Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

• Published in: IET generation, transmission & distribution transmission & distribution, 2024-08, Vol.18 (16), p.2705-2718
• Main Authors: Helmi, Hamid, Abedinzadeh, Taher, Beiza, Jamal, Shahmohammadi, Sima, Daghigh, Ali
• Format: Article
• Language: English
• Subjects: demand side management; distribution networks; distribution planning and operation; energy resources; genetic algorithms; micro grids; power markets; reliability; uncertain systems
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/gtd2.13235

This study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting. This study explores the optimal operation of microgrids as a bi‐level optimization problem where the upper level maximizes the profits of the microgrid owner and distribution companies, while the lower level ensures load balance, distributed generation, and load curtailment. To handle the uncertainty relating to the renewable energy resources, the model proposed in this study employs shiftable load management and curtailable load. The numerical studies demonstrate that by application of the model introduced in this article, more optimal solutions are acquired for renewable energy management in active distribution systems.