TY  - EJOU
AU  - Wang, Tianhao 
AU  - Duan, Hongwei 
AU  - Zheng, Che 
AU  - Jia, Lifeng 
AU  - Dong, Tian 
AU  - Jia, Linzhou 
AU  - Wang, Zehua 
AU  - Guan, Shanshan 

TI  - Economic-Environmental Optimal Dispatch of Hybrid Microgrids Based on an Improved Electric Eel Foraging Optimization Algorithm
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - 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.
KW  - Renewable energy; microgrids; electric eel foraging optimization algorithm; optimal scheduling

DO  - 10.32604/ee.2026.089355