Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution
[Objective] In the context of high renewable energy penetration， the collaborative operation of multiple virtual power plants （VPPs） faces dual challenges： uncertainty risks and conflicts in benefit distribution. This study proposes a collaborative optimization strategy for multiple VPPs that integr...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Power Construction
2025-07-01
|
| Series: | Dianli jianshe |
| Subjects: | |
| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1750819683700-1644729581.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849683170957459456 |
|---|---|
| author | TANG Chenyang, WANG Lei, JIANG Weijian |
| author_facet | TANG Chenyang, WANG Lei, JIANG Weijian |
| author_sort | TANG Chenyang, WANG Lei, JIANG Weijian |
| collection | DOAJ |
| description | [Objective] In the context of high renewable energy penetration， the collaborative operation of multiple virtual power plants （VPPs） faces dual challenges： uncertainty risks and conflicts in benefit distribution. This study proposes a collaborative optimization strategy for multiple VPPs that integrates risk quantification with hybrid game theory by combining conditional value-at-risk （CVaR） and a multi-agent game framework. This approach provides a new perspective for collaborative VPP optimization in scenarios with high renewable energy integration.[Methods] First， a scenario analysis method combining Latin hypercube sampling （LHS） and Manhattan probability distance was designed to address the uncertainties in wind and solar output as well as electricity prices. CVaR was adopted to measure the impact of these uncertainty risks. Second， a Stackelberg game framework was constructed between the distribution system operator （DSO） and the VPP alliance， where the VPP alliance， based on cooperative game theory， established an asymmetric Nash bargaining model incorporating energy contributions. The model was then decomposed into two subproblems： maximizing alliance benefits and distributing cooperative benefits. Finally， the hybrid game model was solved using a combination of the bisection method and the alternating direction method of multipliers （ADMM）.[Results] Simulation results demonstrate that the proposed coordinated optimization strategy for VPPs effectively enhances the operational economy of the VPP alliance and improves operational reliability and security under uncertainty.[Conclusions] The proposed strategy increased the flexibility of coordinated operations among multiple VPPs. By incorporating CVaR for risk quantification and multi-agent game theory， the strategy not only enhances overall system benefits but also ensures a fair distribution of cooperative gains. Moreover， VPPs can balance the risk-benefit trade-off based on their risk aversion coefficients， providing a valuable reference for rational dispatch decision-making. |
| format | Article |
| id | doaj-art-6b423e4218c44500a4041336a728cf13 |
| institution | DOAJ |
| issn | 1000-7229 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Department of Electric Power Construction |
| record_format | Article |
| series | Dianli jianshe |
| spelling | doaj-art-6b423e4218c44500a4041336a728cf132025-08-20T03:23:59ZzhoEditorial Department of Electric Power ConstructionDianli jianshe1000-72292025-07-01467274110.12204/j.issn.1000-7229.2025.07.003Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy ContributionTANG Chenyang, WANG Lei, JIANG Weijian01. College of Electrical Engineering， Shanghai University of Electric Power， Shanghai 200090， China;2. State Grid Jiaxing Electric Power Company， Jiaxing 314000， Zhejiang Province， China[Objective] In the context of high renewable energy penetration， the collaborative operation of multiple virtual power plants （VPPs） faces dual challenges： uncertainty risks and conflicts in benefit distribution. This study proposes a collaborative optimization strategy for multiple VPPs that integrates risk quantification with hybrid game theory by combining conditional value-at-risk （CVaR） and a multi-agent game framework. This approach provides a new perspective for collaborative VPP optimization in scenarios with high renewable energy integration.[Methods] First， a scenario analysis method combining Latin hypercube sampling （LHS） and Manhattan probability distance was designed to address the uncertainties in wind and solar output as well as electricity prices. CVaR was adopted to measure the impact of these uncertainty risks. Second， a Stackelberg game framework was constructed between the distribution system operator （DSO） and the VPP alliance， where the VPP alliance， based on cooperative game theory， established an asymmetric Nash bargaining model incorporating energy contributions. The model was then decomposed into two subproblems： maximizing alliance benefits and distributing cooperative benefits. Finally， the hybrid game model was solved using a combination of the bisection method and the alternating direction method of multipliers （ADMM）.[Results] Simulation results demonstrate that the proposed coordinated optimization strategy for VPPs effectively enhances the operational economy of the VPP alliance and improves operational reliability and security under uncertainty.[Conclusions] The proposed strategy increased the flexibility of coordinated operations among multiple VPPs. By incorporating CVaR for risk quantification and multi-agent game theory， the strategy not only enhances overall system benefits but also ensures a fair distribution of cooperative gains. Moreover， VPPs can balance the risk-benefit trade-off based on their risk aversion coefficients， providing a valuable reference for rational dispatch decision-making.https://www.cepc.com.cn/fileup/1000-7229/PDF/1750819683700-1644729581.pdfvirtual power plant（vpp）|hybrid game theory|conditional value-at-risk（cvar）|asymmetric nash bargaining |
| spellingShingle | TANG Chenyang, WANG Lei, JIANG Weijian Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution Dianli jianshe virtual power plant（vpp）|hybrid game theory|conditional value-at-risk（cvar）|asymmetric nash bargaining |
| title | Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution |
| title_full | Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution |
| title_fullStr | Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution |
| title_full_unstemmed | Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution |
| title_short | Collaborative Optimization Strategy for Multiple Virtual Power Plants Considering Uncertainty Risk and Energy Contribution |
| title_sort | collaborative optimization strategy for multiple virtual power plants considering uncertainty risk and energy contribution |
| topic | virtual power plant（vpp）|hybrid game theory|conditional value-at-risk（cvar）|asymmetric nash bargaining |
| url | https://www.cepc.com.cn/fileup/1000-7229/PDF/1750819683700-1644729581.pdf |
| work_keys_str_mv | AT tangchenyangwangleijiangweijian collaborativeoptimizationstrategyformultiplevirtualpowerplantsconsideringuncertaintyriskandenergycontribution |