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\n\n \n \n \n \n \n Techno-economic Feasibility of A Trust and Grid-aware Coordination Scheme.\n \n \n \n\n\n \n Dominguez, J.; Parrado-Duque, A.; Montoya, O. D.; Henao, N.; Campillo, J.; and Agbossou, K.\n\n\n \n\n\n\n In
2023 IEEE Texas Power and Energy Conference (TPEC), pages 1-5, 2023. \n
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@INPROCEEDINGS{10078675,\n\n author={Dominguez, J.A. and Parrado-Duque, A. and Montoya, O. D. and Henao, N. and Campillo, J. and Agbossou, K.},\n\n booktitle={2023 IEEE Texas Power and Energy Conference (TPEC)}, \n\n title={Techno-economic Feasibility of A Trust and Grid-aware Coordination Scheme}, \n\n year={2023},\n\n volume={},\n\n number={},\n\n pages={1-5},\n\n doi={10.1109/TPEC56611.2023.10078675}}\n\n
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\n\n \n \n \n \n \n A Stochastic Approach to Integrating Electrical Thermal Storage in Distributed Demand Response for Nordic Communities With Wind Power Generation.\n \n \n \n\n\n \n Domínguez-Jiménez, J.; Henao, N.; Agbossou, K.; Parrado, A.; Campillo, J.; and Nagarsheth, S. H.\n\n\n \n\n\n\n
IEEE Open Journal of Industry Applications, 4: 121-138. 2023.\n
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@ARTICLE{10093061,\n\n author={Domínguez-Jiménez, Juan and Henao, Nilson and Agbossou, Kodjo and Parrado, Alejandro and Campillo, Javier and Nagarsheth, Shaival H.},\n\n journal={IEEE Open Journal of Industry Applications}, \n\n title={A Stochastic Approach to Integrating Electrical Thermal Storage in Distributed Demand Response for Nordic Communities With Wind Power Generation}, \n\n year={2023},\n\n volume={4},\n\n number={},\n\n pages={121-138},\n\n doi={10.1109/OJIA.2023.3264651}}\n\n
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\n\n \n \n \n \n \n OpenATE: A Distributed Co-simulation Engine for Transactive Energy Systems.\n \n \n \n\n\n \n Arnedo, R.; Henao, N.; Agbossou, K.; Oviedo-Cepeda, J. C.; Dominguez, J. A.; and Toquica, D.\n\n\n \n\n\n\n In
2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE), pages 188-193, 2023. \n
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@INPROCEEDINGS{10274534,\n author={Arnedo, Rafael and Henao, Nilson and Agbossou, Kodjo and Oviedo-Cepeda, Juan Carlos and Dominguez, Juan Antonio and Toquica, David},\n booktitle={2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)}, \n title={OpenATE: A Distributed Co-simulation Engine for Transactive Energy Systems}, \n year={2023},\n volume={},\n number={},\n pages={188-193},\n keywords={Transactive energy;Regulators;Software architecture;Computational modeling;Simulation;Stochastic processes;Software;Co-simulation;Transactive Energy Systems;Event-driven architecture;Smart Grid},\n doi={10.1109/SEGE59172.2023.10274534}}\n\n
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\n\n \n \n \n \n \n Risk Analysis of Transactive Energy Retail Markets.\n \n \n \n\n\n \n Toquica, D.; Amara, F.; Malhamé, R.; Agbossou, K.; Henao, N.; Oviedo, J. C.; and Rueda, L.\n\n\n \n\n\n\n
IEEE Transactions on Industry Applications, 60(1): 1611-1621. 2023.\n
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@ARTICLE{10296018,\n author={Toquica, David and Amara, Fatima and Malhamé, Roland and Agbossou, Kodjo and Henao, Nilson and Oviedo, Juan C. and Rueda, Luis},\n journal={IEEE Transactions on Industry Applications}, \n title={Risk Analysis of Transactive Energy Retail Markets}, \n year={2023},\n volume={60},\n number={1},\n pages={1611-1621},\n keywords={Costs;Risk management;Forward contracts;Uncertainty;Transactive energy;Generators;Cost function;Distribution grid;forward market;local energy market;risk assessment;transactive energy},\n doi={10.1109/TIA.2023.3327320}}\n\n
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\n\n \n \n \n \n \n Co-Simulation Framework OpenDSS-Python to Consider Distribution Grid Constraints in a Transactive Energy System.\n \n \n \n\n\n \n Galeano-Suarez, D.; Oviedo-Cepeda, J.; Henao, N.; Agbossou, K.; Toquica, D.; and Fournier, M.\n\n\n \n\n\n\n In
2023 14th International Renewable Energy Congress (IREC), pages 1-6, 2023. \n
