Optimal Scheduling of Photovoltaic–Storage–Charging Integrated Stations Based on a PriceSOC-Guided Initialization Particle Swarm Optimization Algorithm

Against the backdrop of the “dual-carbon” strategy (carbon peaking and carbon neutrality), countries worldwide are committed to advancing the application of new energy in the transportation sector. This has spurred the rapid development of electric vehicles (EVs) and led to higher requirements for the research and construction of charging infrastructure. To address the challenges of high daily power purchase costs and severe grid-connected power fluctuations in the daily scheduling of PV–storage–charging integrated stations, as well as the limitations of conventional particle swarm optimization (PSO) with random or chaotic initialization—including insufficient engineering prior knowledge of station time-of-use (TOU) electricity prices and energy storage state of charge (SOC), numerous inferior solutions in the initial population, and high susceptibility to premature convergence—this paper develops a dual-objective optimal scheduling model that balances daily power purchase cost and grid-connected power fluctuation. The model integrates PV output, EV charging loads, energy storage charge–discharge schedules, and multiple categories of operational constraints. Grounded in the economic operation principle of “valley-period charging and peak-period discharging”, an improved PSO algorithm with electricity PriceSOC joint guided initialization (PriceSOC-PSO) is proposed. High-quality initial particles are generated by setting segmented SOC targets, introducing random perturbations, and implementing closed-loop correction of the energy storage schedule, while hybrid random particles are incorporated into the population to preserve diversity. Multiple simulation scenarios, including the no-energy-storage case, standard PSO, chaotic-initialized PSO, the proposed PriceSOC-PSO, Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and the Sparrow Search Algorithm (SSA), are established to carry out objective weight sensitivity analysis and cross-algorithm comparative analysis. The results demonstrate that, compared with the no-energy-storage scenario, the proposed strategy reduces the daily power purchase cost and grid-connected power fluctuation by 11.7% and 74.9% respectively under the weight configuration (ω1=0.3, ω2=0.7). When the weight configuration is adjusted to (ω1=0.7, ω2=0.3), the two indicators are decreased by 14.8% and 62.6% respectively.