Optimizing Microgrid Load Fluctuations through Dynamic Pricing and Electric Vehicle Flexibility: A Comparative Analysis

Mahdi A. Mahdi, Ahmed N. Abdalla (), Lei Liu, Rendong Ji (), Haiyi Bian and Tao Hai
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Mahdi A. Mahdi: Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China
Ahmed N. Abdalla: Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China
Lei Liu: Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China
Rendong Ji: Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China
Haiyi Bian: Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China
Tao Hai: Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates

Energies, 2024, vol. 17, issue 19, 1-11

Abstract: In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Keywords: microgrid; load fluctuations; dynamic pricing; electric vehicle (EV); energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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