A Review of Optimal Energy Storage Allocation in New Power Systems

Gaoyichou Ji

doi:10.54254/2755-2721/2025.GL24472

Applied and Computational EngineeringOpen access

A Review of Optimal Energy Storage Allocation in New Power Systems

Research Article

Open Access

A Review of Optimal Energy Storage Allocation in New Power Systems

Gaoyichou Ji ^1*

¹ Institute of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China

^*Corresponding author: 120221320121@ncepu.edu.cn

Published on 4 July 2025

ACE Vol.172

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-221-8

ISBN (Online): 978-1-80590-222-5

Download Cover

Abstract

With the rapid development of renewable energy, large-scale grid integration of renewable sources such as wind and solar power has become prevalent. However, their intermittent and volatile nature poses significant challenges to the stability and reliability of power systems. As a key technology for peak shaving, valley filling, and smoothing fluctuations, energy storage technology has attracted considerable attention. Consequently, the optimal allocation of energy storage has become a hot research topic. This paper provides a systematic review of energy storage optimal allocation in new power systems from three perspectives. First, energy storage technologies are categorized based on energy types, and their respective characteristics and applicable scenarios are compared. Second, four major solution algorithms for energy storage optimization are summarized, including traditional optimization algorithms, swarm intelligence algorithms, hybrid optimization algorithms and machine learning approaches, with a discussion on their advantages and disadvantages. The paper also highlights that multi-objective optimization will become mainstream. Finally, based on the characteristics of new power systems, the paper discusses specific energy storage optimal allocation strategies from the perspectives of changes in energy structure and grid topology. This review offers theoretical support and technical references for constructing reliable, economical, and intelligent energy storage systems in new power systems.

Keywords:

new power systems, optimal energy storage allocation, renewable energy integration

View PDF

References

[1]. Zhao, H., Zhang, W., Liu, J., Lu, Y., Shi, M., & Meng, X. (2023) Wind-solar energy storage joint system operation strategy based on multi-objective particle swarm optimization algorithm. 2023 2nd Asia Power and Electrical Technology Conference (APET), 823-827.

[2]. Huo, S., Jiang, J., Fan, Y., Wang, S., Li, W., & Li, J. (2025) Study on the optimal allocation of energy storage capacity for stand-alone photovoltaic power generation system based on improved particle swarm algorithm. 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC), 179-184.

[3]. Cui, K., Chi, M., Zhao, Y. and Liu, Z.W. (2025) Bilevel Optimization Framework for Multiregional Integrated Energy Systems Considering 6G Network Slicing and Battery Energy Storage Capacity Sharing. H&E Open Journal of the Industrial Electronics Society, 6, 396-414.

[4]. Li, H.X., Li, J.L., Mi, Y. (2022) Research progress on energy storage optimization configuration technology on the new energy side. Energy Storage Science and Technology, 11(10), 3257-3267.

[5]. Ge, L., Zheng, Y., Li, X., Wang, H., Yang, J., & Che, Y. (2025) A review of optimal allocation technologies for multi-type energy storage in distribution networks with high penetration of distributed photovoltaics. Zhejiang Electric Power, 43(6).

[6]. Lin, S., Wen, S., Zhu, M., Ma, J., & Zhao, Y. (2024) Review on Low-carbon and Economic Development of Seaport Integrated Energy System. Proceedings of the CSEE, 44(4), 1364-1375.

[7]. Zhou, Z., Lin, W., Lu, S., Li, Y., & Di, Q. (2024) Optimization Configuration of New Energy Station Capacity Considering Sand Lifting-Thermal Energy Storage Technologies. Proceedings of the 5th International Conference on Clean Energy and Electric Power Engineering (ICCEPE), 13-17.

[8]. Wang, W., Shi, J., Gu, X., Zhang, M., Huang, P., Wang, Z., Wang, J., Huang, H., Lv, M., Nazir, M. S., & Ji, J. (2025) Optimal configuration of retired battery energy storage system using Two-Scenario Cascade Utilization model and Newton-Raphson Backtracking Optimization algorithm. Journal of Energy Storage, 113, 115600.

[9]. Lv, X., Basem, A., Hasani, M., Sun, P. and Zhang, J. (2025) The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems. Scientific Reports, 15, 11187.

[10]. Duan, F., Eslami, M., Khajehzadeh, M., Basem, A., Jasim, D.J. and Palani, S. (2024) Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms. Scientific Reports, 14, 13354.

[11]. Liu, M., Sun, Z., Li, J. and Zheng, K. (2024) Research on Optimal Allocation of Energy Storage in Power System with High Proportion of Renewable Energy under Extreme Events. In Proceedings of the 9th International Conference on Power and Renewable Energy (ICPRE), Nanning, China, 1280–1285.

[12]. Zhang, D., Shafiullah, G. M., Das, C. K., and Wong, K. W. (2025) Optimal allocation of battery energy storage systems to improve system reliability and voltage and frequency stability in weak grids. Applied Energy, 377, 124541.

[13]. Song, H., Zhang, M. and Shi, Q. (2024) Capacity Optimization of Hybrid Energy Storage System Based on Improved Zebra Optimization Algorithm. IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 1390–1394.

[14]. Lu, X., Gang, W., Cai, S. and Tu, Z. (2025) Energy Management Strategy for a Novel Multi-Stack Integrated Hydrogen Energy Storage System Based on Hybrid Rules and Optimization. Applied Energy, 381, 125189.

[15]. Duan, X., Wang, Y., Wei, Y., Li, Y., and Cui, X. (2023) Joint Optimal Configuration of Distributed Photovoltaics and Hybrid Energy Storage in Distribution Networks Considering Renewable Energy Consumption. Modern Electric Power.

[16]. Jin, X., Dong, D., Tang, H., Li, X., Chen, Q., Lin, C., & Zhang, Y. (2025). Capacity optimization configuration method of distributed photovoltaic and energy storage system considering power quality improvement. Energy and Environmental Protection, 47(3), 243-250.

References

[4]. Li, H.X., Li, J.L., Mi, Y. (2022) Research progress on energy storage optimization configuration technology on the new energy side. Energy Storage Science and Technology, 11(10), 3257-3267.

[6]. Lin, S., Wen, S., Zhu, M., Ma, J., & Zhao, Y. (2024) Review on Low-carbon and Economic Development of Seaport Integrated Energy System. Proceedings of the CSEE, 44(4), 1364-1375.

Cite this article

Ji,G. (2025). A Review of Optimal Energy Storage Allocation in New Power Systems. Applied and Computational Engineering,172,44-52.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN: 978-1-80590-221-8(Print) / 978-1-80590-222-5(Online)

Editor: Anil Fernando

Conference website: https://2025.conffmce.org/glasgow.html

Conference date: 24 October 2025

Series: Applied and Computational Engineering

Volume number: Vol.172

ISSN: 2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).