysystemswithoutofsampletesting-2021\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"District energy systems\",\"Mixed integer linear optimisation\",\"Out-of-sample testing\",\"Resilient systems\",\"Scenario optimisation\",\"Two-stage stochastic programming\"],\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"Master's Thesis\",\"address\":\"Cambridge, UK\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]}],\"month\":\"May\",\"school\":\"University of Cambridge\",\"title\":\"Renewable Energy on the University Estate\",\"year\":\"2014\",\"bibtex\":\"@phdthesis{Pickering_Renewable_2014,\\n address = {Cambridge, UK},\\n author = {Pickering, Bryn},\\n month = {May},\\n school = {University of Cambridge},\\n title = {Renewable Energy on the University Estate},\\n type = {Master's Thesis},\\n year = {2014}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\"],\"key\":\"Pickering_Renewable_2014\",\"id\":\"Pickering_Renewable_2014\",\"bibbaseid\":\"pickering-renewableenergyontheuniversityestate-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"The future European electricity system will depend heavily on variable renewable generation, including wind power. To plan and operate reliable electricity supply systems, an understanding of wind power variability over a range of spatio-temporal scales is critical. In complex terrain, such as that found in mountainous Switzerland, wind speeds are influenced by a multitude of meteorological phenomena, many of which occur on scales too fine to capture with commonly used meteorological reanalysis datasets. Past work has shown that anticorrelation at a continental scale is an important way to help balance variable generation. Here, we investigate systematically for the first time the possibility of balancing wind variability by exploiting anticorrelation between weather patterns in complex terrain. We assess the capability for the Consortium for Small-scale Modeling (COSMO)-REA2 and COSMO-REA6 reanalyses (with a 2 and 6 km horizontal resolution, respectively) to reproduce historical measured data from weather stations, hub height anemometers, and wind turbine electricity generation across Switzerland. Both reanalyses are insufficient to reproduce site-specific wind speeds in Switzerland’s complex terrain. We find however that mountain-valley breezes, orographic channelling, and variability imposed by European-scale weather regimes are represented by COSMO-REA2. We discover multi-day periods of wind electricity generation in regions of Switzerland which are anticorrelated with neighbouring European countries. Our results suggest that significantly more work is needed to understand the impact of fine scale wind power variability on national and continental electricity systems, and that higher-resolution reanalyses are necessary to accurately understand the local variability of renewable generation in complex terrain.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Grams\"],\"firstnames\":[\"Christian\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"doi\":\"10.1088/1748-9326/ab70bd\",\"issn\":\"1748-9326\",\"journal\":\"Environmental Research Letters\",\"language\":\"en\",\"month\":\"January\",\"publisher\":\"IOP Publishing\",\"title\":\"Sub-National Variability of Wind Power Generation in Complex Terrain and Its Correlation with Large-Scale Meteorology\",\"year\":\"2020\",\"bibtex\":\"@article{Pickering_Subnational_2020,\\n abstract = {The future European electricity system will depend heavily on variable renewable generation, including wind power. To plan and operate reliable electricity supply systems, an understanding of wind power variability over a range of spatio-temporal scales is critical. In complex terrain, such as that found in mountainous Switzerland, wind speeds are influenced by a multitude of meteorological phenomena, many of which occur on scales too fine to capture with commonly used meteorological reanalysis datasets. Past work has shown that anticorrelation at a continental scale is an important way to help balance variable generation. Here, we investigate systematically for the first time the possibility of balancing wind variability by exploiting anticorrelation between weather patterns in complex terrain. We assess the capability for the Consortium for Small-scale Modeling (COSMO)-REA2 and COSMO-REA6 reanalyses (with a 2 and 6 km horizontal resolution, respectively) to reproduce historical measured data from weather stations, hub height anemometers, and wind turbine electricity generation across Switzerland. Both reanalyses are insufficient to reproduce site-specific wind speeds in Switzerland’s complex terrain. We find however that mountain-valley breezes, orographic channelling, and variability imposed by European-scale weather regimes are represented by COSMO-REA2. We discover multi-day periods of wind electricity generation in regions of Switzerland which are anticorrelated with neighbouring European countries. Our results suggest that significantly more work is needed to understand the impact of fine scale wind power variability on national and continental electricity systems, and that higher-resolution reanalyses are necessary to accurately understand the local variability of renewable generation in complex terrain.},\\n author = {Pickering, Bryn and Grams, Christian M. and Pfenninger, Stefan},\\n doi = {10.1088/1748-9326/ab70bd},\\n issn = {1748-9326},\\n journal = {Environmental Research Letters},\\n language = {en},\\n month = {January},\\n publisher = {IOP Publishing},\\n title = {Sub-National Variability of Wind Power Generation in Complex Terrain and Its Correlation with Large-Scale Meteorology},\\n year = {2020}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Grams, C. M.\",\"Pfenninger, S.\"],\"key\":\"Pickering_Subnational_2020\",\"id\":\"Pickering_Subnational_2020\",\"bibbaseid\":\"pickering-grams-pfenninger-subnationalvariabilityofwindpowergenerationincomplexterrainanditscorrelationwithlargescalemeteorology-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Shone et al., (2024). PAM: Population Activity Modeller. Journal of Open Source Software, 9(96), 6097, https://doi.org/10.21105/joss.06097\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shone\"],\"firstnames\":[\"Fred\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chatziioannou\"],\"firstnames\":[\"Theodore\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kozlowska\"],\"firstnames\":[\"Kasia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fitzmaurice\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"doi\":\"10.21105/joss.06097\",\"issn\":\"2475-9066\",\"journal\":\"Journal of Open Source Software\",\"language\":\"en\",\"month\":\"April\",\"number\":\"96\",\"pages\":\"6097\",\"shorttitle\":\"PAM\",\"title\":\"PAM: Population Activity Modeller\",\"url\":\"https://joss.theoj.org/papers/10.21105/joss.06097\",\"urldate\":\"2024-07-10\",\"volume\":\"9\",\"year\":\"2024\",\"bibtex\":\"@article{shone_pam_2024,\\n abstract = {Shone et al., (2024). PAM: Population Activity Modeller. Journal of Open Source Software, 9(96), 6097, https://doi.org/10.21105/joss.06097},\\n author = {Shone, Fred and Chatziioannou, Theodore and Pickering, Bryn and Kozlowska, Kasia and Fitzmaurice, Michael},\\n doi = {10.21105/joss.06097},\\n issn = {2475-9066},\\n journal = {Journal of Open Source Software},\\n language = {en},\\n month = {April},\\n number = {96},\\n pages = {6097},\\n shorttitle = {PAM},\\n title = {PAM: Population Activity Modeller},\\n url = {https://joss.theoj.org/papers/10.21105/joss.06097},\\n urldate = {2024-07-10},\\n volume = {9},\\n year = {2024}\\n}\\n\",\"author_short\":[\"Shone, F.\",\"Chatziioannou, T.\",\"Pickering, B.\",\"Kozlowska, K.\",\"Fitzmaurice, M.\"],\"key\":\"shone_pam_2024\",\"id\":\"shone_pam_2024\",\"bibbaseid\":\"shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://joss.theoj.org/papers/10.21105/joss.06097\"},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Energy models are used to explore decarbonisation pathways and potential future energy systems. In this editorial, we comment on the importance of energy system modelling and open tools to inform policymaking in the context of the European Green Deal. We also summarise the seven contributions to the special collection on Energy Systems Modelling, among which are papers that have been presented at the Energy Modelling Platform for Europe (EMP-E) 2021 conference. The presented research advances current modelling approaches and supports energy modelling with open tools and datasets.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Süsser\"],\"firstnames\":[\"Diana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hülk\"],\"firstnames\":[\"Ludwig\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"doi\":\"10.12688/f1000research.121619.1\",\"issn\":\"2046-1402\",\"journal\":\"F1000Res\",\"month\":\"May\",\"pages\":\"531\",\"pmcid\":\"PMC9111364\",\"pmid\":\"35615495\",\"title\":\"Open energy system modelling to support the European Green Deal\",\"url\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/\",\"urldate\":\"2024-07-10\",\"volume\":\"11\",\"year\":\"2022\",\"bibtex\":\"@article{susser_open_2022,\\n abstract = {Energy models are used to explore decarbonisation pathways and potential future energy systems. In this editorial, we comment on the importance of energy system modelling and open tools to inform policymaking in the context of the European Green Deal. We also summarise the seven contributions to the special collection on Energy Systems Modelling, among which are papers that have been presented at the Energy Modelling Platform for Europe (EMP-E) 2021 conference. The presented research advances current modelling approaches and supports energy modelling with open tools and datasets.},\\n author = {Süsser, Diana and Pickering, Bryn and Hülk, Ludwig and Pfenninger, Stefan},\\n doi = {10.12688/f1000research.121619.1},\\n issn = {2046-1402},\\n journal = {F1000Res},\\n month = {May},\\n pages = {531},\\n pmcid = {PMC9111364},\\n pmid = {35615495},\\n title = {Open energy system modelling to support the European Green Deal},\\n url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/},\\n urldate = {2024-07-10},\\n volume = {11},\\n year = {2022}\\n}\\n\",\"author_short\":[\"Süsser, D.\",\"Pickering, B.\",\"Hülk, L.\",\"Pfenninger, S.\"],\"key\":\"susser_open_2022\",\"id\":\"susser_open_2022\",\"bibbaseid\":\"ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/\"},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"$<$p$><$strong class=\\\"journal-contentHeaderColor\\\"$>$Abstract.$<$/strong$>$ Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20 year timescales by one order of magnitude. Albeit smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind related infrastructure like wind energy.$<$/p$>$\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wohland\"],\"firstnames\":[\"Jan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Folini\"],\"firstnames\":[\"Doris\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]}],\"doi\":\"10.5194/esd-2021-29\",\"issn\":\"2190-4979\",\"journal\":\"Earth System Dynamics Discussions\",\"language\":\"English\",\"month\":\"April\",\"pages\":\"1–27\",\"publisher\":\"Copernicus GmbH\",\"title\":\"Wind Speed Stilling and Its Recovery Due to Internal Climate Variability\",\"year\":\"2021\",\"bibtex\":\"@article{Wohland_Wind_2021,\\n abstract = {$<$p$><$strong class=\\\"journal-contentHeaderColor\\\"$>$Abstract.$<$/strong$>$ Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20 year timescales by one order of magnitude. Albeit smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind related infrastructure like wind energy.$<$/p$>$},\\n author = {Wohland, Jan and Folini, Doris and Pickering, Bryn},\\n doi = {10.5194/esd-2021-29},\\n issn = {2190-4979},\\n journal = {Earth System Dynamics Discussions},\\n language = {English},\\n month = {April},\\n pages = {1--27},\\n publisher = {Copernicus GmbH},\\n title = {Wind Speed Stilling and Its Recovery Due to Internal Climate Variability},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"Wohland, J.\",\"Folini, D.\",\"Pickering, B.\"],\"key\":\"Wohland_Wind_2021\",\"id\":\"Wohland_Wind_2021\",\"bibbaseid\":\"wohland-folini-pickering-windspeedstillinganditsrecoveryduetointernalclimatevariability-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Martin\"],\"firstnames\":[\"Nicholas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Madrid-López\"],\"firstnames\":[\"Cristina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Talens-Peiró\"],\"firstnames\":[\"Laura\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]}],\"booktitle\":\"EGU21\",\"doi\":\"10.5194/egusphere-egu21-16017\",\"language\":\"en\",\"month\":\"March\",\"publisher\":\"Copernicus Meetings\",\"shorttitle\":\"How Clean Is Your \\\"Clean\\\" Energy?\",\"title\":\"How Clean Is Your \\\"Clean\\\" Energy? The ENVIRO Module for Energy System Models\",\"year\":\"2021\",\"bibtex\":\"@inproceedings{Martin_How_2021,\\n author = {Martin, Nicholas and Madrid-López, Cristina and Talens-Peiró, Laura and Pickering, Bryn},\\n booktitle = {EGU21},\\n doi = {10.5194/egusphere-egu21-16017},\\n language = {en},\\n month = {March},\\n publisher = {Copernicus Meetings},\\n shorttitle = {How Clean Is Your \\\"Clean\\\" Energy?},\\n title = {How Clean Is Your \\\"Clean\\\" Energy? The ENVIRO Module for Energy System Models},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"Martin, N.\",\"Madrid-López, C.\",\"Talens-Peiró, L.\",\"Pickering, B.\"],\"key\":\"Martin_How_2021\",\"id\":\"Martin_How_2021\",\"bibbaseid\":\"martin-madridlpez-talenspeir-pickering-howcleanisyourcleanenergytheenviromoduleforenergysystemmodels-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"A decarbonised European energy system will require a number of potentially contested decisions on where best to locate renewable generation capacity. Typically, modellers determine the &amp;#8220;best&amp;#8221; system based on the least cost to society, focussing on a cost-minimising energy system model result to inform planning and policy. This approach neglects potentially more desirable alternative results which might, for example, avoid problematic concentrations of onshore wind power deployment, increase national supply security, or lower the risk of system failure in adverse weather conditions.&lt;/p&gt;&lt;p&gt;In response, we have developed a method to generate spatially explicit, practically optimal results (SPORES) in the context of energy system optimisation. SPORES can be used to explore energy systems which may offer more socially, politically, or environmentally acceptable alternatives. Furthermore, we have developed metrics to aid identification of interesting alternatives, like those which maximise the spatial distribution of wind generation capacity or minimise exposure to multi-year demand and weather uncertainty.&lt;/p&gt;&lt;p&gt;In this presentation, we will detail the application of the SPORES method in two cases of energy system decarbonisation: &amp;#160;the Italian power system and the European energy system. We will present technology deployment strategies which are prevalent across SPORES, such as solar photovoltaics coupled with battery storage, as well as those which offer flexibility of choice in location and extent of deployment. To help with the urgent task of planning socially and politically acceptable energy system decarbonisation strategies, our implementation of SPORES in the open-source energy systems modelling framework Calliope makes it accessible to a wide range of potential users; we will also discuss how other research groups can further build on this to accelerate the energy transition.&lt;/p&gt;\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pickering\"],\"firstnames\":[\"Bryn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lombardi\"],\"firstnames\":[\"Francesco\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfenninger\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"booktitle\":\"EGU21\",\"doi\":\"10.5194/egusphere-egu21-16205\",\"month\":\"March\",\"publisher\":\"Copernicus Meetings\",\"title\":\"Decision Support for Renewables Deployment through Spatially Explicit Energy System Alternatives\",\"year\":\"2021\",\"bibtex\":\"@inproceedings{Pickering_Decision_2021,\\n abstract = {A decarbonised European energy system will require a number of potentially contested decisions on where best to locate renewable generation capacity. Typically, modellers determine the &amp;#8220;best&amp;#8221; system based on the least cost to society, focussing on a cost-minimising energy system model result to inform planning and policy. This approach neglects potentially more desirable alternative results which might, for example, avoid problematic concentrations of onshore wind power deployment, increase national supply security, or lower the risk of system failure in adverse weather conditions.&lt;/p&gt;&lt;p&gt;In response, we have developed a method to generate spatially explicit, practically optimal results (SPORES) in the context of energy system optimisation. SPORES can be used to explore energy systems which may offer more socially, politically, or environmentally acceptable alternatives. Furthermore, we have developed metrics to aid identification of interesting alternatives, like those which maximise the spatial distribution of wind generation capacity or minimise exposure to multi-year demand and weather uncertainty.&lt;/p&gt;&lt;p&gt;In this presentation, we will detail the application of the SPORES method in two cases of energy system decarbonisation: &amp;#160;the Italian power system and the European energy system. We will present technology deployment strategies which are prevalent across SPORES, such as solar photovoltaics coupled with battery storage, as well as those which offer flexibility of choice in location and extent of deployment. To help with the urgent task of planning socially and politically acceptable energy system decarbonisation strategies, our implementation of SPORES in the open-source energy systems modelling framework Calliope makes it accessible to a wide range of potential users; we will also discuss how other research groups can further build on this to accelerate the energy transition.&lt;/p&gt;},\\n author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},\\n booktitle = {EGU21},\\n doi = {10.5194/egusphere-egu21-16205},\\n month = {March},\\n publisher = {Copernicus Meetings},\\n title = {Decision Support for Renewables Deployment through Spatially Explicit Energy System Alternatives},\\n year = {2021}\\n}\\n\\n\",\"author_short\":[\"Pickering, B.\",\"Lombardi, F.\",\"Pfenninger, S.\"],\"key\":\"Pickering_Decision_2021\",\"id\":\"Pickering_Decision_2021\",\"bibbaseid\":\"pickering-lombardi-pfenninger-decisionsupportforrenewablesdeploymentthroughspatiallyexplicitenergysystemalternatives-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\"html\":\"\"}],\n      metadata: {\"owner\":\"pickering, b\",\"authorlinks\":{\"pickering, b\":\"https://eeci.github.io/home/docs/people/bryn/\"}},\n      ownerID: \"PcKh2j59GnGM2ebwi\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"bryn.bib\" href=\"data:text/bibtex;base64,@article{brochin_harder_2024,
 abstract = {Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue.},
 author = {Bröchin, Manuel and Pickering, Bryn and Tröndle, Tim and Pfenninger, Stefan},
 doi = {10.1186/s13705-024-00458-z},
 issn = {2192-0567},
 journal = {Energ Sustain Soc},
 keywords = {Benchmark, Energy system models, Interior-point, Linear programming, Numerical issues, Scaling, Simplex},
 language = {en},
 month = {June},
 number = {1},
 pages = {27},
 shorttitle = {Harder, better, faster, stronger},
 title = {Harder, better, faster, stronger: understanding and improving the tractability of large energy system models},
 url = {https://doi.org/10.1186/s13705-024-00458-z},
 urldate = {2024-07-10},
 volume = {14},
 year = {2024}
}


