Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Abstract

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Keywords

• wind–solar coupling
• hybrid hydrogen production
• fluctuation characteristics
• improved algorithm