Improved Control Strategy for Three-Phase Microgrid Management with Electric Vehicles Using Multi Objective Optimization Algorithm

Muhammad Shahab, Shaorong Wang and Abdul Khalique Junejo
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Muhammad Shahab: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Shaorong Wang: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Abdul Khalique Junejo: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2021, vol. 14, issue 4, 1-23

Abstract: The usage of electric vehicles (EV) have been spreading worldwide, not only as an alternative to achieve a low-carbon future but also to provide ancillary services to improve the power system reliability. A common problem encountered in the existing alternating current (AC) grids is low power factor, which cause several power quality problems and has worsened with the growing application of distributed generation (DG). Therefore, considering the spread of EVs usage for ancillary services and the low power factor issue in current electrical grids, this paper proposes an improved control strategy for power factor correction of a three-phase microgrid management composed of a photovoltaic (PV) array, dynamic loads, and an EV parking lot. This control strategy aims to support power factor issues using the EV charging stations, allowing the full PV generation. Different operation modes are proposed to fulfill the microgrid and the EV users’ requirements, characterizing a Multi Objective Optimization (MOO) approach. In order to achieve these optimization requests, a dynamic programming method is used to charge the vehicle while adjusting the microgrid power factor. The proposed control algorithm is verified in different scenarios, and its results indicate a suitable performance for the microgrid even during conditions of overload and high peak power surplus in generation unit. The microgrid power factor remains above the desired reference during the entire analyzed period, in which the error is approximately 4.5 less than the system without vehicles, as well as obtains an energy price reduction.

Keywords: microgrid; electric vehicles; distributed generation; power factor (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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