@article{Chang_Trends_2021,
 abstract = {Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.},
 author = {Chang, Miguel and Thellufsen, Jakob Zink and Zakeri, Behnam and Pickering, Bryn and Pfenninger, Stefan and Lund, Henrik and Østergaard, Poul Alberg},
 doi = {10.1016/j.apenergy.2021.116731},
 issn = {0306-2619},
 journal = {Applied Energy},
 keywords = {Energy models,Energy system analysis,Energy system modelling tool,Review,Survey},
 language = {en},
 month = {May},
 pages = {116731},
 title = {Trends in Tools and Approaches for Modelling the Energy Transition},
 volume = {290},
 year = {2021}
}



@article{craig_overcoming_2022,
 author = {Craig, Michael T. and Wohland, Jan and Stoop, Laurens P. and Kies, Alexander and Pickering, Bryn and Bloomfield, Hannah C. and Browell, Jethro and Felice, Matteo De and Dent, Chris J. and Deroubaix, Adrien and Frischmuth, Felix and Gonzalez, Paula L. M. and Grochowicz, Aleksander and Gruber, Katharina and Härtel, Philipp and Kittel, Martin and Kotzur, Leander and Labuhn, Inga and Lundquist, Julie K. and Pflugradt, Noah and Wiel, Karin van der and Zeyringer, Marianne and Brayshaw, David J.},
 doi = {10.1016/j.joule.2022.05.010},
 issn = {2542-4785, 2542-4351},
 journal = {Joule},
 language = {English},
 month = {July},
 note = {Publisher: Elsevier},
 number = {7},
 pages = {1405--1417},
 title = {Overcoming the disconnect between energy system and climate modeling},
 url = {https://www.cell.com/joule/abstract/S2542-4351(22)00237-9},
 urldate = {2024-07-10},
 volume = {6},
 year = {2022}
}


@incollection{Forman_Improving_2017,
 author = {Forman, Tim and Mutschler, Roberta and Guthrie, Peter and Soulti, Eleni and Pickering, Bryn and Byström, Viktor and Lee, Si Min},
 booktitle = {Handbook of Theory and Practice of Sustainable Development in Higher Education},
 month = {January},
 pages = {245--266},
 publisher = {Springer},
 shorttitle = {Improving Building Energy Performance in Universities},
 title = {Improving Building Energy Performance in Universities: The Case Study of the University of Cambridge},
 year = {2017}
}



