Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach

In order to realize the effective use of energy and promote the full consumption of renewable energy, the microgrid integrated by distributed resources has the potential of resource sharing as independent stakeholders. While, the traditional multi-microgrid interconnection ignores the information privacy of each microgrid, moreover, the cooperation strategy often cannot achieve a fair and reasonable distribution of benefits. Based on this, this paper firstly establishes a microgrid model for the coordination of three energy forms of electricity, heat, and gas. Secondly, considering the actual situation involving uncertainties on both sides of the source and load, robust interval optimization is used to achieve better robustness. Then, combined with the increasingly important low-carbon operation goal, a combined heat and power (CHP) model integrating power to gas (P2G) and carbon capture systems (CCS) is constructed, and the goal of emission reduction is achieved through the carbon trading operation mechanism. Finally, based on the Nash bargaining theory, a multi-microgrid Peer-toPeer(P2P) low-carbon economic operation model is established, and the alternating direction method of multipliers (ADMM) with good convergence and privacy is used to decomposed the original problem into two subproblems to solve.

Automated summary

In this paper, a microgrid model is established for coordinating electricity, heat, and gas. Robust interval optimization is used to deal with uncertainties in the source and load. A combined heat and power (CHP) model integrating power to gas (P2G) and carbon capture systems (CCS) is constructed to achieve emissions reduction through carbon trading. A multi-microgrid Peer-to-Peer (P2P) low-carbon economic operation model is established based on the Nash bargaining theory and solved using the alternating direction method of multipliers (ADMM) with good convergence and privacy.