Stackelberg Game-Based Optimal Scheduling Strategy for Multi-Microgrid Systems with Electricity-Heat Shared Energy Storage

To address the challenges of the complexity of electricity-heat coupling, the pollution of carbon emissions, the security of privacy information, and the uncertainty of renewable energy generations (REGs), this paper proposes a novel multi-microgrid electricity-heat shared energy storage system framework, considering that the integrated energy storage operator (IESO) is equipped with electric boiler and two types of energy storage (i.e., electrical energy storage and heat energy storage) devices, and each microgrid (MG) is equipped with combined heat and power system that coupled carbon capture system and power-to-gas device. Latin hypercube sampling is used to deal with the uncertainty of REGs. A distributed bi-level iterative algorithm based on Stackelberg game theory is designed, which effectively protects the users' privacy information, and finally a Stackelberg equilibrium strategy is obtained, where the IESO and each MG minimize its operating cost. Simulation results show that the proposed optimal scheduling strategy can effectively reduce the operating cost and carbon emissions of each MG, and increase the benefits of the IESO.