@article{Lombardi_Policy_2020,
 author = {Lombardi, Francesco and Pickering, Bryn and Colombo, Emanuela and Pfenninger, Stefan},
 doi = {10.1016/j.joule.2020.08.002},
 issn = {2542-4785, 2542-4351},
 journal = {Joule},
 keywords = {decarbonization,energy modeling,Italy,near-optimal solutions,policy alternatives,renewables,spatially explicit,SPORES,uncertainty,wind deployment},
 language = {English},
 month = {August},
 number = {0},
 publisher = {Elsevier},
 title = {Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives},
 volume = {0},
 year = {2020}
}



@article{lombardi_what_2023,
 abstract = {Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study’s central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.},
 author = {Lombardi, Francesco and Pickering, Bryn and Pfenninger, Stefan},
 doi = {10.1016/j.apenergy.2023.121002},
 issn = {0306-2619},
 journal = {Applied Energy},
 keywords = {MGA, On-shore wind, Optimization, Spatial dissimilarity, SPORES},
 month = {June},
 pages = {121002},
 shorttitle = {What is redundant and what is not?},
 title = {What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment},
 url = {https://www.sciencedirect.com/science/article/pii/S0306261923003665},
 urldate = {2024-07-10},
 volume = {339},
 year = {2023}
}


@article{mayer_economy-wide_2024,
 abstract = {Net-zero energy system configurations can be met in numerous ways, implying diverse economic effects. However, what is usually ignored in techno-economic and economy-wide analysis are the distinct social-political drivers and barriers, which might constrain certain elements of future energy systems. We thus apply a model ensemble that defines social-political storylines which constrain feasible net-zero configurations of the European energy system. Using these configurations in a macroeconomic general equilibrium model allows us to explore economy-wide effects and ultimately the cost-effectiveness of different systems. We find that social-political storylines provide valuable boundary conditions for feasible net-zero designs of the energy system and that the costliest energy sector configuration in fact leads to the highest European-wide welfare levels. This result originates in indirect effects, particularly positive employment effects, covered by the macroeconomic model. However, adverse public budget effects on the transition to net-zero energy may limit the willingness of policymakers who focus on shorter time-horizons to foster such a development. Our results highlight the relevance of considering the interaction of energy system-changes with labor, emission allowance and capital markets, as well as considering long-term perspectives.},
 author = {Mayer, Jakob and Süsser, Diana and Pickering, Bryn and Bachner, Gabriel and Sanvito, Francesco Davide},
 doi = {10.1016/j.energy.2024.130425},
 issn = {0360-5442},
 journal = {Energy},
 keywords = {Climate change mitigation, Computable general equilibrium, Cost-effectiveness, Energy system design, Social-political storylines},
 month = {March},
 pages = {130425},
 title = {Economy-wide impacts of socio-politically driven net-zero energy systems in europe},
 url = {https://www.sciencedirect.com/science/article/pii/S0360544224001968},
 urldate = {2024-07-10},
 volume = {291},
 year = {2024}
}


@article{McKenna_Reviewing_2021,
 abstract = {The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.},
 archiveprefix = {arXiv},
 author = {McKenna, Russell and Pfenninger, Stefan and Heinrichs, Heidi and Schmidt, Johannes and Staffell, Iain and Gruber, Katharina and Hahmann, Andrea N. and Jansen, Malte and Klingler, Michael and Landwehr, Natascha and Larsén, Xiaoli Guo and Lilliestam, Johan and Pickering, Bryn and Robinius, Martin and Tröndle, Tim and Turkovska, Olga and Wehrle, Sebastian and Weinand, Jann Michael and Wohland, Jan},
 eprint = {2103.09781},
 eprinttype = {arxiv},
 journal = {arXiv:2103.09781 [econ, q-fin]},
 keywords = {Economics - General Economics},
 month = {March},
 primaryclass = {econ, q-fin},
 title = {Reviewing Methods and Assumptions for High-Resolution Large-Scale Onshore Wind Energy Potential Assessments},
 year = {2021}
}



@article{Pfenninger_Calliope_2018,
 author = {Pfenninger, Stefan and Pickering, Bryn},
 doi = {10.21105/joss.00825},
 journal = {The Journal of Open Source Software},
 language = {en},
 month = {September},
 number = {29},
 pages = {825},
 shorttitle = {Calliope},
 title = {Calliope: A Multi-Scale Energy Systems Modelling Framework},
 volume = {3},
 year = {2018}
}



@inproceedings{Pickering_Applying_2017,
 address = {San Diego, USA},
 author = {Pickering, Bryn and Choudhary, Ruchi},
 booktitle = {Proceedings of the 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems},
 month = {July},
 pages = {1080--1092},
 title = {Applying Piecewise Linear Characteristic Curves in District Energy Optimisation},
 year = {2017}
}



@inproceedings{Pickering_Comparison_2016,
 address = {Aalborg, Denmark},
 author = {Pickering, Bryn and Ikeda, Shintaro and Choudhary, Ruchi and Ooka, Ryozo},
 booktitle = {12th REHVA World Congress},
 isbn = {87-91606-31-4},
 month = {May},
 publisher = {Aalborg University, Department of Civil Engineering},
 title = {Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule},
 volume = {6},
 year = {2016}
}



@article{Pickering_District_2019,
 abstract = {Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.},
 author = {Pickering, B. and Choudhary, R.},
 doi = {10.1016/j.apenergy.2018.12.037},
 issn = {0306-2619},
 journal = {Applied Energy},
 keywords = {Data-driven demand,District energy systems,Mixed integer linear optimisation,Scenario optimisation,Scenario reduction},
 month = {February},
 pages = {1138--1157},
 shorttitle = {District Energy System Optimisation under Uncertain Demand},
 title = {District Energy System Optimisation under Uncertain Demand: Handling Data-Driven Stochastic Profiles},
 volume = {236},
 year = {2019}
}



@article{pickering_diversity_2022,
 abstract = {Disagreements persist on how to design a self-sufficient, carbon-neutral European energy system. To explore the diversity of design options, we develop a high-resolution model of the entire European energy system and produce 441 technically feasible system designs that are within 10% of the optimal economic cost. We show that a wide range of systems based on renewable energy are feasible, with no need to import energy from outside Europe. Model solutions reveal considerable flexibility in the choice and geographical distribution of new infrastructure across the continent. Balanced renewable energy supply can be achieved either with or without mechanisms such as biofuel use, curtailment, and expansion of the electricity network. Trade-offs emerge once specific preferences are imposed. Low biofuel use, for example, requires heat electrification and controlled vehicle charging. This exploration of the impact of preferences on system design options is vital to inform urgent, politically difficult decisions for eliminating fossil fuel imports and achieving European carbon neutrality.},
 author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},
 doi = {10.1016/j.joule.2022.05.009},
 issn = {2542-4785, 2542-4351},
 journal = {Joule},
 language = {English},
 month = {June},
 note = {Publisher: Elsevier},
 number = {6},
 pages = {1253--1276},
 title = {Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system},
 url = {https://www.cell.com/joule/abstract/S2542-4351(22)00236-7},
 urldate = {2024-07-10},
 volume = {6},
 year = {2022}
}


@phdthesis{Pickering_Electricity_2015,
 address = {Cambridge, UK},
 author = {Pickering, Bryn},
 month = {August},
 school = {University of Cambridge},
 title = {Electricity Infrastructure in Rapidly Developing Cities},
 type = {Master's Thesis},
 year = {2015}
}



@inproceedings{Pickering_Mitigating_2018,
 abstract = {Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.},
 address = {Cambridge, UK},
 author = {Pickering, Bryn and Choudhary, Ruchi},
 booktitle = {Proceedings of BSO 2018},
 language = {en},
 month = {September},
 pages = {38--45},
 title = {Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation},
 year = {2018}
}



@techreport{Pickering_Modelling_2021,
 author = {Pickering, Bryn and Chang, Miguel and Thellufsen, Jakob Zinck and Roelfsema, Mark and Mikropoulos, Stratos and van Vuuren, Detlef},
 month = {February},
 number = {Deliverable 4.2 of the SENTINEL project funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 837089},
 title = {Modelling in Support to the Transition to a Low-Carbon Energy System in Europe:  Model Development to Match System Design Models to User Needs},
 type = {Deliverable},
 year = {2021}
}



@phdthesis{Pickering_Practical_2019,
 author = {Pickering, Brynmor Caradog},
 month = {May},
 school = {University of Cambridge},
 shorttitle = {Practical Optimisation of District Energy Systems},
 title = {Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness},
 type = {PhD Thesis},
 year = {2019}
}



@article{Pickering_Quantifying_2021,
 abstract = {The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO2 emissions, and aversion to risk. We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO2 emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.},
 author = {Pickering, Bryn and Choudhary, Ruchi},
 doi = {10.1016/j.apenergy.2021.116465},
 issn = {0306-2619},
 journal = {Applied Energy},
 keywords = {District energy systems,Mixed integer linear optimisation,Out-of-sample testing,Resilient systems,Scenario optimisation,Two-stage stochastic programming},
 language = {en},
 month = {March},
 pages = {116465},
 title = {Quantifying Resilience in Energy Systems with Out-of-Sample Testing},
 volume = {285},
 year = {2021}
}



@phdthesis{Pickering_Renewable_2014,
 address = {Cambridge, UK},
 author = {Pickering, Bryn},
 month = {May},
 school = {University of Cambridge},
 title = {Renewable Energy on the University Estate},
 type = {Master's Thesis},
 year = {2014}
}



