A multi-objective optimization algorithm-based capacity scheduling method for photovoltaic power hybrid energy storage systems
Year: 2024
Type: article
Abstract: In this study, the combination of crossover algorithm and particle swarm optimization—crossover algorithm-particle swarm optimization (CS-PSO) algorithm—to optimize photovoltaic hybrid energy storage scheduling, improving global search and convergence speed, is discussed. The new method reduces energy storage costs and energy losses, ensures supply–demand balance and interaction power constraints, and maintains population diversity through cross-search. The CS-PSO algorithm introduces battery state of charge optimization for energy storage scheduling, improving global search and convergence speed, and obtaining accurate multi-objective scheduling results. The experiment shows that the optimal configuration for photovoltaic energy storage is 10 045 batteries + 687 244 supercapacitors, with a cost of 3.452 × 105 yuan and an energy loss of less than 5%. CS-PSO has similar costs but lower losses and faster convergence compared to traditional methods.
Source: AIP Advances
DOI: 10.1063/5.0234096
Authors: Shuaijie Wang, Xin Guan, Shu Liu
Language: English
Subfield: Energy Engineering and Power Technology
Field: Energy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,350
OpenAlex ID: W4405017087