Multi-Objective Optimization of Energy Storage Station Configuration in Power Grids Considering the Flexibility of Thermal Load Control

Given that traditional grid energy storage planning neglects the impact of power supply demand on the effectiveness of storage deployment, the resulting system suffers from limited operational economic performance and restricted renewable energy integration capability. In response to this challenge, this paper presents a multi-objective optimization approach for configuring a distribution network energy storage station (ESS) by incorporating the flexibility of temperature-controlled loads. This approach aims to enhance the efficiency of energy storage utilization, facilitate the local consumption of renewable energy (RE), and achieve mutually beneficial outcomes for both energy providers and consumers. Firstly, a controllable load model for the distribution network was developed, incorporating power balance constraints and the flexibility of temperature-controlled loads. Additionally, an ESS model was formulated, taking into account economic considerations and other influencing factors to ensure optimal deployment and operation. Secondly, this paper introduces a multi-objective optimization strategy for a distribution network ESS, targeting the minimization of both the microgrid operating costs and energy storage allocation costs. The proposed model was solved using the POA-GWO-CSO optimization algorithm to achieve the optimal energy storage deployment and cost efficiency. Finally, the effectiveness of the proposed model was verified through case analysis. The results demonstrate that the proposed grid energy storage optimization configuration model not only satisfies the requirements of both parties, but also enhances the overall system economic performance.

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Publication Details

Journal
Energies
Published
2025-05-14
DOI
https://doi.org/10.3390/en18102527
Citations
2
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
1.46

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article

Multi-Objective Optimization of Energy Storage Station Configuration in Power Grids Considering the Flexibility of Thermal Load Control

MA Zhen-fei, Jin Gao, Zening Li, Yao Wang et al.
2 citations
Energies
Smart Grid Energy Management
1.46
article

Multi-Objective Optimization of Energy Storage Station Configuration in Power Grids Considering the Flexibility of Thermal Load Control

MA Zhen-fei, Jin Gao, Zening Li, Yao Wang, Hanyue Liu, Yan Liang, Kaikai Wang
article en
2 citations

Abstract

Given that traditional grid energy storage planning neglects the impact of power supply demand on the effectiveness of storage deployment, the resulting system suffers from limited operational economic performance and restricted renewable energy integration capability. In response to this challenge, this paper presents a multi-objective optimization approach for configuring a distribution network energy storage station (ESS) by incorporating the flexibility of temperature-controlled loads. This approach aims to enhance the efficiency of energy storage utilization, facilitate the local consumption of renewable energy (RE), and achieve mutually beneficial outcomes for both energy providers and consumers. Firstly, a controllable load model for the distribution network was developed, incorporating power balance constraints and the flexibility of temperature-controlled loads. Additionally, an ESS model was formulated, taking into account economic considerations and other influencing factors to ensure optimal deployment and operation. Secondly, this paper introduces a multi-objective optimization strategy for a distribution network ESS, targeting the minimization of both the microgrid operating costs and energy storage allocation costs. The proposed model was solved using the POA-GWO-CSO optimization algorithm to achieve the optimal energy storage deployment and cost efficiency. Finally, the effectiveness of the proposed model was verified through case analysis. The results demonstrate that the proposed grid energy storage optimization configuration model not only satisfies the requirements of both parties, but also enhances the overall system economic performance.

EnergiesVol. 18(10)
Shanxi University (CN), Taiyuan University of Technology (CN)
Science and Technology Project of State Grid
Openalex Percentile: Top 18%
Smart Grid Energy Management
1.46
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