@article{Pickering_Subnational_2020,
 abstract = {The future European electricity system will depend heavily on variable renewable generation, including wind power. To plan and operate reliable electricity supply systems, an understanding of wind power variability over a range of spatio-temporal scales is critical. In complex terrain, such as that found in mountainous Switzerland, wind speeds are influenced by a multitude of meteorological phenomena, many of which occur on scales too fine to capture with commonly used meteorological reanalysis datasets. Past work has shown that anticorrelation at a continental scale is an important way to help balance variable generation. Here, we investigate systematically for the first time the possibility of balancing wind variability by exploiting anticorrelation between weather patterns in complex terrain. We assess the capability for the Consortium for Small-scale Modeling (COSMO)-REA2 and COSMO-REA6 reanalyses (with a 2 and 6 km horizontal resolution, respectively) to reproduce historical measured data from weather stations, hub height anemometers, and wind turbine electricity generation across Switzerland. Both reanalyses are insufficient to reproduce site-specific wind speeds in Switzerland’s complex terrain. We find however that mountain-valley breezes, orographic channelling, and variability imposed by European-scale weather regimes are represented by COSMO-REA2. We discover multi-day periods of wind electricity generation in regions of Switzerland which are anticorrelated with neighbouring European countries. Our results suggest that significantly more work is needed to understand the impact of fine scale wind power variability on national and continental electricity systems, and that higher-resolution reanalyses are necessary to accurately understand the local variability of renewable generation in complex terrain.},
 author = {Pickering, Bryn and Grams, Christian M. and Pfenninger, Stefan},
 doi = {10.1088/1748-9326/ab70bd},
 issn = {1748-9326},
 journal = {Environmental Research Letters},
 language = {en},
 month = {January},
 publisher = {IOP Publishing},
 title = {Sub-National Variability of Wind Power Generation in Complex Terrain and Its Correlation with Large-Scale Meteorology},
 year = {2020}
}



@article{shone_pam_2024,
 abstract = {Shone et al., (2024). PAM: Population Activity Modeller. Journal of Open Source Software, 9(96), 6097, https://doi.org/10.21105/joss.06097},
 author = {Shone, Fred and Chatziioannou, Theodore and Pickering, Bryn and Kozlowska, Kasia and Fitzmaurice, Michael},
 doi = {10.21105/joss.06097},
 issn = {2475-9066},
 journal = {Journal of Open Source Software},
 language = {en},
 month = {April},
 number = {96},
 pages = {6097},
 shorttitle = {PAM},
 title = {PAM: Population Activity Modeller},
 url = {https://joss.theoj.org/papers/10.21105/joss.06097},
 urldate = {2024-07-10},
 volume = {9},
 year = {2024}
}


@article{susser_open_2022,
 abstract = {Energy models are used to explore decarbonisation pathways and potential future energy systems. In this editorial, we comment on the importance of energy system modelling and open tools to inform policymaking in the context of the European Green Deal. We also summarise the seven contributions to the special collection on Energy Systems Modelling, among which are papers that have been presented at the Energy Modelling Platform for Europe (EMP-E) 2021 conference. The presented research advances current modelling approaches and supports energy modelling with open tools and datasets.},
 author = {Süsser, Diana and Pickering, Bryn and Hülk, Ludwig and Pfenninger, Stefan},
 doi = {10.12688/f1000research.121619.1},
 issn = {2046-1402},
 journal = {F1000Res},
 month = {May},
 pages = {531},
 pmcid = {PMC9111364},
 pmid = {35615495},
 title = {Open energy system modelling to support the European Green Deal},
 url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/},
 urldate = {2024-07-10},
 volume = {11},
 year = {2022}
}


@article{Wohland_Wind_2021,
 abstract = {$<$p$><$strong class="journal-contentHeaderColor"$>$Abstract.$<$/strong$>$ Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20 year timescales by one order of magnitude. Albeit smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind related infrastructure like wind energy.$<$/p$>$},
 author = {Wohland, Jan and Folini, Doris and Pickering, Bryn},
 doi = {10.5194/esd-2021-29},
 issn = {2190-4979},
 journal = {Earth System Dynamics Discussions},
 language = {English},
 month = {April},
 pages = {1--27},
 publisher = {Copernicus GmbH},
 title = {Wind Speed Stilling and Its Recovery Due to Internal Climate Variability},
 year = {2021}
}



@inproceedings{Martin_How_2021,
 author = {Martin, Nicholas and Madrid-López, Cristina and Talens-Peiró, Laura and Pickering, Bryn},
 booktitle = {EGU21},
 doi = {10.5194/egusphere-egu21-16017},
 language = {en},
 month = {March},
 publisher = {Copernicus Meetings},
 shorttitle = {How Clean Is Your "Clean" Energy?},
 title = {How Clean Is Your "Clean" Energy? The ENVIRO Module for Energy System Models},
 year = {2021}
}



@inproceedings{Pickering_Decision_2021,
 abstract = {A decarbonised European energy system will require a number of potentially contested decisions on where best to locate renewable generation capacity. Typically, modellers determine the &amp;#8220;best&amp;#8221; system based on the least cost to society, focussing on a cost-minimising energy system model result to inform planning and policy. This approach neglects potentially more desirable alternative results which might, for example, avoid problematic concentrations of onshore wind power deployment, increase national supply security, or lower the risk of system failure in adverse weather conditions.&lt;/p&gt;&lt;p&gt;In response, we have developed a method to generate spatially explicit, practically optimal results (SPORES) in the context of energy system optimisation. SPORES can be used to explore energy systems which may offer more socially, politically, or environmentally acceptable alternatives. Furthermore, we have developed metrics to aid identification of interesting alternatives, like those which maximise the spatial distribution of wind generation capacity or minimise exposure to multi-year demand and weather uncertainty.&lt;/p&gt;&lt;p&gt;In this presentation, we will detail the application of the SPORES method in two cases of energy system decarbonisation: &amp;#160;the Italian power system and the European energy system. We will present technology deployment strategies which are prevalent across SPORES, such as solar photovoltaics coupled with battery storage, as well as those which offer flexibility of choice in location and extent of deployment. To help with the urgent task of planning socially and politically acceptable energy system decarbonisation strategies, our implementation of SPORES in the open-source energy systems modelling framework Calliope makes it accessible to a wide range of potential users; we will also discuss how other research groups can further build on this to accelerate the energy transition.&lt;/p&gt;},
 author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},
 booktitle = {EGU21},
 doi = {10.5194/egusphere-egu21-16205},
 month = {March},
 publisher = {Copernicus Meetings},
 title = {Decision Support for Renewables Deployment through Spatially Explicit Energy System Alternatives},
 year = {2021}
}

