Day-ahead optimal peer-to-peer energy trading strategy for multi-microgrids based on Nash bargaining game with data-driven chance constraints

This paper proposes an optimization model to obtain the day-ahead optimal peer-to-peer (P2P) trading strategy for multi-microgrids (MMG). Firstly, a joint economic energy and reserve scheduling model of a microgrid (MG) is established while considering specific network constraints. Then, to mitigate the impact of renewable energy and load forecasting errors, chance constraints are introduced for the reserve capacity and buses voltage limitations of an individual microgrid. Additionally, a versatile distribution method is adopted to capture the probability distribution of uncertain variables in a data-driven manner, avoiding any prior assumptions. Finally, Nash bargaining theory is employed to deal with the P2P energy trading problem among MMG. The problem is equivalently transformed into two sequential subproblems for solving. Moreover, the alternating direction method of multipliers algorithm is used to solve the subproblems in a distributed manner for privacy concerns. The proposed model not only enables effective P2P energy trading for MMG, but also ensures compliance with the internal network constraints of each MG. Furthermore, the scheduling strategy exhibits robustness in handling forecast errors related to renewable energy and load. In case studies, MMG containing three interconnected MGs is constructed based on the IEEE-123 bus distribution network, and simulation results show that the cost of the MMG is reduced by 9.92 % while all the MGs can benefit from P2P trading. In addition, the riskaverse scheduling results of energy and reserve are obtained, and the conservativeness can be controlled by changing the confidence level, which verifies the effectiveness of the proposed model.

Automated summary

This paper proposes an optimization model to obtain the day-ahead optimal peer-to-peer trading strategy for multi-microgrids. The model considers network constraints and introduces chance constraints to mitigate the impact of renewable energy and load forecasting errors. It adopts a data-driven method to capture the probability distribution of uncertain variables and uses Nash bargaining theory and a distributed solving algorithm to handle the P2P energy trading problem.