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@INPROCEEDINGS{10389446,\n author={Galeano-Suarez, Daniel and Oviedo-Cepeda, JC and Henao, Nilson and Agbossou, Kodjo and Toquica, David and Fournier, Michael},\n booktitle={2023 14th International Renewable Energy Congress (IREC)}, \n title={Co-Simulation Framework OpenDSS-Python to Consider Distribution Grid Constraints in a Transactive Energy System}, \n year={2023},\n volume={},\n number={},\n pages={1-6},\n keywords={Transactive energy;Renewable energy sources;Reactive power;Systems operation;Voltage;Quality of service;Reliability engineering;Distribution grids;Open-source;Power flow simulation;Residential demand;Transactive energy},\n doi={10.1109/IREC59750.2023.10389446}}\n\n
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\n\n \n \n \n \n \n \n Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand.\n \n \n \n \n\n\n \n Dab, K.; Henao, N.; Nagarsheth, S.; Dubé, Y.; Sansregret, S.; and Agbossou, K.\n\n\n \n\n\n\n
Energy and Buildings, 299: 113550. 2023.\n
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\n\n \n \n ![\"Consensus-based](\"//bibbase.org/img/filetypes/link.svg\"\n\t "\\"Paper\\"\n\t")
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{DAB2023113550,\ntitle = {Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand},\njournal = {Energy and Buildings},\nvolume = {299},\npages = {113550},\nyear = {2023},\nissn = {0378-7788},\ndoi = {https://doi.org/10.1016/j.enbuild.2023.113550},\nurl = {https://www.sciencedirect.com/science/article/pii/S0378778823007806},\nauthor = {Khansa Dab and Nilson Henao and Shaival Nagarsheth and Yves Dubé and Simon Sansregret and Kodjo Agbossou},\nkeywords = {Short-term load forecasting, Aggregated load forecast, Consensus clustering, Gaussian process, Residential load patterns, Time-series},\nabstract = {Load forecasting could play a crucial role in energy management and control of buildings in residential neighborhoods. In these areas, electricity demand is influenced by different phenomena accounting for climate conditions and comfort preferences. The uncertain nature of these circumstances results in power profiles with diverse patterns. Under this condition, overall load prediction is suggested by utilizing Cluster-based Aggregate Forecasting (CBAF). Accordingly, this paper proposes a unified approach to such a practice. The proposed scheme employs an unsupervised machine-learning algorithm to develop a time-series clustering scheme that performs the classification task through the k-medoids-based clustering incorporating the Dynamic Time Warping (DTW) algorithm. Subsequently, a consensus is achieved among the resultant clusters where the Jaccard similarity index adjudges the similarity measurement. The Additive Gaussian Process (AGP), a powerful non-parametric forecasting technique, is exploited to predict aggregated load at each cluster level. With low complexity and high scalability, AGP is particularly utilized to provide effective forecasting. Numerical simulations on synthetic as well as real datasets have been carried out to illustrate the effectiveness of the proposed methodology. Additionally, two comparative studies are carried out with forecasts without clustering and with the benchmark non-parametric models employing a cluster-based technique. The proposed method demonstrates significant improvement in forecasting accuracy for both datasets by reducing the error metrics and achieving 7% improvement in the coefficient of determination (R2) value as compared to the aggregated load forecast achieved without clustering. The comparative study demonstrates that the proposed method with AGP can forecast the total residential load more accurately than other benchmark models with an improvement of 26% and 21% in R2, respectively, for both datasets.}\n}\n\n
\n\n\n
