Research on Bi-directional Cooperative Optimal Scheduling of Novel Power System Source and Load Based on Particle Swarm Optimization Algorithm

With the transformation of energy structure and the increase of power demand, novel power system faces the challenge of fluctuation and uncertainty of renewable energy. To enhance the efficiency, stability, and cost-effectiveness of the system, this study introduces the Particle Swarm Optimization (PSO) algorithm. The goal is to achieve optimal bi-directional cooperative scheduling of both the source and load. In this paper, IEEE33-node power grid is selected as the experimental framework, and a power system model including distributed generation and various loads is constructed. The objective of the optimization is to reduce the overall operating cost of the system, which includes power purchase cost, distributed power supply operating cost, load reduction cost and load transfer compensation cost. PSO algorithm uses mixed coding method to deal with decision variables such as distributed power output, load shedding and load transfer, and designs reasonable fitness function and updating strategy. The simulation results demonstrate that the optimized scheduling scheme significantly smooths the output curve of distributed power sources, effectively lowers the system’s total operating costs, and ensures stable operation and reliable power supply. Compared to the Genetic Algorithm (GA) and Ant Colony Algorithm (ACO), the PSO algorithm can identify the near-optimal solution in fewer iterations, exhibiting strong global search capabilities and faster convergence. This study not only verifies the effectiveness and superiority of PSO algorithm in the bi-directional cooperative optimal scheduling of source and load, but also provides theoretical support and technical reference for the economic operation and stable power supply of novel power system.