Optimal scheduling strategy of grid-connected microgrid with ladder-type carbon trading based on Stackelberg game

Aiming at the problems of promoting new energy consumption, reducing carbon emissions, load fluctuations, and multi-agent conflict of interests in the networked microgrid system, this article proposes a microgrid optimization operation strategy based on demand response and reward-penalty ladder-type carbon trading mechanism. First, in order to determine the electricity sales price of the system, an optimal scheduling model for microgrid operators is established, including gas cost, electricity profit for users, and surplus power supply network profit. Second, a demand response strategy on electricity price and low-carbon compensation incentives is proposed on the user side. The transaction model is embedded between microgrid operators and users into the master-slave game framework, and a multi-slave game collaborative optimization model is established with microgrid operators as leaders and users as followers. The existence and uniqueness of Stackelberg game are proved, and the differential evolution algorithm and CPLEX solver are used to solve the proposed model. Finally, an example of a microgrid system including three community users is provided to show the effectiveness of the proposed model and strategy.

Automated summary

This article proposes a microgrid optimization operation strategy based on demand response and reward-penalty ladder-type carbon trading mechanism to address the problems of promoting new energy consumption, reducing carbon emissions, load fluctuations, and multi-agent conflict of interests in the networked microgrid system. The strategy includes an optimal scheduling model for microgrid operators and a demand response strategy on electricity price and low-carbon compensation incentives for users. The effectiveness of the proposed model and strategy is demonstrated through a case study.