Bi-level Robust Optimal Energy Management of a Community Microgrid via Stackelberg Game

To tackle the challenge of energy management in community microgrids, this paper proposes a novel bi-level robust optimal scheduling model based on the Stackelberg game. At the leader level, the community microgrids operator (CMO), equipped with a battery energy storage system (BESS), is responsible for devising an energy management strategy that accounts for market price uncertainties. The energy scheduling of CMO is formulated as an adjustable robust optimization (RO) model. At the follower level, residential prosumers, equipped with renewable energy sources (RESs), both controllable and uncontrollable loads, and electric vehicles (EVs), respond to the internal pricing signals to achieve economical dispatch while considering the uncertainties of intermittent RESs and uncontrollable loads. The economical dispatch for prosumers is established as a two-stage RO model. Furthermore, the CMO develops internal pricing mechanisms to promote local transactions and ensure fairness by aligning with the supply and demand dynamics. The proposed bi-level robust Stackelberg game model is linearized and solved through an iterative process, utilizing gurobi's barrier method alongside a modified column-and-constraint generation (C&CG) algorithm. Simulation results verify the effectiveness of our framework in shifting peak loads for the main grid and demonstrate the strengths of the proposed model in handling multiple uncertainties.