\n Load forecasting could play a crucial role in energy management and control of buildings in residential neighborhoods. In these areas, electricity demand is influenced by different phenomena accounting for climate conditions and comfort preferences. The uncertain nature of these circumstances results in power profiles with diverse patterns. Under this condition, overall load prediction is suggested by utilizing Cluster-based Aggregate Forecasting (CBAF). Accordingly, this paper proposes a unified approach to such a practice. The proposed scheme employs an unsupervised machine-learning algorithm to develop a time-series clustering scheme that performs the classification task through the k-medoids-based clustering incorporating the Dynamic Time Warping (DTW) algorithm. Subsequently, a consensus is achieved among the resultant clusters where the Jaccard similarity index adjudges the similarity measurement. The Additive Gaussian Process (AGP), a powerful non-parametric forecasting technique, is exploited to predict aggregated load at each cluster level. With low complexity and high scalability, AGP is particularly utilized to provide effective forecasting. Numerical simulations on synthetic as well as real datasets have been carried out to illustrate the effectiveness of the proposed methodology. Additionally, two comparative studies are carried out with forecasts without clustering and with the benchmark non-parametric models employing a cluster-based technique. The proposed method demonstrates significant improvement in forecasting accuracy for both datasets by reducing the error metrics and achieving 7% improvement in the coefficient of determination (R2) value as compared to the aggregated load forecast achieved without clustering. The comparative study demonstrates that the proposed method with AGP can forecast the total residential load more accurately than other benchmark models with an improvement of 26% and 21% in R2, respectively, for both datasets.\n
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\n\n \n \n \n \n \n \n Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand.\n \n \n \n \n\n\n \n Dab, K.; Henao, N.; Nagarsheth, S.; Dubé, Y.; Sansregret, S.; and Agbossou, K.\n\n\n \n\n\n\n
Energy and Buildings, 299: 113550. 2023.\n
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\n\n
\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{DAB2023113550,\ntitle = {Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand},\njournal = {Energy and Buildings},\nvolume = {299},\npages = {113550},\nyear = {2023},\nissn = {0378-7788},\ndoi = {https://doi.org/10.1016/j.enbuild.2023.113550},\nurl = {https://www.sciencedirect.com/science/article/pii/S0378778823007806},\nauthor = {Khansa Dab and Nilson Henao and Shaival Nagarsheth and Yves Dubé and Simon Sansregret and Kodjo Agbossou},\nkeywords = {Short-term load forecasting, Aggregated load forecast, Consensus clustering, Gaussian process, Residential load patterns, Time-series},\nabstract = {Load forecasting could play a crucial role in energy management and control of buildings in residential neighborhoods. In these areas, electricity demand is influenced by different phenomena accounting for climate conditions and comfort preferences. The uncertain nature of these circumstances results in power profiles with diverse patterns. Under this condition, overall load prediction is suggested by utilizing Cluster-based Aggregate Forecasting (CBAF). Accordingly, this paper proposes a unified approach to such a practice. The proposed scheme employs an unsupervised machine-learning algorithm to develop a time-series clustering scheme that performs the classification task through the k-medoids-based clustering incorporating the Dynamic Time Warping (DTW) algorithm. Subsequently, a consensus is achieved among the resultant clusters where the Jaccard similarity index adjudges the similarity measurement. The Additive Gaussian Process (AGP), a powerful non-parametric forecasting technique, is exploited to predict aggregated load at each cluster level. With low complexity and high scalability, AGP is particularly utilized to provide effective forecasting. Numerical simulations on synthetic as well as real datasets have been carried out to illustrate the effectiveness of the proposed methodology. Additionally, two comparative studies are carried out with forecasts without clustering and with the benchmark non-parametric models employing a cluster-based technique. The proposed method demonstrates significant improvement in forecasting accuracy for both datasets by reducing the error metrics and achieving 7% improvement in the coefficient of determination (R2) value as compared to the aggregated load forecast achieved without clustering. The comparative study demonstrates that the proposed method with AGP can forecast the total residential load more accurately than other benchmark models with an improvement of 26% and 21% in R2, respectively, for both datasets.}\n}\n\n
