Open Access

ARTICLE

Economic-Environmental Optimal Dispatch of Hybrid Microgrids Based on an Improved Electric Eel Foraging Optimization Algorithm

Tianhao Wang¹, Hongwei Duan¹, Che Zheng², Lifeng Jia³, Tian Dong⁴, Linzhou Jia⁵, Zehua Wang⁶, Shanshan Guan^1,*
1 College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
2 Electric Power Department, Jilin Yuanlin Safety Technology Co., Ltd., Changchun, China
3 Baishan Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Baishan, China
4 Supervision Department, State Grid Jilin Electric Power Co., Ltd., Changchun, China
5 Qian’an Wind Power Branch, State Grid Jilin New Energy Group Co., Ltd., Changchun, China
6 Tonghua Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Tonghua, China
* Corresponding Author: Shanshan Guan. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.089355

Received 18 July 2026; Accepted 08 September 2026; Published online 20 September 2026

Abstract

Against the backdrop of carbon peaking and carbon neutrality targets, the increasing penetration of renewable energy presents new challenges to the economic and low-carbon operation of microgrids. This study develops an economic-environmental optimal dispatch model for a grid-connected microgrid comprising wind turbines (WT), photovoltaics (PV), microturbines (MT), and a storage battery (SB). To improve the optimization performance of electric eel foraging optimization (EEFO), four strategies are introduced, namely elite opposition-based learning initialization, an adaptive Cauchy-differential operator, a tournament selection mechanism, and a horizontal crossover strategy, resulting in an improved algorithm termed IEEFO. The algorithm is evaluated using all ten CEC2019 benchmark functions, and the results demonstrate its competitive optimization accuracy and robustness compared with the seven comparison algorithms. IEEFO is applied to microgrid optimal dispatch under three power-supply configurations in winter and summer: WT and PV are both available, WT is unavailable, and PV is unavailable. IEEFO obtains the lowest mean total cost among the eight algorithms under all six operating conditions. Under the WT–PV configuration, its mean total costs are 1366.7310 CNY in winter and 1380.8129 CNY in summer. Relative to this configuration, the mean total costs without WT and without PV increase by 15.04% and 4.12%, respectively, in winter, and by 12.99% and 14.78%, respectively, in summer. These results demonstrate the effectiveness of IEEFO for economic-environmental microgrid dispatch under different seasonal and power-supply conditions.

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Keywords

Renewable energy; microgrids; electric eel foraging optimization algorithm; optimal scheduling

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