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<p>In the context of renewable energy integration, shared energy storage (SES) offers a promising solution for improving the operational flexibility and economic performance of renewable energy stations (RESs). This paper proposes an improved nucleolus method-based rolling optimization operation method for RESs and SES to address operational uncertainties and ensure fair revenue distribution among RESs. The proposed method comprises a profit maximization model for the cooperative alliance that dynamically adjusts the RESs’ bidding strategies in both day-ahead and real-time energy markets using a receding horizon control approach, as well as an improved nucleolus-based revenue allocation model enhanced with constraint generation techniques to ensure fair, stable and efficient profit sharing among large-scale alliance members. This methodology effectively addresses uncertainties in renewable energy output, market prices and forecast deviations, while maintaining system stability and fairness in cost-sharing. Case study results show that the proposed method improves overall alliance revenue, reduces deviation penalty costs, and significantly decreases computation time, thereby underscoring its scalability and practical feasibility for large-scale RES alliances.</p>

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Improved nucleolus method-based rolling optimization operation for renewable energy stations and shared energy storage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月5日

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  • Gang Li,
  • Bingyu Sang,
  • Wei Tang,
  • Kecheng Li,
  • Bin Xu,
  • Zhihao Yang,
  • Wei Ma,
  • Yibin Tao

摘要

In the context of renewable energy integration, shared energy storage (SES) offers a promising solution for improving the operational flexibility and economic performance of renewable energy stations (RESs). This paper proposes an improved nucleolus method-based rolling optimization operation method for RESs and SES to address operational uncertainties and ensure fair revenue distribution among RESs. The proposed method comprises a profit maximization model for the cooperative alliance that dynamically adjusts the RESs’ bidding strategies in both day-ahead and real-time energy markets using a receding horizon control approach, as well as an improved nucleolus-based revenue allocation model enhanced with constraint generation techniques to ensure fair, stable and efficient profit sharing among large-scale alliance members. This methodology effectively addresses uncertainties in renewable energy output, market prices and forecast deviations, while maintaining system stability and fairness in cost-sharing. Case study results show that the proposed method improves overall alliance revenue, reduces deviation penalty costs, and significantly decreases computation time, thereby underscoring its scalability and practical feasibility for large-scale RES alliances.