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Energy management strategy for community prosumers aggregated VPP participation in the ancillary services market based on P2P trading

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  • Li, Li
  • Fan, Shuai
  • Xiao, Jucheng
  • Zhang, Yi
  • Huang, Renke
  • He, Guangyu

Abstract

Multi-energy virtual power plants (MEVPPs) are a promising option for aggregating decentralized resources to meet the access requirements of the ancillary services (ASs) market. However, distributed resources are generally considered to be vertically scheduled by MEVPPs, ignoring that their ownership belongs to different prosumers with evident interest conflicts. To this end, this study proposes an equilibrium energy management strategy for MEVPPs that aggregate community prosumers to participate in the ASs market with peer-to-peer (P2P) transaction-driven. That is, MEVPP, as a bridge, articulates the local P2P market and the ASs market, solving the market incompatibility problem. MEVPP sets energy and AS prices, and prosumers determine P2P exchange and operation strategies based on their information and MEVPP's P2P pricing. MEVPP adjusts P2P prices according to the aggregation of prosumers' ASs until the benefit allocation is balanced. Then, MEVPP completes the bid. In this process, ancillary service requirements are effectively internalized in P2P prices. Heterogeneous prosumer models are developed considering comprehensive ASs scheduling for multi-energy devices. An illustrative case study indicates that compared to prosumers stand-alone, the proposed approach makes a profit of 19,830 RMB/day for MEVPP and cost savings of 39%, 9% and 35% for recreational, industrial and commercial prosumers, respectively.

Suggested Citation

  • Li, Li & Fan, Shuai & Xiao, Jucheng & Zhang, Yi & Huang, Renke & He, Guangyu, 2025. "Energy management strategy for community prosumers aggregated VPP participation in the ancillary services market based on P2P trading," Applied Energy, Elsevier, vol. 384(C).
    • Handle: RePEc:eee:appene:v:384:y:2025:i:c:s0306261925002028
    DOI: 10.1016/j.apenergy.2025.125472
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    References listed on IDEAS

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    Cited by:

    1. Li, Li & Fan, Shuai & Dong, Lianxin & Huang, Renke & Shen, Yu & He, Guangyu, 2025. "Multi-time scale scheduling framework for multi-energy system considering demand response: A self-approaching optimal approach," Energy, Elsevier, vol. 330(C).
    2. Yang, Yilong & Fan, Shuai & Xiao, Jucheng & Huang, Renke & He, Guangyu, 2025. "Aggregation and packaging of distributed energy resources: A time-coupled flexibility-demand-oriented method," Energy, Elsevier, vol. 334(C).
    3. Lefeng Cheng & Pengrong Huang & Mengya Zhang & Kun Wang & Kuozhen Zhang & Tao Zou & Wentian Lu, 2025. "Optimizing Virtual Power Plants Cooperation via Evolutionary Game Theory: The Role of Reward–Punishment Mechanisms," Mathematics, MDPI, vol. 13(15), pages 1-86, July.
    4. Ge, Xinxin & Wang, Ge & Wang, Fei, 2025. "Optimal day-ahead dispatching strategy for VPP using dynamic grouping DER aggregation method based on improved virtual battery model," Energy, Elsevier, vol. 326(C).

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