Virtual power plant capacity tariff pricing method based on master–slave game
The large-scale construction of virtual power plants (VPPs) is a crucial method for alleviating the tight supply–demand balance during peak periods, necessitating capacity tariff incentives to encourage their development. Consequently, a VPP capacity tariff pricing method based on a master–slave gam...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525003229 |
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| author | Shaobo Yin Weiqing Sun Haibing Wang |
| author_facet | Shaobo Yin Weiqing Sun Haibing Wang |
| author_sort | Shaobo Yin |
| collection | DOAJ |
| description | The large-scale construction of virtual power plants (VPPs) is a crucial method for alleviating the tight supply–demand balance during peak periods, necessitating capacity tariff incentives to encourage their development. Consequently, a VPP capacity tariff pricing method based on a master–slave game is proposed, considering the incentive effect of the capacity tariff on VPP construction. The grid is dominant within this framework, determining the capacity tariff based on construction capacity signals. VPP, as the follower, responds optimally to the leader’s pricing strategy to decide on construction capacity, with both parties influencing each other until equilibrium is reached. Based on this, the upper-level grid minimizes the operational cost of the power system by optimizing the values of capacity tariff and VPP output. The lower-level VPP aims for an annual return on investment (ROI) of 20%, thus determining the optimal construction capacity. Furthermore, the grey wolf optimization (GWO) algorithm is employed to solve the master–slave game problem. The proposed method’s effectiveness and rationality are verified through a real-world scenario of a regional grid. The results indicate that the master–slave game pricing method effectively simulates the interactive decision-making relationship between the grid and VPP sides, ensuring the rationality of VPP capacity tariff and construction capacity decisions, thereby providing valuable references for managers. |
| format | Article |
| id | doaj-art-a7fc432e03fb4722b322afc3dbd44345 |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-a7fc432e03fb4722b322afc3dbd443452025-08-20T02:07:56ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-08-0116911077410.1016/j.ijepes.2025.110774Virtual power plant capacity tariff pricing method based on master–slave gameShaobo Yin0Weiqing Sun1Haibing Wang2School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaCorresponding author.; School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaThe large-scale construction of virtual power plants (VPPs) is a crucial method for alleviating the tight supply–demand balance during peak periods, necessitating capacity tariff incentives to encourage their development. Consequently, a VPP capacity tariff pricing method based on a master–slave game is proposed, considering the incentive effect of the capacity tariff on VPP construction. The grid is dominant within this framework, determining the capacity tariff based on construction capacity signals. VPP, as the follower, responds optimally to the leader’s pricing strategy to decide on construction capacity, with both parties influencing each other until equilibrium is reached. Based on this, the upper-level grid minimizes the operational cost of the power system by optimizing the values of capacity tariff and VPP output. The lower-level VPP aims for an annual return on investment (ROI) of 20%, thus determining the optimal construction capacity. Furthermore, the grey wolf optimization (GWO) algorithm is employed to solve the master–slave game problem. The proposed method’s effectiveness and rationality are verified through a real-world scenario of a regional grid. The results indicate that the master–slave game pricing method effectively simulates the interactive decision-making relationship between the grid and VPP sides, ensuring the rationality of VPP capacity tariff and construction capacity decisions, thereby providing valuable references for managers.http://www.sciencedirect.com/science/article/pii/S0142061525003229Virtual power plantCapacity tariffMaster-slave gamePeak-shaving demand responseGrey wolf optimization algorithm |
| spellingShingle | Shaobo Yin Weiqing Sun Haibing Wang Virtual power plant capacity tariff pricing method based on master–slave game International Journal of Electrical Power & Energy Systems Virtual power plant Capacity tariff Master-slave game Peak-shaving demand response Grey wolf optimization algorithm |
| title | Virtual power plant capacity tariff pricing method based on master–slave game |
| title_full | Virtual power plant capacity tariff pricing method based on master–slave game |
| title_fullStr | Virtual power plant capacity tariff pricing method based on master–slave game |
| title_full_unstemmed | Virtual power plant capacity tariff pricing method based on master–slave game |
| title_short | Virtual power plant capacity tariff pricing method based on master–slave game |
| title_sort | virtual power plant capacity tariff pricing method based on master slave game |
| topic | Virtual power plant Capacity tariff Master-slave game Peak-shaving demand response Grey wolf optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525003229 |
| work_keys_str_mv | AT shaoboyin virtualpowerplantcapacitytariffpricingmethodbasedonmasterslavegame AT weiqingsun virtualpowerplantcapacitytariffpricingmethodbasedonmasterslavegame AT haibingwang virtualpowerplantcapacitytariffpricingmethodbasedonmasterslavegame |