基于改进粒子群算法的综合能源优化调度策略OA

To promote coordinated optimization of regional integrated energy systems and maximize the economic benefits of participants in energy trading markets,this paper proposes a multi-objective optimal scheduling strategy that incorporates integrated energy demand response.By analyzing the structure of regional integrated energy systems,a bi-level Stackelberg game model is constructed,in which the operator acts as the leader,while suppliers and clustered users respond as followers.To address the nonlinear challenges in energy pricing and trading within the game model,an improved particle swarm optimization(PSO)algorithm is developed.Specifically,a chaotic initialization strategy and a second-order oscillation mechanism are introduced to enhance global search capability.Additionally,a segmented nonlinear inertia weight is adopted to reduce the risk of falling into local optima.The algorithm iteratively determines the optimal sequence of energy prices and trading quantities across different time periods.Simulation results demonstrate that the proposed optimization strategy effectively balances peak and valley load differences for users and reduces energy purchasing costs.