Paper doi abstract bibtex

To alleviate environmental pollution and improve the energy usage efficiency of terminals, energy systems integration (ESI) has become an important paradigm in the energy structure evolution. Power, gas and heat systems are becoming tightly interlinked with each other in ESI. The dispatching strategy of local-area ESI has significant impact on its operation. In this paper, a local-area ESI operational scheduling model based on conditional value-at-risk (CVaR) is proposed to minimize expected operational cost, which considers the uncertainty of energy supply-side and demand-side as well as multi-energy network constraints, including electrical network, thermal network and gas network. The risk cost is analyzed comprehensively under the condition of under- or overestimated cost. On this basis, a hybrid method combining particle swarm optimization with interior point algorithm is executed to compute the optimal solutions of two-stage multi-period mixed-integer convex model. Finally, a case study is performed on ESI to demonstrate the effectiveness of the proposed method.

@article{shi_operating_2017, title = {Operating {Strategy} for {Local}-{Area} {Energy} {Systems} {Integration} {Considering} {Uncertainty} of {Supply}-{Side} and {Demand}-{Side} under {Conditional} {Value}-{At}-{Risk} {Assessment}}, volume = {9}, copyright = {http://creativecommons.org/licenses/by/3.0/}, url = {https://www.mdpi.com/2071-1050/9/9/1655}, doi = {10.3390/su9091655}, abstract = {To alleviate environmental pollution and improve the energy usage efficiency of terminals, energy systems integration (ESI) has become an important paradigm in the energy structure evolution. Power, gas and heat systems are becoming tightly interlinked with each other in ESI. The dispatching strategy of local-area ESI has significant impact on its operation. In this paper, a local-area ESI operational scheduling model based on conditional value-at-risk (CVaR) is proposed to minimize expected operational cost, which considers the uncertainty of energy supply-side and demand-side as well as multi-energy network constraints, including electrical network, thermal network and gas network. The risk cost is analyzed comprehensively under the condition of under- or overestimated cost. On this basis, a hybrid method combining particle swarm optimization with interior point algorithm is executed to compute the optimal solutions of two-stage multi-period mixed-integer convex model. Finally, a case study is performed on ESI to demonstrate the effectiveness of the proposed method.}, language = {en}, number = {9}, urldate = {2020-06-21}, journal = {Sustainability}, author = {Shi, Jiaqi and Wang, Yingrui and Fu, Ruibin and Zhang, Jianhua}, month = sep, year = {2017}, note = {Number: 9 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {bi-level optimization, conditional value-at-risk (CVaR), energy systems integration (ESI), multi-energy network constraints, risk cost}, pages = {1655}, }

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