Multi-Objective Dynamic Economic Emission Dispatch with Wind-Photovoltaic-Biomass-Electric Vehicles Interaction System Using Self-Adaptive MOEA/D

Baihao Qiao, Jinglong Ye, Hejuan Hu and Pengwei Wen ()
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Baihao Qiao: School of Automation and Electrical Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
Jinglong Ye: School of Information and Communication Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
Hejuan Hu: School of Automation and Electrical Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
Pengwei Wen: School of Information and Communication Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China

Sustainability, 2025, vol. 17, issue 22, 1-25

Abstract: The rapid use of renewables like wind power (WP) and photovoltaic (PV) power is essential for a sustainable energy future, yet their volatility poses a threat to grid stability. Electric vehicles (EVs) contribute to the solution by providing storage, while biomass energy (BE) ensures a reliable and sustainable power supply, solidifying its critical role in the stable operation and sustainable development of the power system. Therefore, a dynamic economic emission dispatch (DEED) model based on WP–PV–BE–EVs (DEED WPBEV ) is proposed. The DEED WPBEV model is designed to simultaneously minimize operating costs and environmental emissions. The model formulation incorporates several practical constraints, such as those related to power balance, the travel needs of EV owners, and spinning reserve. To obtain a satisfactory dispatch solution, an adaptive improved multi-objective evolutionary algorithm based on decomposition with differential evolution (IMOEA/D-DE) is further proposed. In IMOEA/D-DE, the initialization of the population is achieved through an iterative chaotic map with infinite collapses, and the differential evolution mutation operator is adaptively adjusted. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on the ten-units system. The experimental results show that the proposed model and algorithm can effectively mitigate renewable energy uncertainty, reduce system costs, and lessen environmental impact.

Keywords: dynamic economic emission dispatch; electric vehicles; biomass energy; multi-objective optimization; renewable energy; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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