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June 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1186/s13705-024-00458-z\"\n     onclick=\"log_download('brchin-pickering-trndle-pfenninger-harderbetterfasterstrongerunderstandingandimprovingthetractabilityoflargeenergysystemmodels-2024', 'https://doi.org/10.1186/s13705-024-00458-z', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Harder, better, faster, stronger: understanding and improving the tractability of large energy system models [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1186/s13705-024-00458-z\"\n     onclick=\"log_download('brchin-pickering-trndle-pfenninger-harderbetterfasterstrongerunderstandingandimprovingthetractabilityoflargeenergysystemmodels-2024', 'http://doi.org/10.1186/s13705-024-00458-z', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/brchin-pickering-trndle-pfenninger-harderbetterfasterstrongerunderstandingandimprovingthetractabilityoflargeenergysystemmodels-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('brchin-pickering-trndle-pfenninger-harderbetterfasterstrongerunderstandingandimprovingthetractabilityoflargeenergysystemmodels-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{brochin_harder_2024,\n abstract = {Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue.},\n author = {Bröchin, Manuel and Pickering, Bryn and Tröndle, Tim and Pfenninger, Stefan},\n doi = {10.1186/s13705-024-00458-z},\n issn = {2192-0567},\n journal = {Energ Sustain Soc},\n keywords = {Benchmark, Energy system models, Interior-point, Linear programming, Numerical issues, Scaling, Simplex},\n language = {en},\n month = {June},\n number = {1},\n pages = {27},\n shorttitle = {Harder, better, faster, stronger},\n title = {Harder, better, faster, stronger: understanding and improving the tractability of large energy system models},\n url = {https://doi.org/10.1186/s13705-024-00458-z},\n urldate = {2024-07-10},\n volume = {14},\n year = {2024}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0360544224001968\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024', 'https://www.sciencedirect.com/science/article/pii/S0360544224001968', event);\">\n    Economy-wide impacts of socio-politically driven net-zero energy systems in europe.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mayer, J., Süsser, D., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Bachner, G.,  &amp; Sanvito, F. D.\n</span>\n\n  \n\n</span>\n\n  <i>Energy</i>, 291: 130425. March 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0360544224001968\"\n     onclick=\"log_download('mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024', 'https://www.sciencedirect.com/science/article/pii/S0360544224001968', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Economy-wide impacts of socio-politically driven net-zero energy systems in europe [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.energy.2024.130425\"\n     onclick=\"log_download('mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024', 'http://doi.org/10.1016/j.energy.2024.130425', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mayer-ssser-pickering-bachner-sanvito-economywideimpactsofsociopoliticallydrivennetzeroenergysystemsineurope-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{mayer_economy-wide_2024,\n abstract = {Net-zero energy system configurations can be met in numerous ways, implying diverse economic effects. However, what is usually ignored in techno-economic and economy-wide analysis are the distinct social-political drivers and barriers, which might constrain certain elements of future energy systems. We thus apply a model ensemble that defines social-political storylines which constrain feasible net-zero configurations of the European energy system. Using these configurations in a macroeconomic general equilibrium model allows us to explore economy-wide effects and ultimately the cost-effectiveness of different systems. We find that social-political storylines provide valuable boundary conditions for feasible net-zero designs of the energy system and that the costliest energy sector configuration in fact leads to the highest European-wide welfare levels. This result originates in indirect effects, particularly positive employment effects, covered by the macroeconomic model. However, adverse public budget effects on the transition to net-zero energy may limit the willingness of policymakers who focus on shorter time-horizons to foster such a development. Our results highlight the relevance of considering the interaction of energy system-changes with labor, emission allowance and capital markets, as well as considering long-term perspectives.},\n author = {Mayer, Jakob and Süsser, Diana and Pickering, Bryn and Bachner, Gabriel and Sanvito, Francesco Davide},\n doi = {10.1016/j.energy.2024.130425},\n issn = {0360-5442},\n journal = {Energy},\n keywords = {Climate change mitigation, Computable general equilibrium, Cost-effectiveness, Energy system design, Social-political storylines},\n month = {March},\n pages = {130425},\n title = {Economy-wide impacts of socio-politically driven net-zero energy systems in europe},\n url = {https://www.sciencedirect.com/science/article/pii/S0360544224001968},\n urldate = {2024-07-10},\n volume = {291},\n year = {2024}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Net-zero energy system configurations can be met in numerous ways, implying diverse economic effects. However, what is usually ignored in techno-economic and economy-wide analysis are the distinct social-political drivers and barriers, which might constrain certain elements of future energy systems. We thus apply a model ensemble that defines social-political storylines which constrain feasible net-zero configurations of the European energy system. Using these configurations in a macroeconomic general equilibrium model allows us to explore economy-wide effects and ultimately the cost-effectiveness of different systems. We find that social-political storylines provide valuable boundary conditions for feasible net-zero designs of the energy system and that the costliest energy sector configuration in fact leads to the highest European-wide welfare levels. This result originates in indirect effects, particularly positive employment effects, covered by the macroeconomic model. However, adverse public budget effects on the transition to net-zero energy may limit the willingness of policymakers who focus on shorter time-horizons to foster such a development. Our results highlight the relevance of considering the interaction of energy system-changes with labor, emission allowance and capital markets, as well as considering long-term perspectives.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://joss.theoj.org/papers/10.21105/joss.06097\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024', 'https://joss.theoj.org/papers/10.21105/joss.06097', event);\">\n    PAM: Population Activity Modeller.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shone, F., Chatziioannou, T., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Kozlowska, K.,  &amp; Fitzmaurice, M.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Open Source Software</i>, 9(96): 6097. April 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://joss.theoj.org/papers/10.21105/joss.06097\"\n     onclick=\"log_download('shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024', 'https://joss.theoj.org/papers/10.21105/joss.06097', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"PAM: Population Activity Modeller [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.21105/joss.06097\"\n     onclick=\"log_download('shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024', 'http://doi.org/10.21105/joss.06097', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shone-chatziioannou-pickering-kozlowska-fitzmaurice-pampopulationactivitymodeller-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{shone_pam_2024,\n abstract = {Shone et al., (2024). PAM: Population Activity Modeller. Journal of Open Source Software, 9(96), 6097, https://doi.org/10.21105/joss.06097},\n author = {Shone, Fred and Chatziioannou, Theodore and Pickering, Bryn and Kozlowska, Kasia and Fitzmaurice, Michael},\n doi = {10.21105/joss.06097},\n issn = {2475-9066},\n journal = {Journal of Open Source Software},\n language = {en},\n month = {April},\n number = {96},\n pages = {6097},\n shorttitle = {PAM},\n title = {PAM: Population Activity Modeller},\n url = {https://joss.theoj.org/papers/10.21105/joss.06097},\n urldate = {2024-07-10},\n volume = {9},\n year = {2024}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Shone et al., (2024). PAM: Population Activity Modeller. Journal of Open Source Software, 9(96), 6097, https://doi.org/10.21105/joss.06097\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0306261923003665\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023', 'https://www.sciencedirect.com/science/article/pii/S0306261923003665', event);\">\n    What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lombardi, F., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>,  &amp; Pfenninger, S.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Energy</i>, 339: 121002. June 2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0306261923003665\"\n     onclick=\"log_download('lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023', 'https://www.sciencedirect.com/science/article/pii/S0306261923003665', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.apenergy.2023.121002\"\n     onclick=\"log_download('lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023', 'http://doi.org/10.1016/j.apenergy.2023.121002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lombardi-pickering-pfenninger-whatisredundantandwhatisnotcomputationaltradeoffsinmodellingtogeneratealternativesforenergyinfrastructuredeployment-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{lombardi_what_2023,\n abstract = {Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study’s central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.},\n author = {Lombardi, Francesco and Pickering, Bryn and Pfenninger, Stefan},\n doi = {10.1016/j.apenergy.2023.121002},\n issn = {0306-2619},\n journal = {Applied Energy},\n keywords = {MGA, On-shore wind, Optimization, Spatial dissimilarity, SPORES},\n month = {June},\n pages = {121002},\n shorttitle = {What is redundant and what is not?},\n title = {What is redundant and what is not? Computational trade-offs in modelling to generate alternatives for energy infrastructure deployment},\n url = {https://www.sciencedirect.com/science/article/pii/S0306261923003665},\n urldate = {2024-07-10},\n volume = {339},\n year = {2023}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study’s central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.cell.com/joule/abstract/S2542-4351(22)00237-9\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022', 'https://www.cell.com/joule/abstract/S2542-4351(22)00237-9', event);\">\n    Overcoming the disconnect between energy system and climate modeling.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Craig, M. T., Wohland, J., Stoop, L. P., Kies, A., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Bloomfield, H. C., Browell, J., Felice, M. D., Dent, C. J., Deroubaix, A., Frischmuth, F., Gonzalez, P. L. M., Grochowicz, A., Gruber, K., Härtel, P., Kittel, M., Kotzur, L., Labuhn, I., Lundquist, J. K., Pflugradt, N., Wiel, K. v. d., Zeyringer, M.,  &amp; Brayshaw, D. J.\n</span>\n\n  \n\n</span>\n\n  <i>Joule</i>, 6(7): 1405–1417. July 2022.\n  <span class=\"note\">Publisher: Elsevier</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.cell.com/joule/abstract/S2542-4351(22)00237-9\"\n     onclick=\"log_download('craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022', 'https://www.cell.com/joule/abstract/S2542-4351(22)00237-9', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Overcoming the disconnect between energy system and climate modeling [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.joule.2022.05.010\"\n     onclick=\"log_download('craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022', 'http://doi.org/10.1016/j.joule.2022.05.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('craig-wohland-stoop-kies-pickering-bloomfield-browell-felice-etal-overcomingthedisconnectbetweenenergysystemandclimatemodeling-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{craig_overcoming_2022,\n author = {Craig, Michael T. and Wohland, Jan and Stoop, Laurens P. and Kies, Alexander and Pickering, Bryn and Bloomfield, Hannah C. and Browell, Jethro and Felice, Matteo De and Dent, Chris J. and Deroubaix, Adrien and Frischmuth, Felix and Gonzalez, Paula L. M. and Grochowicz, Aleksander and Gruber, Katharina and Härtel, Philipp and Kittel, Martin and Kotzur, Leander and Labuhn, Inga and Lundquist, Julie K. and Pflugradt, Noah and Wiel, Karin van der and Zeyringer, Marianne and Brayshaw, David J.},\n doi = {10.1016/j.joule.2022.05.010},\n issn = {2542-4785, 2542-4351},\n journal = {Joule},\n language = {English},\n month = {July},\n note = {Publisher: Elsevier},\n number = {7},\n pages = {1405--1417},\n title = {Overcoming the disconnect between energy system and climate modeling},\n url = {https://www.cell.com/joule/abstract/S2542-4351(22)00237-9},\n urldate = {2024-07-10},\n volume = {6},\n year = {2022}\n}\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.cell.com/joule/abstract/S2542-4351(22)00236-7\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022', 'https://www.cell.com/joule/abstract/S2542-4351(22)00236-7', event);\">\n    Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Lombardi, F.,  &amp; Pfenninger, S.\n</span>\n\n  \n\n</span>\n\n  <i>Joule</i>, 6(6): 1253–1276. June 2022.