Modeling market trading strategies of the intermediary entity for microgrids: A reinforcement learning-based approach.

• A new decision-making framework is proposed to solve the energy management problem of multiple MGs. • The proposed method formulates participation of MGs in the energy market through the intermediary entity. • Energy price forecasting is conducted using the long short-term memory (LSTM) recurrent neural networks. • Power exchange of the MGs with the main grid is optimized by solving the energy management problem. • Monte Carlo reinforcement learning technique is employed to optimize real-time pricing decisions. Participation of a large number of microgrids (MGs) in the energy markets faces several challenges. To address these challenges, MGs can be aggregated by an Intermediary entity (IE). In this paper, a new decision-making framework is proposed to solve the energy management problem of multiple MGs and their participation in the energy market through the IE. This framework has three main functions. First, energy price forecasting is conducted using the long short-term memory (LSTM) recurrent neural networks method. Then, the power exchange of the MGs with the main grid is optimized by solving their energy management problem. In this stage, the integrated power exchanges of the MGs with the grid are determined based on the predicted prices. In the final function, the Monte Carlo reinforcement learning technique is employed to optimize real-time pricing decisions and identify potential obstacles that may affect proposed bid prices. This approach addresses energy management challenges and enables profitable energy trading in multiple MGs within energy markets, with confirmation supported by simulation results. The results show a maximum increment of 4.55% in the profit of the IE when purchasing energy and a 3.79% maximum increment when selling energy in the real-time market compared to day-ahead decisions, respectively. [ABSTRACT FROM AUTHOR]