Configuration of fast/slow charging piles for multiple microgrids considering climbing costs and load fluctuations

This paper presents a two-layer optimal configuration model for EVs' fast/slow charging stations within a multi-microgrid system. The model considers costs related to climbing and netload fluctuations, aiming to meet EVs' charging demands while ensuring grid safety and economy. The upper layer focuses on minimizing investment and operational costs for the microgrid, accounting for EVs' charging characteristics and interdependencies among microgrids. It develops an optimal configuration model for charging stations across multiple microgrids and implements differentiated electricity pricing in various zones to promote orderly charging. The lower layer aims to minimize EVs' charging costs. Using differentiated pricing, it analyzes the spatiotemporal distribution of EVs' charging loads through the Monte Carlo method. This analysis informs the upper layer for further optimization of charging station configurations. An analysis of three scenarios shows that the proposed approach reduces EVs' charging costs by 44.3% compared to uncoordinated charging. It also mitigates the impact of EVs' charging loads on the microgrid, enhances operational safety and contributes to increased social welfare.

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Publication Details

Journal
Systems Science & Control Engineering
Published
2024-11-23
DOI
https://doi.org/10.1080/21642583.2024.2427029
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Configuration of fast/slow charging piles for multiple microgrids considering climbing costs and load fluctuations

Wei Hu, Lei Yang, Z. G. Wen, Gang Huang et al.
Systems Science & Control Engineering
Microgrid Control and Optimization
article

Configuration of fast/slow charging piles for multiple microgrids considering climbing costs and load fluctuations

Wei Hu, Lei Yang, Z. G. Wen, Gang Huang, Zhe Zhang, Jinzhe Li
article en

Abstract

This paper presents a two-layer optimal configuration model for EVs' fast/slow charging stations within a multi-microgrid system. The model considers costs related to climbing and netload fluctuations, aiming to meet EVs' charging demands while ensuring grid safety and economy. The upper layer focuses on minimizing investment and operational costs for the microgrid, accounting for EVs' charging characteristics and interdependencies among microgrids. It develops an optimal configuration model for charging stations across multiple microgrids and implements differentiated electricity pricing in various zones to promote orderly charging. The lower layer aims to minimize EVs' charging costs. Using differentiated pricing, it analyzes the spatiotemporal distribution of EVs' charging loads through the Monte Carlo method. This analysis informs the upper layer for further optimization of charging station configurations. An analysis of three scenarios shows that the proposed approach reduces EVs' charging costs by 44.3% compared to uncoordinated charging. It also mitigates the impact of EVs' charging loads on the microgrid, enhances operational safety and contributes to increased social welfare.

Systems Science & Control EngineeringVol. 12(1)
China Three Gorges University (CN)
State Grid Jiangsu Electric Power
Openalex Percentile: Top 75%
Microgrid Control and Optimization
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