Abstract:

To tackle the load fluctuation issues in a park caused by the intermittency of photovoltaic(PV) system power generation and the randomness of electric vehicle(EV) loads, a coordinated optimization scheduling model based on the particle swarm optimization-differential evolution(PSO-DE) algorithm is proposed. The model adopts a joint objective optimization function aimed at minimizing the park’s operating costs while maximizing the benefits for EV users. By integrating the global fast convergence characteristics of the particle swarm optimization(PSO) algorithm with the local search capability and population diversity maintenance mechanism of the differential evolution(DE) algorithm, the proposed approach effectively enhances convergence speed by approximately 30%. Using the typical daily load data for a summer day in a park in Shanghai as a simulation case study, the results demonstrate that the proposed coordinated optimization model effectively reduces the park’s operating costs, increases EV user benefits by 7.7%, and improves both economic performance and operational stability of the system. The simulation outcomes validate the accuracy and effectiveness of the model.

Key words: load fluctuation, coordinated optimization, global search, population diversity