\n  <span class=\"note\">Publisher: Elsevier</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.cell.com/joule/abstract/S2542-4351(22)00236-7\"\n     onclick=\"log_download('pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022', 'https://www.cell.com/joule/abstract/S2542-4351(22)00236-7', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.joule.2022.05.009\"\n     onclick=\"log_download('pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022', 'http://doi.org/10.1016/j.joule.2022.05.009', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-lombardi-pfenninger-diversityofoptionstoeliminatefossilfuelsandreachcarbonneutralityacrosstheentireeuropeanenergysystem-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{pickering_diversity_2022,\n abstract = {Disagreements persist on how to design a self-sufficient, carbon-neutral European energy system. To explore the diversity of design options, we develop a high-resolution model of the entire European energy system and produce 441 technically feasible system designs that are within 10% of the optimal economic cost. We show that a wide range of systems based on renewable energy are feasible, with no need to import energy from outside Europe. Model solutions reveal considerable flexibility in the choice and geographical distribution of new infrastructure across the continent. Balanced renewable energy supply can be achieved either with or without mechanisms such as biofuel use, curtailment, and expansion of the electricity network. Trade-offs emerge once specific preferences are imposed. Low biofuel use, for example, requires heat electrification and controlled vehicle charging. This exploration of the impact of preferences on system design options is vital to inform urgent, politically difficult decisions for eliminating fossil fuel imports and achieving European carbon neutrality.},\n author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},\n doi = {10.1016/j.joule.2022.05.009},\n issn = {2542-4785, 2542-4351},\n journal = {Joule},\n language = {English},\n month = {June},\n note = {Publisher: Elsevier},\n number = {6},\n pages = {1253--1276},\n title = {Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system},\n url = {https://www.cell.com/joule/abstract/S2542-4351(22)00236-7},\n urldate = {2024-07-10},\n volume = {6},\n year = {2022}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Disagreements persist on how to design a self-sufficient, carbon-neutral European energy system. To explore the diversity of design options, we develop a high-resolution model of the entire European energy system and produce 441 technically feasible system designs that are within 10% of the optimal economic cost. We show that a wide range of systems based on renewable energy are feasible, with no need to import energy from outside Europe. Model solutions reveal considerable flexibility in the choice and geographical distribution of new infrastructure across the continent. Balanced renewable energy supply can be achieved either with or without mechanisms such as biofuel use, curtailment, and expansion of the electricity network. Trade-offs emerge once specific preferences are imposed. Low biofuel use, for example, requires heat electrification and controlled vehicle charging. This exploration of the impact of preferences on system design options is vital to inform urgent, politically difficult decisions for eliminating fossil fuel imports and achieving European carbon neutrality.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022', 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/', event);\">\n    Open energy system modelling to support the European Green Deal.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Süsser, D., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Hülk, L.,  &amp; Pfenninger, S.\n</span>\n\n  \n\n</span>\n\n  <i>F1000Res</i>, 11: 531. May 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/\"\n     onclick=\"log_download('ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022', 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Open energy system modelling to support the European Green Deal [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.12688/f1000research.121619.1\"\n     onclick=\"log_download('ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022', 'http://doi.org/10.12688/f1000research.121619.1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ssser-pickering-hlk-pfenninger-openenergysystemmodellingtosupporttheeuropeangreendeal-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{susser_open_2022,\n abstract = {Energy models are used to explore decarbonisation pathways and potential future energy systems. In this editorial, we comment on the importance of energy system modelling and open tools to inform policymaking in the context of the European Green Deal. We also summarise the seven contributions to the special collection on Energy Systems Modelling, among which are papers that have been presented at the Energy Modelling Platform for Europe (EMP-E) 2021 conference. The presented research advances current modelling approaches and supports energy modelling with open tools and datasets.},\n author = {Süsser, Diana and Pickering, Bryn and Hülk, Ludwig and Pfenninger, Stefan},\n doi = {10.12688/f1000research.121619.1},\n issn = {2046-1402},\n journal = {F1000Res},\n month = {May},\n pages = {531},\n pmcid = {PMC9111364},\n pmid = {35615495},\n title = {Open energy system modelling to support the European Green Deal},\n url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111364/},\n urldate = {2024-07-10},\n volume = {11},\n year = {2022}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Energy models are used to explore decarbonisation pathways and potential future energy systems. In this editorial, we comment on the importance of energy system modelling and open tools to inform policymaking in the context of the European Green Deal. We also summarise the seven contributions to the special collection on Energy Systems Modelling, among which are papers that have been presented at the Energy Modelling Platform for Europe (EMP-E) 2021 conference. The presented research advances current modelling approaches and supports energy modelling with open tools and datasets.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (7)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"chang-thellufsen-zakeri-pickering-pfenninger-lund-stergaard-trendsintoolsandapproachesformodellingtheenergytransition-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Trends in Tools and Approaches for Modelling the Energy Transition.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chang, M., Thellufsen, J. Z., Zakeri, B., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Pfenninger, S., Lund, H.,  &amp; Østergaard, P. A.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Energy</i>, 290: 116731. May 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.apenergy.2021.116731\"\n     onclick=\"log_download('chang-thellufsen-zakeri-pickering-pfenninger-lund-stergaard-trendsintoolsandapproachesformodellingtheenergytransition-2021', 'http://doi.org/10.1016/j.apenergy.2021.116731', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chang-thellufsen-zakeri-pickering-pfenninger-lund-stergaard-trendsintoolsandapproachesformodellingtheenergytransition-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chang-thellufsen-zakeri-pickering-pfenninger-lund-stergaard-trendsintoolsandapproachesformodellingtheenergytransition-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Chang_Trends_2021,\n abstract = {Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.},\n author = {Chang, Miguel and Thellufsen, Jakob Zink and Zakeri, Behnam and Pickering, Bryn and Pfenninger, Stefan and Lund, Henrik and Østergaard, Poul Alberg},\n doi = {10.1016/j.apenergy.2021.116731},\n issn = {0306-2619},\n journal = {Applied Energy},\n keywords = {Energy models,Energy system analysis,Energy system modelling tool,Review,Survey},\n language = {en},\n month = {May},\n pages = {116731},\n title = {Trends in Tools and Approaches for Modelling the Energy Transition},\n volume = {290},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mckenna-pfenninger-heinrichs-schmidt-staffell-gruber-hahmann-jansen-etal-reviewingmethodsandassumptionsforhighresolutionlargescaleonshorewindenergypotentialassessments-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Reviewing Methods and Assumptions for High-Resolution Large-Scale Onshore Wind Energy Potential Assessments.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  McKenna, R., Pfenninger, S., Heinrichs, H., Schmidt, J., Staffell, I., Gruber, K., Hahmann, A. N., Jansen, M., Klingler, M., Landwehr, N., Larsén, X. G., Lilliestam, J., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Robinius, M., Tröndle, T., Turkovska, O., Wehrle, S., Weinand, J. M.,  &amp; Wohland, J.\n</span>\n\n  \n\n</span>\n\n  <i>arXiv:2103.09781 [econ, q-fin]</i>. March 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mckenna-pfenninger-heinrichs-schmidt-staffell-gruber-hahmann-jansen-etal-reviewingmethodsandassumptionsforhighresolutionlargescaleonshorewindenergypotentialassessments-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mckenna-pfenninger-heinrichs-schmidt-staffell-gruber-hahmann-jansen-etal-reviewingmethodsandassumptionsforhighresolutionlargescaleonshorewindenergypotentialassessments-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{McKenna_Reviewing_2021,\n abstract = {The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.},\n archiveprefix = {arXiv},\n author = {McKenna, Russell and Pfenninger, Stefan and Heinrichs, Heidi and Schmidt, Johannes and Staffell, Iain and Gruber, Katharina and Hahmann, Andrea N. and Jansen, Malte and Klingler, Michael and Landwehr, Natascha and Larsén, Xiaoli Guo and Lilliestam, Johan and Pickering, Bryn and Robinius, Martin and Tröndle, Tim and Turkovska, Olga and Wehrle, Sebastian and Weinand, Jann Michael and Wohland, Jan},\n eprint = {2103.09781},\n eprinttype = {arxiv},\n journal = {arXiv:2103.09781 [econ, q-fin]},\n keywords = {Economics - General Economics},\n month = {March},\n primaryclass = {econ, q-fin},\n title = {Reviewing Methods and Assumptions for High-Resolution Large-Scale Onshore Wind Energy Potential Assessments},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-chang-thellufsen-roelfsema-mikropoulos-vanvuuren-modellinginsupporttothetransitiontoalowcarbonenergysystemineuropemodeldevelopmenttomatchsystemdesignmodelstouserneeds-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Modelling in Support to the Transition to a Low-Carbon Energy System in Europe: Model Development to Match System Design Models to User Needs.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Chang, M., Thellufsen, J. Z., Roelfsema, M., Mikropoulos, S.,  &amp; van Vuuren, D.\n</span>\n\n  \n\n</span>\n\n  Technical Report Deliverable 4.2 of the SENTINEL project funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 837089, February 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-chang-thellufsen-roelfsema-mikropoulos-vanvuuren-modellinginsupporttothetransitiontoalowcarbonenergysystemineuropemodeldevelopmenttomatchsystemdesignmodelstouserneeds-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-chang-thellufsen-roelfsema-mikropoulos-vanvuuren-modellinginsupporttothetransitiontoalowcarbonenergysystemineuropemodeldevelopmenttomatchsystemdesignmodelstouserneeds-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@techreport{Pickering_Modelling_2021,\n author = {Pickering, Bryn and Chang, Miguel and Thellufsen, Jakob Zinck and Roelfsema, Mark and Mikropoulos, Stratos and van Vuuren, Detlef},\n month = {February},\n number = {Deliverable 4.2 of the SENTINEL project funded under the European Union&#x27;s Horizon 2020 research and innovation programme under grant agreement No 837089},\n title = {Modelling in Support to the Transition to a Low-Carbon Energy System in Europe:  Model Development to Match System Design Models to User Needs},\n type = {Deliverable},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-choudhary-quantifyingresilienceinenergysystemswithoutofsampletesting-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Quantifying Resilience in Energy Systems with Out-of-Sample Testing.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>,  &amp; Choudhary, R.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Energy</i>, 285: 116465. March 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.apenergy.2021.116465\"\n     onclick=\"log_download('pickering-choudhary-quantifyingresilienceinenergysystemswithoutofsampletesting-2021', 'http://doi.org/10.1016/j.apenergy.2021.116465', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-choudhary-quantifyingresilienceinenergysystemswithoutofsampletesting-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-choudhary-quantifyingresilienceinenergysystemswithoutofsampletesting-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Pickering_Quantifying_2021,\n abstract = {The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO2 emissions, and aversion to risk. We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO2 emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.},\n author = {Pickering, Bryn and Choudhary, Ruchi},\n doi = {10.1016/j.apenergy.2021.116465},\n issn = {0306-2619},\n journal = {Applied Energy},\n keywords = {District energy systems,Mixed integer linear optimisation,Out-of-sample testing,Resilient systems,Scenario optimisation,Two-stage stochastic programming},\n language = {en},\n month = {March},\n pages = {116465},\n title = {Quantifying Resilience in Energy Systems with Out-of-Sample Testing},\n volume = {285},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO2 emissions, and aversion to risk. We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO2 emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wohland-folini-pickering-windspeedstillinganditsrecoveryduetointernalclimatevariability-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Wind Speed Stilling and Its Recovery Due to Internal Climate Variability.