\n\n\n
\n Load forecasting could play a crucial role in energy management and control of buildings in residential neighborhoods. In these areas, electricity demand is influenced by different phenomena accounting for climate conditions and comfort preferences. The uncertain nature of these circumstances results in power profiles with diverse patterns. Under this condition, overall load prediction is suggested by utilizing Cluster-based Aggregate Forecasting (CBAF). Accordingly, this paper proposes a unified approach to such a practice. The proposed scheme employs an unsupervised machine-learning algorithm to develop a time-series clustering scheme that performs the classification task through the k-medoids-based clustering incorporating the Dynamic Time Warping (DTW) algorithm. Subsequently, a consensus is achieved among the resultant clusters where the Jaccard similarity index adjudges the similarity measurement. The Additive Gaussian Process (AGP), a powerful non-parametric forecasting technique, is exploited to predict aggregated load at each cluster level. With low complexity and high scalability, AGP is particularly utilized to provide effective forecasting. Numerical simulations on synthetic as well as real datasets have been carried out to illustrate the effectiveness of the proposed methodology. Additionally, two comparative studies are carried out with forecasts without clustering and with the benchmark non-parametric models employing a cluster-based technique. The proposed method demonstrates significant improvement in forecasting accuracy for both datasets by reducing the error metrics and achieving 7% improvement in the coefficient of determination (R2) value as compared to the aggregated load forecast achieved without clustering. The comparative study demonstrates that the proposed method with AGP can forecast the total residential load more accurately than other benchmark models with an improvement of 26% and 21% in R2, respectively, for both datasets.\n
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\n\n \n \n \n \n \n \n Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters.\n \n \n \n \n\n\n \n Etedadi, F.; Kelouwani, S.; Agbossou, K.; Henao, N.; and Laurencelle, F.\n\n\n \n\n\n\n
Applied Energy, 336: 120833. 2023.\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{ETEDADI2023120833,\ntitle = {Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters},\njournal = {Applied Energy},\nvolume = {336},\npages = {120833},\nyear = {2023},\nissn = {0306-2619},\ndoi = {https://doi.org/10.1016/j.apenergy.2023.120833},\nurl = {https://www.sciencedirect.com/science/article/pii/S0306261923001976},\nauthor = {Farshad Etedadi and Sousso Kelouwani and Kodjo Agbossou and Nilson Henao and François Laurencelle},\nkeywords = {Demand response, Home energy management, Coordination, Smart grids, Gain distribution, Transactive energy},\nabstract = {The repercussions from excessive penetration of uncoordinated Home Energy Management Systems (HEMSs) have proven to be pernicious in the distribution system regarding contingencies, instabilities, and rebound peaks. This paper aims to design a distributed coordination technique with the required topology to coordinate transactive HEMSs with demand response enabled electric baseboard heater thermostats to avoid the detrimental effects of uncoordinated HEMSs in a residential group. Specifically, the proposed technique establishes a consensus to fulfill individual as well as shared objectives by modifying consumers’ consumption patterns. The shared objective is to flatten the aggregated profile and decrease the total cost in the grid. In addition, an incentive policy has been designed to pay a total reward to the team for encouraging consumers to participate in the coordination. The presented coordination technique comprises a Shapley game-based reward-sharing mechanism and an incentive-compatible mechanism, where the team’s gain is distributed among the players based on their contribution. Besides, the coordination leads to agents’ complementary decision-making and mitigates the grid challenges. The functionality and effectiveness of the proposed coordinated HEMSs algorithm are tested for a set of different case studies based on user preferences and coordination levels. The simulation results indicate that the proposed coordination improves aggregated profile’s load factor up to 0.85 and reduces the electricity bill by 21.4%.}\n}\n\n\n
