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Stochastic bidding for VPPs enabled ancillary services: A case study

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  • Wang, Zheng
  • Li, Chaojie
  • Zhou, Xiaojun
  • Xie, Renyou
  • Li, Xiangyu
  • Dong, Zhaoyang

Abstract

Strategic bidding which aims to optimally harvest the price difference in the wholesale electricity market can efficiently allocate VPPs’ aggregated resources to provide large flexibility for energy and frequency regulation (FR), aiding in the real-time rebalancing of supply and demand. However, the uncertainty of renewable energy results in substantial imbalances between supply and demand, which causes high randomness of system frequency deviation and significant price fluctuations in the electricity market, making bidding challenging. To tackle these issues, a risk-averse optimal bidding strategy is proposed for VPPs to participate in both energy and FR markets. Moreover, a payment recovery mechanism is designed to recover the cost of FR undersupply. Specifically, each VPP submits bids to maximize profit, while the market operator clears the market and penalizes undersupply through the payment recovery mechanism. The existence of Nash equilibrium is proved, and a distributed best response algorithm is implemented to calculate the risk-averse optimal bidding strategy, taking into account Denial-of-Service attacks. The mean-convergence and variance-convergence of our proposed algorithm are derived. A case study of Australian National Electricity Market (NEM) validates the effectiveness of the proposed method in reducing overbidding risk, strengthening FR service reliability and improving profits for VPPs.

Suggested Citation

  • Wang, Zheng & Li, Chaojie & Zhou, Xiaojun & Xie, Renyou & Li, Xiangyu & Dong, Zhaoyang, 2023. "Stochastic bidding for VPPs enabled ancillary services: A case study," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923012825
    DOI: 10.1016/j.apenergy.2023.121918
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Yuzheng & Dong, Jun & Huang, Hexiang, 2024. "Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm," Energy, Elsevier, vol. 306(C).

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