Multi-objective robust optimization strategy for community virtual cloud power plant considering different demand responses based on correlative confidence gap decision theory
•Develop a two-layer community CDR model and consider various types of users.•Build CCGDT multi-objective robust models with CGDT and time-varying copula theory.•Solve the optimal strategy by MOGWO and FUCOM method.•After optimization, the multi-objective values are increased by 0.054%−51.31%. To st...
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| Published in: | Sustainable cities and society Vol. 97; p. 104738 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2023
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| ISSN: | 2210-6707 |
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| Abstract | •Develop a two-layer community CDR model and consider various types of users.•Build CCGDT multi-objective robust models with CGDT and time-varying copula theory.•Solve the optimal strategy by MOGWO and FUCOM method.•After optimization, the multi-objective values are increased by 0.054%−51.31%.
To study the effect of different demand responses (DRs) on solving uncertainty, a multi-objective robust optimization model for a community virtual cloud power plant (CVCPP) is proposed based on the correlative confidence gap decision theory (CCGDT) and considering different DRs. Firstly, a CVCPP framework with multi-energy conversion equipment, multi-load DR, and a rapid energy scheduling information processing approach is proposed. Secondly, a two-layer community comprehensive demand response model is developed, which includes the CDR of CIES internal consumers and the CDR of the external energy network that connects to CIES. Thirdly, the CCGDT is presented using the confidence gap decision model and the time-varying Copula theory. A multi-objective robust economy-energy-environment optimization model is developed. Fourthly, the Pareto solution set is solved using the multi-objective grey wolf optimization algorithm. The full consistency method is employed to identify an optimal scheduling strategy for decision-makers. Finally, the model's validity is verified by a multi-scene case of a residential area. The results show that: 1) Whether uncertainty is considered or not, different DRs have varied effects on the results. Combination DR are better than single, and the same DR has different effects on different residents. 2) The multi-objective values improve after adopting the integrated energy system. The resident cost of various residents is decreased by 0.054%-12.07% when uncertainty is included. Meanwhile, the profit of energy providers is improved by 0.95%-10.98%, the carbon treatment amount is lowered by 26.85%-51.31%, and the renewable energy utilization rate is enhanced by 0.85%-7.94%. 3) The results with different significance have different robustness to uncertainty changes, and the greater the significance, the stronger the robustness. Therefore, the decision-maker can flexibly control the optimization result by setting the confidence of the target value. The model plays a good role in the economy-energy-environment aspects and provides a reference for community decision-makers to choose the comprehensive multi-objective energy scheduling strategy considering the uncertainty. |
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| AbstractList | •Develop a two-layer community CDR model and consider various types of users.•Build CCGDT multi-objective robust models with CGDT and time-varying copula theory.•Solve the optimal strategy by MOGWO and FUCOM method.•After optimization, the multi-objective values are increased by 0.054%−51.31%.
To study the effect of different demand responses (DRs) on solving uncertainty, a multi-objective robust optimization model for a community virtual cloud power plant (CVCPP) is proposed based on the correlative confidence gap decision theory (CCGDT) and considering different DRs. Firstly, a CVCPP framework with multi-energy conversion equipment, multi-load DR, and a rapid energy scheduling information processing approach is proposed. Secondly, a two-layer community comprehensive demand response model is developed, which includes the CDR of CIES internal consumers and the CDR of the external energy network that connects to CIES. Thirdly, the CCGDT is presented using the confidence gap decision model and the time-varying Copula theory. A multi-objective robust economy-energy-environment optimization model is developed. Fourthly, the Pareto solution set is solved using the multi-objective grey wolf optimization algorithm. The full consistency method is employed to identify an optimal scheduling strategy for decision-makers. Finally, the model's validity is verified by a multi-scene case of a residential area. The results show that: 1) Whether uncertainty is considered or not, different DRs have varied effects on the results. Combination DR are better than single, and the same DR has different effects on different residents. 2) The multi-objective values improve after adopting the integrated energy system. The resident cost of various residents is decreased by 0.054%-12.07% when uncertainty is included. Meanwhile, the profit of energy providers is improved by 0.95%-10.98%, the carbon treatment amount is lowered by 26.85%-51.31%, and the renewable energy utilization rate is enhanced by 0.85%-7.94%. 3) The results with different significance have different robustness to uncertainty changes, and the greater the significance, the stronger the robustness. Therefore, the decision-maker can flexibly control the optimization result by setting the confidence of the target value. The model plays a good role in the economy-energy-environment aspects and provides a reference for community decision-makers to choose the comprehensive multi-objective energy scheduling strategy considering the uncertainty. |
| ArticleNumber | 104738 |
| Author | Haoyu, Wu Ningbo, Huang Fangjie, Gao Jianwei, Gao |
| Author_xml | – sequence: 1 givenname: Gao surname: Fangjie fullname: Fangjie, Gao organization: School of Economics and Management, North China Electric Power University, Beijing 102206, PR China – sequence: 2 givenname: Gao surname: Jianwei fullname: Jianwei, Gao email: gaojianwei111@sina.cn organization: School of Economics and Management, North China Electric Power University, Beijing 102206, PR China – sequence: 3 givenname: Huang surname: Ningbo fullname: Ningbo, Huang organization: School of Economics and Management, North China Electric Power University, Beijing 102206, PR China – sequence: 4 givenname: Wu surname: Haoyu fullname: Haoyu, Wu organization: School of Economics and Management, North China Electric Power University, Beijing 102206, PR China |
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| Cites_doi | 10.1016/j.energy.2018.01.078 10.1016/j.apenergy.2019.04.144 10.1016/j.eswa.2015.10.039 10.1016/j.rser.2015.12.070 10.1016/j.apenergy.2021.117608 10.1016/j.scs.2021.103075 10.1109/TIA.2021.3120971 10.1016/j.est.2023.107366 10.1016/j.enconman.2020.113597 10.1016/j.scs.2022.103711 10.1016/j.advengsoft.2013.12.007 10.3390/sym10090393 10.1016/j.energy.2019.116780 10.1016/j.ijepes.2020.106126 10.1016/j.scs.2021.103652 10.1016/j.ijepes.2021.107304 |
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| Keywords | Community virtual cloud power plant Multi-objective grey wolf optimization algorithm Demand response Full consistency method Correlative confidence gap decision theory Multi-objective robust optimization |
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| SubjectTerms | Community virtual cloud power plant Correlative confidence gap decision theory Demand response Full consistency method Multi-objective grey wolf optimization algorithm Multi-objective robust optimization |
| Title | Multi-objective robust optimization strategy for community virtual cloud power plant considering different demand responses based on correlative confidence gap decision theory |
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