\n\n\n
\n The repercussions from excessive penetration of uncoordinated Home Energy Management Systems (HEMSs) have proven to be pernicious in the distribution system regarding contingencies, instabilities, and rebound peaks. This paper aims to design a distributed coordination technique with the required topology to coordinate transactive HEMSs with demand response enabled electric baseboard heater thermostats to avoid the detrimental effects of uncoordinated HEMSs in a residential group. Specifically, the proposed technique establishes a consensus to fulfill individual as well as shared objectives by modifying consumers’ consumption patterns. The shared objective is to flatten the aggregated profile and decrease the total cost in the grid. In addition, an incentive policy has been designed to pay a total reward to the team for encouraging consumers to participate in the coordination. The presented coordination technique comprises a Shapley game-based reward-sharing mechanism and an incentive-compatible mechanism, where the team’s gain is distributed among the players based on their contribution. Besides, the coordination leads to agents’ complementary decision-making and mitigates the grid challenges. The functionality and effectiveness of the proposed coordinated HEMSs algorithm are tested for a set of different case studies based on user preferences and coordination levels. The simulation results indicate that the proposed coordination improves aggregated profile’s load factor up to 0.85 and reduces the electricity bill by 21.4%.\n
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\n\n \n \n \n \n \n Risk Analysis of Transactive Energy Retail Markets.\n \n \n \n\n\n \n Toquica, D.; Amara, F.; Malhamé, R.; Agbossou, K.; Henao, N.; Oviedo, J. C.; and Rueda, L.\n\n\n \n\n\n\n
IEEE Transactions on Industry Applications,1-11. 2023.\n
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@ARTICLE{10296018,\n\n author={Toquica, David and Amara, Fatima and Malhamé, Roland and Agbossou, Kodjo and Henao, Nilson and Oviedo, Juan C. and Rueda, Luis},\n\n journal={IEEE Transactions on Industry Applications}, \n\n title={Risk Analysis of Transactive Energy Retail Markets}, \n\n year={2023},\n\n volume={},\n\n number={},\n\n pages={1-11},\n\n doi={10.1109/TIA.2023.3327320}}\n\n\n
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\n\n \n \n \n \n \n \n Towards Feasible Solutions for Load Monitoring in Quebec Residences.\n \n \n \n \n\n\n \n Hosseini, S. S.; Delcroix, B.; Henao, N.; Agbossou, K.; and Kelouwani, S.\n\n\n \n\n\n\n
Sensors, 23(16). 2023.\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@Article{s23167288,\nAUTHOR = {Hosseini, Sayed Saeed and Delcroix, Benoit and Henao, Nilson and Agbossou, Kodjo and Kelouwani, Sousso},\nTITLE = {Towards Feasible Solutions for Load Monitoring in Quebec Residences},\nJOURNAL = {Sensors},\nVOLUME = {23},\nYEAR = {2023},\nNUMBER = {16},\nARTICLE-NUMBER = {7288},\nURL = {https://www.mdpi.com/1424-8220/23/16/7288},\nPubMedID = {37631824},\nISSN = {1424-8220},\nABSTRACT = {For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.},\nDOI = {10.3390/s23167288}\n}\n\n\n\n\n
\n\n\n
\n For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.\n
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\n\n \n \n \n \n \n OpenATE: A Distributed Co-simulation Engine for Transactive Energy Systems.\n \n \n \n\n\n \n Arnedo, R.; Henao, N.; Agbossou, K.; Oviedo-Cepeda, J. C.; Dominguez, J. A.; and Toquica, D.\n\n\n \n\n\n\n In
2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE), pages 188-193, 2023. \n
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@INPROCEEDINGS{10274534,\n\n author={Arnedo, Rafael and Henao, Nilson and Agbossou, Kodjo and Oviedo-Cepeda, Juan Carlos and Dominguez, Juan Antonio and Toquica, David},\n\n booktitle={2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)}, \n\n title={OpenATE: A Distributed Co-simulation Engine for Transactive Energy Systems}, \n\n year={2023},\n\n volume={},\n\n number={},\n\n pages={188-193},\n\n doi={10.1109/SEGE59172.2023.10274534}}\n\n
