Application of Improved Particle Swarm Optimization Algorithm in Distributed Energy Optimization Configuration in New Power Systems

Effective optimization strategies are needed for the switch to new power systems, to improve the distributed energy resources (DERs) configuration and ensure a reliable and sustainable energy supply. However, scalability and computing efficiency issues frequently affect traditional optimization techniques when used on large-scale and intricate energy systems. It seeks to overcome these issues by utilizing an improved particle swarm optimization (IPSO) method to optimize DERs in new power systems. To improve on standard particle swarm optimization (PSO), the proposed IPSO incorporates dynamic inertia weight modification, and a hybrid velocity updating technique to increase convergence speed while avoiding premature trapping in local optima. The strategy is tested using a simulated power grid model that includes a wide range of DERs, such as solar, wind and storage devices. The findings show that the IPSO is superior to the standard PSO in terms of optimization efficiency, with better design of energy resources and lower operational costs. The IPSO using MATLAB software achieves an energy consumption value of 380,000 kWh, a cost efficiency of 97.5%, an improved optimization efficiency of 96.3%, a power loss of 250 W, and a speed deviation of 2 s. Specifically, the IPSO reduces energy waste and improves cost efficiency as compared to conventional approaches. Therefore, the proposed IPSO algorithm presents a potential solution to the issues of optimizing DER designs in new power systems. Its ability to balance several objectives makes it a useful tool for future power system design.