A Cooperative Master-Slave Game-Based Optimization Method for Multi-Park Integrated Energy System

With the advancement of new energy technologies and the enhanced capacity for renewable energy integration, studying the collaborative optimization of multi-park integrated energy systems has become increasingly important. To address the cooperative optimization challenges in Park-level Integrated Energy Systems (PIES), a cooperative optimization framework for multi-park integrated energy system is proposed. First, a master-slave cooperative game model for a single park is integrated into the alliance benefit maximization problem of the park-level integrated energy system, and an interactive power decoupling variable is introduced to enable distributed solutions. Next, to address the alliance allocation problem at the system level, an exponential function is employed to quantify each park's contribution to energy interactions. The alliance benefits are then allocated based on these contributions, thereby incentivizing park operators to actively participate in the cooperation. Finally, to tackle the issue of slow convergence, an enhanced Alternating Direction Method of Multipliers (ADMM) algorithm is proposed, incorporating dynamic penalty factors and Lagrange multiplier updates to reduce iteration counts and accelerate convergence. Simulation results demonstrate that the proposed three-layer game model and benefit allocation method effectively distribute benefits based on PIES power contributions, enhancing the operational efficiency of both PIES and Prosumer(PSU).