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wohland, J., Folini, D.,  &amp; <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Earth System Dynamics Discussions</i>,1–27. April 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5194/esd-2021-29\"\n     onclick=\"log_download('wohland-folini-pickering-windspeedstillinganditsrecoveryduetointernalclimatevariability-2021', 'http://doi.org/10.5194/esd-2021-29', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wohland-folini-pickering-windspeedstillinganditsrecoveryduetointernalclimatevariability-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wohland-folini-pickering-windspeedstillinganditsrecoveryduetointernalclimatevariability-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Wohland_Wind_2021,\n abstract = {$&lt;$p$&gt;&lt;$strong class=&quot;journal-contentHeaderColor&quot;$&gt;$Abstract.$&lt;$/strong$&gt;$ Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20 year timescales by one order of magnitude. Albeit smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind related infrastructure like wind energy.$&lt;$/p$&gt;$},\n author = {Wohland, Jan and Folini, Doris and Pickering, Bryn},\n doi = {10.5194/esd-2021-29},\n issn = {2190-4979},\n journal = {Earth System Dynamics Discussions},\n language = {English},\n month = {April},\n pages = {1--27},\n publisher = {Copernicus GmbH},\n title = {Wind Speed Stilling and Its Recovery Due to Internal Climate Variability},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  $<$p$><$strong class=\"journal-contentHeaderColor\"$>$Abstract.$<$/strong$>$ Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20 year timescales by one order of magnitude. Albeit smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind related infrastructure like wind energy.$<$/p$>$\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"martin-madridlpez-talenspeir-pickering-howcleanisyourcleanenergytheenviromoduleforenergysystemmodels-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  How Clean Is Your \"Clean\" Energy? The ENVIRO Module for Energy System Models.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Martin, N., Madrid-López, C., Talens-Peiró, L.,  &amp; <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>EGU21</i>, March 2021. Copernicus Meetings\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5194/egusphere-egu21-16017\"\n     onclick=\"log_download('martin-madridlpez-talenspeir-pickering-howcleanisyourcleanenergytheenviromoduleforenergysystemmodels-2021', 'http://doi.org/10.5194/egusphere-egu21-16017', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/martin-madridlpez-talenspeir-pickering-howcleanisyourcleanenergytheenviromoduleforenergysystemmodels-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('martin-madridlpez-talenspeir-pickering-howcleanisyourcleanenergytheenviromoduleforenergysystemmodels-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Martin_How_2021,\n author = {Martin, Nicholas and Madrid-López, Cristina and Talens-Peiró, Laura and Pickering, Bryn},\n booktitle = {EGU21},\n doi = {10.5194/egusphere-egu21-16017},\n language = {en},\n month = {March},\n publisher = {Copernicus Meetings},\n shorttitle = {How Clean Is Your &quot;Clean&quot; Energy?},\n title = {How Clean Is Your &quot;Clean&quot; Energy? The ENVIRO Module for Energy System Models},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-lombardi-pfenninger-decisionsupportforrenewablesdeploymentthroughspatiallyexplicitenergysystemalternatives-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Decision Support for Renewables Deployment through Spatially Explicit Energy System Alternatives.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Lombardi, F.,  &amp; Pfenninger, S.\n</span>\n\n  \n\n</span>\n\n  In <i>EGU21</i>, March 2021. Copernicus Meetings\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5194/egusphere-egu21-16205\"\n     onclick=\"log_download('pickering-lombardi-pfenninger-decisionsupportforrenewablesdeploymentthroughspatiallyexplicitenergysystemalternatives-2021', 'http://doi.org/10.5194/egusphere-egu21-16205', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-lombardi-pfenninger-decisionsupportforrenewablesdeploymentthroughspatiallyexplicitenergysystemalternatives-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-lombardi-pfenninger-decisionsupportforrenewablesdeploymentthroughspatiallyexplicitenergysystemalternatives-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Pickering_Decision_2021,\n abstract = {A decarbonised European energy system will require a number of potentially contested decisions on where best to locate renewable generation capacity. Typically, modellers determine the &amp;amp;#8220;best&amp;amp;#8221; system based on the least cost to society, focussing on a cost-minimising energy system model result to inform planning and policy. This approach neglects potentially more desirable alternative results which might, for example, avoid problematic concentrations of onshore wind power deployment, increase national supply security, or lower the risk of system failure in adverse weather conditions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In response, we have developed a method to generate spatially explicit, practically optimal results (SPORES) in the context of energy system optimisation. SPORES can be used to explore energy systems which may offer more socially, politically, or environmentally acceptable alternatives. Furthermore, we have developed metrics to aid identification of interesting alternatives, like those which maximise the spatial distribution of wind generation capacity or minimise exposure to multi-year demand and weather uncertainty.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this presentation, we will detail the application of the SPORES method in two cases of energy system decarbonisation: &amp;amp;#160;the Italian power system and the European energy system. We will present technology deployment strategies which are prevalent across SPORES, such as solar photovoltaics coupled with battery storage, as well as those which offer flexibility of choice in location and extent of deployment. To help with the urgent task of planning socially and politically acceptable energy system decarbonisation strategies, our implementation of SPORES in the open-source energy systems modelling framework Calliope makes it accessible to a wide range of potential users; we will also discuss how other research groups can further build on this to accelerate the energy transition.&amp;lt;/p&amp;gt;},\n author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},\n booktitle = {EGU21},\n doi = {10.5194/egusphere-egu21-16205},\n month = {March},\n publisher = {Copernicus Meetings},\n title = {Decision Support for Renewables Deployment through Spatially Explicit Energy System Alternatives},\n year = {2021}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  A decarbonised European energy system will require a number of potentially contested decisions on where best to locate renewable generation capacity. Typically, modellers determine the &amp;#8220;best&amp;#8221; system based on the least cost to society, focussing on a cost-minimising energy system model result to inform planning and policy. This approach neglects potentially more desirable alternative results which might, for example, avoid problematic concentrations of onshore wind power deployment, increase national supply security, or lower the risk of system failure in adverse weather conditions.&lt;/p&gt;&lt;p&gt;In response, we have developed a method to generate spatially explicit, practically optimal results (SPORES) in the context of energy system optimisation. SPORES can be used to explore energy systems which may offer more socially, politically, or environmentally acceptable alternatives. Furthermore, we have developed metrics to aid identification of interesting alternatives, like those which maximise the spatial distribution of wind generation capacity or minimise exposure to multi-year demand and weather uncertainty.&lt;/p&gt;&lt;p&gt;In this presentation, we will detail the application of the SPORES method in two cases of energy system decarbonisation: &amp;#160;the Italian power system and the European energy system. We will present technology deployment strategies which are prevalent across SPORES, such as solar photovoltaics coupled with battery storage, as well as those which offer flexibility of choice in location and extent of deployment. To help with the urgent task of planning socially and politically acceptable energy system decarbonisation strategies, our implementation of SPORES in the open-source energy systems modelling framework Calliope makes it accessible to a wide range of potential users; we will also discuss how other research groups can further build on this to accelerate the energy transition.&lt;/p&gt;\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"lombardi-pickering-colombo-pfenninger-policydecisionsupportforrenewablesdeploymentthroughspatiallyexplicitpracticallyoptimalalternatives-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lombardi, F., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Colombo, E.,  &amp; Pfenninger, S.\n</span>\n\n  \n\n</span>\n\n  <i>Joule</i>, 0(0). August 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.joule.2020.08.002\"\n     onclick=\"log_download('lombardi-pickering-colombo-pfenninger-policydecisionsupportforrenewablesdeploymentthroughspatiallyexplicitpracticallyoptimalalternatives-2020', 'http://doi.org/10.1016/j.joule.2020.08.002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lombardi-pickering-colombo-pfenninger-policydecisionsupportforrenewablesdeploymentthroughspatiallyexplicitpracticallyoptimalalternatives-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lombardi-pickering-colombo-pfenninger-policydecisionsupportforrenewablesdeploymentthroughspatiallyexplicitpracticallyoptimalalternatives-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Lombardi_Policy_2020,\n author = {Lombardi, Francesco and Pickering, Bryn and Colombo, Emanuela and Pfenninger, Stefan},\n doi = {10.1016/j.joule.2020.08.002},\n issn = {2542-4785, 2542-4351},\n journal = {Joule},\n keywords = {decarbonization,energy modeling,Italy,near-optimal solutions,policy alternatives,renewables,spatially explicit,SPORES,uncertainty,wind deployment},\n language = {English},\n month = {August},\n number = {0},\n publisher = {Elsevier},\n title = {Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives},\n volume = {0},\n year = {2020}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-grams-pfenninger-subnationalvariabilityofwindpowergenerationincomplexterrainanditscorrelationwithlargescalemeteorology-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Sub-National Variability of Wind Power Generation in Complex Terrain and Its Correlation with Large-Scale Meteorology.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Grams, C. M.,  &amp; Pfenninger, S.\n</span>\n\n  \n\n</span>\n\n  <i>Environmental Research Letters</i>. January 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1088/1748-9326/ab70bd\"\n     onclick=\"log_download('pickering-grams-pfenninger-subnationalvariabilityofwindpowergenerationincomplexterrainanditscorrelationwithlargescalemeteorology-2020', 'http://doi.org/10.1088/1748-9326/ab70bd', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-grams-pfenninger-subnationalvariabilityofwindpowergenerationincomplexterrainanditscorrelationwithlargescalemeteorology-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-grams-pfenninger-subnationalvariabilityofwindpowergenerationincomplexterrainanditscorrelationwithlargescalemeteorology-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Pickering_Subnational_2020,\n abstract = {The future European electricity system will depend heavily on variable renewable generation, including wind power. To plan and operate reliable electricity supply systems, an understanding of wind power variability over a range of spatio-temporal scales is critical. In complex terrain, such as that found in mountainous Switzerland, wind speeds are influenced by a multitude of meteorological phenomena, many of which occur on scales too fine to capture with commonly used meteorological reanalysis datasets. Past work has shown that anticorrelation at a continental scale is an important way to help balance variable generation. Here, we investigate systematically for the first time the possibility of balancing wind variability by exploiting anticorrelation between weather patterns in complex terrain. We assess the capability for the Consortium for Small-scale Modeling (COSMO)-REA2 and COSMO-REA6 reanalyses (with a 2 and 6 km horizontal resolution, respectively) to reproduce historical measured data from weather stations, hub height anemometers, and wind turbine electricity generation across Switzerland. Both reanalyses are insufficient to reproduce site-specific wind speeds in Switzerland’s complex terrain. We find however that mountain-valley breezes, orographic channelling, and variability imposed by European-scale weather regimes are represented by COSMO-REA2. We discover multi-day periods of wind electricity generation in regions of Switzerland which are anticorrelated with neighbouring European countries. Our results suggest that significantly more work is needed to understand the impact of fine scale wind power variability on national and continental electricity systems, and that higher-resolution reanalyses are necessary to accurately understand the local variability of renewable generation in complex terrain.