Open Access

ARTICLE

Research on Wind-Solar Complementarity Rate Analysis and Capacity Configuration Based on COPULA-IMOPSO

Caifeng Wen¹, Feifei Xue^1,*, Hongliang Hao², Edwin E. Nyakilla², Ning Yang^1,*, Yongsheng Wang³, Yuwen Zhang²

1 School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot, 010080, China
2 Department of New Energy Storage, Peking University Ordos Research Institute of Energy, Ordos, 017010, China
3 School of Data Science and Application, Inner Mongolia University of Technology, Hohhot, 010080, China

* Corresponding Authors: Feifei Xue. Email: ; Ning Yang. Email:

(This article belongs to the Special Issue: Advances in Renewable Energy Systems: Integrating Machine Learning for Enhanced Efficiency and Optimization)

Energy Engineering 2025, 122(4), 1511-1529. https://doi.org/10.32604/ee.2025.060810

Received 10 November 2024; Accepted 28 February 2025; Issue published 31 March 2025

Abstract

This paper presents a new capacity planning method that utilizes the complementary characteristics of wind and solar power output. It addresses the limitations of relying on a single metric for a comprehensive assessment of complementarity. To enable more accurate predictions of the optimal wind-solar ratio, a comprehensive complementarity rate is proposed, which allows for the optimization of wind-solar capacity based on this measure. Initially, the Clayton Copula function is employed to create a joint probability distribution model for wind and solar power, enabling the calculation of the comprehensive complementarity rate. Following this, a joint planning model is developed to enhance the system’s economy and reliability. The goal is to minimize total costs, load deficit rates, and curtailment rates by applying an Improved Multi-Objective Particle Swarm Optimization algorithm (IMOPSO). Results show that when the proportion of wind power reaches 70%, the comprehensive complementarity rate is optimized. This optimization leads to a 14.83% reduction in total costs and a 9.27% decrease in curtailment rates. Compared to existing studies, this paper offers a multidimensional analysis of the relationship between the comprehensive complementarity rate and the optimal wind-solar ratio, thereby improving predictive accuracy and providing a valuable reference for research on the correlation between wind and solar power.

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