Open Access

ARTICLE

CCHP-Type Micro-Grid Scheduling Optimization Based on Improved Multi-Objective Grey Wolf Optimizer

Yu Zhang^*, Sheng Wang, Fanming Zeng, Yijie Lin

College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China

* Corresponding Author: Yu Zhang. Email:

(This article belongs to the Special Issue: Emerging Technologies for Future Smart Grids)

Energy Engineering 2025, 122(3), 1137-1151. https://doi.org/10.32604/ee.2025.060945

Received 13 November 2024; Accepted 20 January 2025; Issue published 07 March 2025

Abstract

With the development of renewable energy technologies such as photovoltaics and wind power, it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement. To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy, while simultaneously enhancing user satisfaction on the demand side, this paper introduces an improved multi-objective Grey Wolf Optimizer based on Cauchy variation. The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima. At the same time, adopting multiple energy storage methods to improve the consumption rate of renewable energy. Subsequently, under different energy balance orders, the multi-objective particle swarm algorithm, multi-objective grey wolf optimizer, and Cauchy’s variant of the improved multi-objective grey wolf optimizer are used for example simulation, solving the Pareto solution set of the model and comparing. The analysis of the results reveals that, compared to the original optimizer, the improved optimizer decreases the daily cost by approximately 100 yuan, and reduces the energy abandonment rate to zero. Meanwhile, it enhances user satisfaction and ensures the stable operation of the micro-grid.

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