# Optimal capacity configuration of wind-photovoltaic-storage hybrid systems based on improved chaotic evolution optimization algorithm

## TL;DR

This paper introduces a new optimization algorithm to efficiently plan wind, solar, and storage energy systems for better cost and performance.

## Contribution

The novel ICEO algorithm combines Gaussian mutation, Lévy flight, and adaptive local search for improved optimization of hybrid energy systems.

## Key findings

- ICEO outperforms existing meta-heuristics in solving complex WPS optimization problems.
- The algorithm improves cost-effectiveness and robustness in capacity planning for renewable energy systems.
- Simulation results validate ICEO's effectiveness on standard benchmarks and real-world WPS cases.

## Abstract

This study addresses the optimal capacity configuration of wind–photovoltaic–storage (WPS) systems under complex nonlinear constraints and economic requirements in grids with a high share of renewable energy. A multi-energy collaborative capacity planning model is developed, together with an energy management formulation that captures the coupling among wind, PV, and storage. To solve the resulting constrained optimization problem, an improved chaotic evolution optimization algorithm (ICEO) is proposed by embedding a self-learning perturbation strategy and an adaptive local search mechanism into the chaotic evolution framework. Specifically, Gaussian mutation and Lévy flight are combined to generate cooperative perturbations around high-quality solutions, while a stagnation-triggered local search refines solutions when the population evolution slows down. Simulation results on standard benchmark functions and a practical WPS case study demonstrate that ICEO achieves higher solution quality and robustness than several state-of-the-art meta-heuristics, thereby improving cost-effectiveness for WPS capacity planning.