},\n author = {Pickering, Bryn and Grams, Christian M. and Pfenninger, Stefan},\n doi = {10.1088/1748-9326/ab70bd},\n issn = {1748-9326},\n journal = {Environmental Research Letters},\n language = {en},\n month = {January},\n publisher = {IOP Publishing},\n title = {Sub-National Variability of Wind Power Generation in Complex Terrain and Its Correlation with Large-Scale Meteorology},\n year = {2020}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The future European electricity system will depend heavily on variable renewable generation, including wind power. To plan and operate reliable electricity supply systems, an understanding of wind power variability over a range of spatio-temporal scales is critical. In complex terrain, such as that found in mountainous Switzerland, wind speeds are influenced by a multitude of meteorological phenomena, many of which occur on scales too fine to capture with commonly used meteorological reanalysis datasets. Past work has shown that anticorrelation at a continental scale is an important way to help balance variable generation. Here, we investigate systematically for the first time the possibility of balancing wind variability by exploiting anticorrelation between weather patterns in complex terrain. We assess the capability for the Consortium for Small-scale Modeling (COSMO)-REA2 and COSMO-REA6 reanalyses (with a 2 and 6 km horizontal resolution, respectively) to reproduce historical measured data from weather stations, hub height anemometers, and wind turbine electricity generation across Switzerland. Both reanalyses are insufficient to reproduce site-specific wind speeds in Switzerland’s complex terrain. We find however that mountain-valley breezes, orographic channelling, and variability imposed by European-scale weather regimes are represented by COSMO-REA2. We discover multi-day periods of wind electricity generation in regions of Switzerland which are anticorrelated with neighbouring European countries. Our results suggest that significantly more work is needed to understand the impact of fine scale wind power variability on national and continental electricity systems, and that higher-resolution reanalyses are necessary to accurately understand the local variability of renewable generation in complex terrain.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pickering-choudhary-districtenergysystemoptimisationunderuncertaindemandhandlingdatadrivenstochasticprofiles-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  District Energy System Optimisation under Uncertain Demand: Handling Data-Driven Stochastic Profiles.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>,  &amp; Choudhary, R.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Energy</i>, 236: 1138–1157. February 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.apenergy.2018.12.037\"\n     onclick=\"log_download('pickering-choudhary-districtenergysystemoptimisationunderuncertaindemandhandlingdatadrivenstochasticprofiles-2019', 'http://doi.org/10.1016/j.apenergy.2018.12.037', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-choudhary-districtenergysystemoptimisationunderuncertaindemandhandlingdatadrivenstochasticprofiles-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-choudhary-districtenergysystemoptimisationunderuncertaindemandhandlingdatadrivenstochasticprofiles-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Pickering_District_2019,\n abstract = {Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.},\n author = {Pickering, B. and Choudhary, R.},\n doi = {10.1016/j.apenergy.2018.12.037},\n issn = {0306-2619},\n journal = {Applied Energy},\n keywords = {Data-driven demand,District energy systems,Mixed integer linear optimisation,Scenario optimisation,Scenario reduction},\n month = {February},\n pages = {1138--1157},\n shorttitle = {District Energy System Optimisation under Uncertain Demand},\n title = {District Energy System Optimisation under Uncertain Demand: Handling Data-Driven Stochastic Profiles},\n volume = {236},\n year = {2019}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India. Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-practicaloptimisationofdistrictenergysystemsrepresentationoftechnologycharacteristicsdemanduncertaintyandsystemrobustness-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B. C.</a>\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Cambridge, May 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-practicaloptimisationofdistrictenergysystemsrepresentationoftechnologycharacteristicsdemanduncertaintyandsystemrobustness-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-practicaloptimisationofdistrictenergysystemsrepresentationoftechnologycharacteristicsdemanduncertaintyandsystemrobustness-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{Pickering_Practical_2019,\n author = {Pickering, Brynmor Caradog},\n month = {May},\n school = {University of Cambridge},\n shorttitle = {Practical Optimisation of District Energy Systems},\n title = {Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness},\n type = {PhD Thesis},\n year = {2019}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pfenninger-pickering-calliopeamultiscaleenergysystemsmodellingframework-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Calliope: A Multi-Scale Energy Systems Modelling Framework.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pfenninger, S.,  &amp; <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>\n</span>\n\n  \n\n</span>\n\n  <i>The Journal of Open Source Software</i>, 3(29): 825. September 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.21105/joss.00825\"\n     onclick=\"log_download('pfenninger-pickering-calliopeamultiscaleenergysystemsmodellingframework-2018', 'http://doi.org/10.21105/joss.00825', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pfenninger-pickering-calliopeamultiscaleenergysystemsmodellingframework-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pfenninger-pickering-calliopeamultiscaleenergysystemsmodellingframework-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Pfenninger_Calliope_2018,\n author = {Pfenninger, Stefan and Pickering, Bryn},\n doi = {10.21105/joss.00825},\n journal = {The Journal of Open Source Software},\n language = {en},\n month = {September},\n number = {29},\n pages = {825},\n shorttitle = {Calliope},\n title = {Calliope: A Multi-Scale Energy Systems Modelling Framework},\n volume = {3},\n year = {2018}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-choudhary-mitigatingriskindistrictlevelenergyinvestmentdecisionsbyscenariooptimisation-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>,  &amp; Choudhary, R.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of BSO 2018</i>, pages 38–45, Cambridge, UK, September 2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-choudhary-mitigatingriskindistrictlevelenergyinvestmentdecisionsbyscenariooptimisation-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-choudhary-mitigatingriskindistrictlevelenergyinvestmentdecisionsbyscenariooptimisation-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Pickering_Mitigating_2018,\n abstract = {Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.},\n address = {Cambridge, UK},\n author = {Pickering, Bryn and Choudhary, Ruchi},\n booktitle = {Proceedings of BSO 2018},\n language = {en},\n month = {September},\n pages = {38--45},\n title = {Mitigating Risk in District-Level Energy Investment Decisions by Scenario Optimisation},\n year = {2018}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Increased availability of high resolution metered consumption data shows clear spatio-temporal variability in energy demand, both in terms of magnitude and time. This variability is rarely captured in district energy modelling and optimisation. In this paper, we demonstrate a modelling approach that integrates the stochastic variability of energy demand in energy system optimisation. In our set-up, energy demand is a stochastic function over time, separated into weekdays and weekends in a year. We consider cooling and electricity as end-uses. We implement the district energy optimisation using the mixed integer linear programming (MILP) Scenario optimisation (SO) framework. The stochastic variability of hourly demand is represented by 500 scenarios for 24 typical days in the year. For computational efficiency, we implement a scenario reduction step, resulting in 16 reduced scenarios as representative of the full scenario set. These 16 scenarios are used to formulate an SO model for a group of office buildings in Bangalore, India. The objective in this model is to minimise the Conditional Value at Risk (CVaR) associated with each scenario, weighted by the probability of that scenario being realised. A scenario can have some demand unmet, but this will incur a financial penalty. To better understand the necessary parametrisation of the model, the penalty for unmet demand is tested by sensitivity analysis.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"forman-mutschler-guthrie-soulti-pickering-bystrm-lee-improvingbuildingenergyperformanceinuniversitiesthecasestudyoftheuniversityofcambridge-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Improving Building Energy Performance in Universities: The Case Study of the University of Cambridge.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Forman, T., Mutschler, R., Guthrie, P., Soulti, E., <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Byström, V.,  &amp; Lee, S. M.\n</span>\n\n  \n\n</span>\n\n  In <i>Handbook of Theory and Practice of Sustainable Development in Higher Education</i>, pages 245–266. Springer, January 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/forman-mutschler-guthrie-soulti-pickering-bystrm-lee-improvingbuildingenergyperformanceinuniversitiesthecasestudyoftheuniversityofcambridge-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('forman-mutschler-guthrie-soulti-pickering-bystrm-lee-improvingbuildingenergyperformanceinuniversitiesthecasestudyoftheuniversityofcambridge-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{Forman_Improving_2017,\n author = {Forman, Tim and Mutschler, Roberta and Guthrie, Peter and Soulti, Eleni and Pickering, Bryn and Byström, Viktor and Lee, Si Min},\n booktitle = {Handbook of Theory and Practice of Sustainable Development in Higher Education},\n month = {January},\n pages = {245--266},\n publisher = {Springer},\n shorttitle = {Improving Building Energy Performance in Universities},\n title = {Improving Building Energy Performance in Universities: The Case Study of the University of Cambridge},\n year = {2017}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pickering-choudhary-applyingpiecewiselinearcharacteristiccurvesindistrictenergyoptimisation-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Applying Piecewise Linear Characteristic Curves in District Energy Optimisation.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>,  &amp; Choudhary, R.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems</i>, pages 1080–1092, San Diego, USA, July 2017. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-choudhary-applyingpiecewiselinearcharacteristiccurvesindistrictenergyoptimisation-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-choudhary-applyingpiecewiselinearcharacteristiccurvesindistrictenergyoptimisation-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Pickering_Applying_2017,\n address = {San Diego, USA},\n author = {Pickering, Bryn and Choudhary, Ruchi},\n booktitle = {Proceedings of the 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems},\n month = {July},\n pages = {1080--1092},\n title = {Applying Piecewise Linear Characteristic Curves in District Energy Optimisation},\n year = {2017}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pickering-ikeda-choudhary-ooka-comparisonofmetaheuristicandlinearprogrammingmodelsforthepurposeofoptimisingbuildingenergysupplyoperationschedule-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>, Ikeda, S., Choudhary, R.,  &amp; Ooka, R.\n</span>\n\n  \n\n</span>\n\n  In <i>12th REHVA World Congress</i>, volume 6, Aalborg, Denmark, May 2016. Aalborg University, Department of Civil Engineering\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-ikeda-choudhary-ooka-comparisonofmetaheuristicandlinearprogrammingmodelsforthepurposeofoptimisingbuildingenergysupplyoperationschedule-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-ikeda-choudhary-ooka-comparisonofmetaheuristicandlinearprogrammingmodelsforthepurposeofoptimisingbuildingenergysupplyoperationschedule-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Pickering_Comparison_2016,\n address = {Aalborg, Denmark},\n author = {Pickering, Bryn and Ikeda, Shintaro and Choudhary, Ruchi and Ooka, Ryozo},\n booktitle = {12th REHVA World Congress},\n isbn = {87-91606-31-4},\n month = {May},\n publisher = {Aalborg University, Department of Civil Engineering},\n title = {Comparison of Metaheuristic and Linear Programming Models for the Purpose of Optimising Building Energy Supply Operation Schedule},\n volume = {6},\n year = {2016}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pickering-electricityinfrastructureinrapidlydevelopingcities-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Electricity Infrastructure in Rapidly Developing Cities.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Cambridge, Cambridge, UK, August 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-electricityinfrastructureinrapidlydevelopingcities-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-electricityinfrastructureinrapidlydevelopingcities-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{Pickering_Electricity_2015,\n address = {Cambridge, UK},\n author = {Pickering, Bryn},\n month = {August},\n school = {University of Cambridge},\n title = {Electricity Infrastructure in Rapidly Developing Cities},\n type = {Master&#x27;s Thesis},\n year = {2015}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pickering-renewableenergyontheuniversityestate-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Renewable Energy on the University Estate.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://eeci.github.io/home/docs/people/bryn/\">Pickering, B.</a>\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Cambridge, Cambridge, UK, May 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pickering-renewableenergyontheuniversityestate-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pickering-renewableenergyontheuniversityestate-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{Pickering_Renewable_2014,\n address = {Cambridge, UK},\n author = {Pickering, Bryn},\n month = {May},\n school = {University of Cambridge},\n title = {Renewable Energy on the University Estate},\n type = {Master&#x27;s Thesis},\n year = {2014}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);