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An optimal bidding and scheduling method for load service entities considering demand response uncertainty

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  • Han, Rushuai
  • Hu, Qinran
  • Cui, Hantao
  • Chen, Tao
  • Quan, Xiangjun
  • Wu, Zaijun

Abstract

With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

Suggested Citation

  • Han, Rushuai & Hu, Qinran & Cui, Hantao & Chen, Tao & Quan, Xiangjun & Wu, Zaijun, 2022. "An optimal bidding and scheduling method for load service entities considering demand response uncertainty," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014246
    DOI: 10.1016/j.apenergy.2022.120167
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    References listed on IDEAS

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    Cited by:

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    2. He, Ye & Wu, Hongbin & Wu, Andrew Y. & Li, Peng & Ding, Ming, 2024. "Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory," Renewable Energy, Elsevier, vol. 224(C).
    3. Sicheng Wang & Weiqing Sun, 2023. "Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules," Sustainability, MDPI, vol. 15(21), pages 1-19, October.

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