Wind-Solar-Hydro Storage Joint Operation Strategy Based on Improved Immune Particle Swarm Optimization Algorithm
Year: 2026
Type: conference-paper
Abstract: Based on the resource characteristics of Hubei Province, this study explores the application and impact of scheduling optimization control strategies for new power systems incorporating hydro, wind, and solar energy. A dynamic immune particle swarm optimization algorithm incorporating dynamically adjusted learning coefficients and inertia weights is proposed, aiming to enhance the economic benefits of combined power generation systems and mitigate the impact of new energy grid integration on grid stability. This paper introduces a dynamic immune particle swarm optimization algorithm incorporating dynamically adjusted learning coefficients and inertia weights to achieve scheduling optimization for new power systems. Firstly, a brief introduction is given to the definitions and algorithm processes of the basic particle swarm algorithm and immune particle swarm algorithm. Secondly, addressing the issues faced by both algorithms, such as the tendency to converge to suboptimal solutions and slow convergence, a dynamic immune particle swarm optimization algorithm incorporating dynamically adjusted learning coefficients and inertia weights is proposed. Finally, to address the shortcomings of wind and solar curtailment during grid integration, the concept of a penalty function is introduced and combined with CIPSO to propose a new algorithm process.
Author: Wen Li
Institution: Wuhan Donghu University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204969366