Optimization Scheduling of Microgrid Systems and Enhance-ment of Green Economic Benefits through Particle Swarm Al-gorithm

With the transformation of energy structure and the promotion of carbon peak target, microgrid, a regional energy system integrating various renewable energy sources, has become increasingly important in promoting green and low-carbon development. However, microgrid scheduling faces many challenges, including heterogeneity of energy sources, severe load fluctuation and complex operation constraints. In order to solve these problems, an improved particle swarm optimization (IPSO) algorithm is proposed, which combines the bacterial foraging mechanism to enhance the global search ability and improve the convergence stability in complex scheduling environment. IPSO algorithm is based on the classical particle swarm optimization (PSO) framework, and introduces the disturbance component based on chemotaxis and the dynamic inertia weight strategy. These improvements improve the ability of the algorithm to jump out of the local optimal solution and adapt to the dynamic changes of the system. In this study, a multi-objective optimization model is established for a typical microgrid system, taking into account economic costs, carbon emissions, energy storage life and dynamic electricity prices. The proposed scheduling framework can ensure power balance, meet output constraints and maintain operational safety. Simulation experiments under typical summer and winter load conditions in a certain area of Zhejiang Province show that IPSO algorithm is significantly superior to standard PSO, genetic algorithm (GA) and bee colony optimization (BCO) in terms of cost efficiency, carbon dioxide emission reduction and system stability. Specifically, in the summer working conditions, the IPSO algorithm reduces the operating cost by 16.7%, carbon emissions by 18.4% and energy storage cycles by 37.5%. In winter, the algorithm reduces the cost by 14.5% and the carbon emission by 15.1%. Ablation experiments confirmed the synergistic effect of chemotaxis-based disturbance and dynamic weighting mechanism. In addition, in 30 independent simulation runs, IPSO algorithm always shows fast convergence, low fluctuation of optimal results and no convergence failure. Compared with traditional optimization methods (such as linear programming (LP) and sequential quadratic programming (SQP)), IPSO shows better scalability and robustness under nonlinear constraints. Generally speaking, this study expands the theoretical application of PSO in green energy dispatching, and provides a practical and extensible way for intelligent low-carbon dispatching and valuable reference for engineering application and policy formulation.