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\n\n \n \n \n \n \n \n Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters.\n \n \n \n \n\n\n \n Etedadi, F.; Kelouwani, S.; Agbossou, K.; Henao, N.; and Laurencelle, F.\n\n\n \n\n\n\n
Applied Energy, 336: 120833. 2023.\n
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@article{ETEDADI2023120833,\ntitle = {Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters},\njournal = {Applied Energy},\nvolume = {336},\npages = {120833},\nyear = {2023},\nissn = {0306-2619},\ndoi = {https://doi.org/10.1016/j.apenergy.2023.120833},\nurl = {https://www.sciencedirect.com/science/article/pii/S0306261923001976},\nauthor = {Farshad Etedadi and Sousso Kelouwani and Kodjo Agbossou and Nilson Henao and François Laurencelle},\nkeywords = {Demand response, Home energy management, Coordination, Smart grids, Gain distribution, Transactive energy},\nabstract = {The repercussions from excessive penetration of uncoordinated Home Energy Management Systems (HEMSs) have proven to be pernicious in the distribution system regarding contingencies, instabilities, and rebound peaks. This paper aims to design a distributed coordination technique with the required topology to coordinate transactive HEMSs with demand response enabled electric baseboard heater thermostats to avoid the detrimental effects of uncoordinated HEMSs in a residential group. Specifically, the proposed technique establishes a consensus to fulfill individual as well as shared objectives by modifying consumers’ consumption patterns. The shared objective is to flatten the aggregated profile and decrease the total cost in the grid. In addition, an incentive policy has been designed to pay a total reward to the team for encouraging consumers to participate in the coordination. The presented coordination technique comprises a Shapley game-based reward-sharing mechanism and an incentive-compatible mechanism, where the team’s gain is distributed among the players based on their contribution. Besides, the coordination leads to agents’ complementary decision-making and mitigates the grid challenges. The functionality and effectiveness of the proposed coordinated HEMSs algorithm are tested for a set of different case studies based on user preferences and coordination levels. The simulation results indicate that the proposed coordination improves aggregated profile’s load factor up to 0.85 and reduces the electricity bill by 21.4%.}\n}\n\n
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\n The repercussions from excessive penetration of uncoordinated Home Energy Management Systems (HEMSs) have proven to be pernicious in the distribution system regarding contingencies, instabilities, and rebound peaks. This paper aims to design a distributed coordination technique with the required topology to coordinate transactive HEMSs with demand response enabled electric baseboard heater thermostats to avoid the detrimental effects of uncoordinated HEMSs in a residential group. Specifically, the proposed technique establishes a consensus to fulfill individual as well as shared objectives by modifying consumers’ consumption patterns. The shared objective is to flatten the aggregated profile and decrease the total cost in the grid. In addition, an incentive policy has been designed to pay a total reward to the team for encouraging consumers to participate in the coordination. The presented coordination technique comprises a Shapley game-based reward-sharing mechanism and an incentive-compatible mechanism, where the team’s gain is distributed among the players based on their contribution. Besides, the coordination leads to agents’ complementary decision-making and mitigates the grid challenges. The functionality and effectiveness of the proposed coordinated HEMSs algorithm are tested for a set of different case studies based on user preferences and coordination levels. The simulation results indicate that the proposed coordination improves aggregated profile’s load factor up to 0.85 and reduces the electricity bill by 21.4%.\n
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