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Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing

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  • Wang, Dongxue
  • Fan, Ruguo
  • Yang, Peiwen
  • Du, Kang
  • Xu, Xiaoxia
  • Chen, Rongkai

Abstract

With the rapid development of shared energy storage (SES) and distributed energy resources, the local energy market (LEM) has become a pivotal platform for the interaction between microgrids and distributed energy. In LEM, the challenge of formulating pricing strategies that effectively align with wholesale market prices, and coordinating SES leasing with energy trading, is crucial for the healthy development of the market. This study introduces a real-time floating pricing (FRTP) strategy based on day-ahead market (DAM) prices and establishes a multi-objective, two-level Stackelberg game model between micro-grid operator (MGO) and the photovoltaic prosumer aggregator (PVPA). The model, constrained by a price cap mechanism, employs distributed algorithms to optimize coordinated pricing strategies for trading and SES leasing. Through simulations of various trading modes, this study thoroughly examines the impact of market signals on MGO's pricing strategies within the LEM. Our findings reveal that: (1) Under the two-level trading model, both MGO and PVPA can maximize their economic benefits. (2) The FRTP strategy increases MGO's revenue by 2.86%, 2.35% and 1.55% compared to fixed pricing, time-of-use (TOU) and hybrid pricing strategies, respectively, while also reducing carbon emissions by 3.68%, 1.35%, and 0.8%. (3) Setting price caps plays a crucial role in managing DAM price volatility. This study provides theoretical insights that enhance the market performance of MGO and PVPA, promoting the market-based integration of distributed energy resources crucial for their effective utilization.

Suggested Citation

  • Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
    • Handle: RePEc:eee:appene:v:368:y:2024:i:c:s0306261924007955
    DOI: 10.1016/j.apenergy.2024.123412
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    1. Zhengdong Wan & Yan Huang & Liangzheng Wu & Chengwei Liu, 2024. "ADPA Optimization for Real-Time Energy Management Using Deep Learning," Energies, MDPI, vol. 17(19), pages 1-13, September.

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