Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty
The engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/8/2006 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850143825596514304 |
|---|---|
| author | Zhonghai Sun Runyi Pi Junjie Yang Chao Yang Xin Chen |
| author_facet | Zhonghai Sun Runyi Pi Junjie Yang Chao Yang Xin Chen |
| author_sort | Zhonghai Sun |
| collection | DOAJ |
| description | The engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (CESSOs), and load aggregators (LAs) collectively participate in market competition, this study develops a bi-level game-theoretic framework for market equilibrium analysis. The proposed architecture comprises two interdependent layers: The upper-layer Stackelberg game coordinates strategic interactions among EVA, LA, and CESSO to mitigate bidding uncertainties through cooperative mechanisms. The lower-layer non-cooperative Nash game models competition patterns to determine market equilibria under multi-agent participation. A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches. |
| format | Article |
| id | doaj-art-5aaf4043fef746ec91eed89e2c532dbb |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-5aaf4043fef746ec91eed89e2c532dbb2025-08-20T02:28:33ZengMDPI AGEnergies1996-10732025-04-01188200610.3390/en18082006Equilibrium Analysis of Electricity Market with Multi-Agents Considering UncertaintyZhonghai Sun0Runyi Pi1Junjie Yang2Chao Yang3Xin Chen4School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaThe engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (CESSOs), and load aggregators (LAs) collectively participate in market competition, this study develops a bi-level game-theoretic framework for market equilibrium analysis. The proposed architecture comprises two interdependent layers: The upper-layer Stackelberg game coordinates strategic interactions among EVA, LA, and CESSO to mitigate bidding uncertainties through cooperative mechanisms. The lower-layer non-cooperative Nash game models competition patterns to determine market equilibria under multi-agent participation. A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches.https://www.mdpi.com/1996-1073/18/8/2006electric vehicle aggregatorcloud energy storage system operatorload aggregatorscene reduction methodelectricity market equilibrium |
| spellingShingle | Zhonghai Sun Runyi Pi Junjie Yang Chao Yang Xin Chen Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty Energies electric vehicle aggregator cloud energy storage system operator load aggregator scene reduction method electricity market equilibrium |
| title | Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty |
| title_full | Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty |
| title_fullStr | Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty |
| title_full_unstemmed | Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty |
| title_short | Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty |
| title_sort | equilibrium analysis of electricity market with multi agents considering uncertainty |
| topic | electric vehicle aggregator cloud energy storage system operator load aggregator scene reduction method electricity market equilibrium |
| url | https://www.mdpi.com/1996-1073/18/8/2006 |
| work_keys_str_mv | AT zhonghaisun equilibriumanalysisofelectricitymarketwithmultiagentsconsideringuncertainty AT runyipi equilibriumanalysisofelectricitymarketwithmultiagentsconsideringuncertainty AT junjieyang equilibriumanalysisofelectricitymarketwithmultiagentsconsideringuncertainty AT chaoyang equilibriumanalysisofelectricitymarketwithmultiagentsconsideringuncertainty AT xinchen equilibriumanalysisofelectricitymarketwithmultiagentsconsideringuncertainty |