Searching Optimal Placement and Operations of Energy Storage Systems based on Equilibrium Optimizer
Received: 1 April 2025 | Revised: 21 April 2025 | Accepted: 24 April 2025 | Online: 2 August 2025
Corresponding author: Khoa Hoang Truong
Abstract
Renewable energy integration in Distribution Networks (DN) presents significant opportunities for enhancing stability, reliability, and operational efficiency. To address the challenges that arise from this integration, the use of a Battery Energy Storage System (BESS) within the DN can be very effective. This study explores the optimization of a BESS in order to reduce the system costs and improve the overall DN performance. To facilitate this optimization, the present study proposes two innovative methods: an Equilibrium Optimizer (EO) and a Newton-Raphson-based Optimizer (NRBO). These methods were applied to an IEEE 18-bus distribution network through various case scenarios. The findings demonstrate that an effective BESS integration can significantly bolster DN performance. Furthermore, the EO method was compared with alternative approaches and its effectiveness in solving optimization challenges was validated. This research underscores the potential for advancements in renewable energy integration, paving the way for more efficient and reliable DNs.
Keywords:
battery energy storage system optimization, equilibrium optimizer, photovoltaic power, distributed generation, IEEE-18 bus distribution networkDownloads
References
E. Ejuh Che, K. Roland Abeng, C. D. Iweh, G. J. Tsekouras, and A. Fopah-Lele, "The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects," Energies, vol. 18, no. 3, p. 689, Jan. 2025. DOI: https://doi.org/10.3390/en18030689
Y. Wang et al., " Revitalising sodium–sulfur batteries for non-high-temperature operation: a crucial review," Energy & Environmental Science, vol. 13, no. 11, pp. 3848-3879, Oct. 2020. DOI: https://doi.org/10.1039/D0EE02203A
P. P. Lopes and V. R. Stamenkovic, "Past, present, and future of lead-acid batteries," Science, vol. 369, no. 6506, pp. 923-924, Aug. 2020. DOI: https://doi.org/10.1126/science.abd3352
G. Rancilio et al., " Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis." Energies, vol. 12, no. 17, Aug. 2019, no. 3312. DOI: https://doi.org/10.3390/en12173312
G. Luo, X. Li, L. Chen, Y. Chao, and W. Zhu, "Electrochemical lithium ion pumps for lithium recovery: A systematic review and influencing factors analysis," Desalination, vol. 548, no. 116228, Feb. 2023. DOI: https://doi.org/10.1016/j.desal.2022.116228
J. Mitali, S. Dhinakaran, and A. A. Mohamad, "Energy storage systems: a review," Energy Storage and Saving, vol. 1, no. 3, pp. 166–216, Sep. 2022. DOI: https://doi.org/10.1016/j.enss.2022.07.002
M. Stecca, L. R. Elizondo, T. B. Soeiro, P. Bauer, and P. Palensky, "A Comprehensive Review of the Integration of Battery Energy Storage Systems Into Distribution Networks," IEEE Open Journal of the Industrial Electronics Society, vol. 1, pp. 46–65, 2020. DOI: https://doi.org/10.1109/OJIES.2020.2981832
T. Thanh Nguyen, T. Trung Nguyen, and B. Le, "Artificial ecosystem optimization for optimizing of position and operational power of battery energy storage system on the distribution network considering distributed generations," Expert Systems with Applications, vol. 208, no. 118127, Dec. 2022. DOI: https://doi.org/10.1016/j.eswa.2022.118127
R. Hemmati and M. Hasan, "Stochastic Linear Programming for Optimal Planning of Battery Storage Systems Under Unbalanced-uncertain Conditions," Journal of Modern Power Systems and Clean Energy, vol. 8, no. 5, pp. 971-980, Mar. 2020. DOI: https://doi.org/10.35833/MPCE.2019.000324
C. A. Mora, O. D. Montoya, and E. R. Trujillo, "Mixed-Integer Programming Model for Transmission Network Expansion Planning with Battery Energy Storage Systems (BESS)," Energies, vol. 13, no. 17, no. 4386, Jan. 2020. DOI: https://doi.org/10.3390/en13174386
A. K. Erenoğlu, İ. Şengör, O. Erdinç, A. Taşcıkaraoğlu, and J. P. S. Catalão, "Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation," International Journal of Electrical Power & Energy Systems, vol. 136, no. 107714, Mar. 2022. DOI: https://doi.org/10.1016/j.ijepes.2021.107714
C. Zheng, M. Eskandari, M. Li, and Z. Sun, "GA−Reinforced Deep Neural Network for Net Electric Load Forecasting in Microgrids with Renewable Energy Resources for Scheduling Battery Energy Storage Systems," Algorithms, vol. 15, no. 10, p. 338, Oct. 2022. DOI: https://doi.org/10.3390/a15100338
W. M. N. Witharama, K. M. D. P. Bandara, M. I. Azeez, K. Bandara, V. Logeeshan, and C. Wanigasekara, "Advanced Genetic Algorithm for Optimal Microgrid Scheduling Considering Solar and Load Forecasting, Battery Degradation, and Demand Response Dynamics," IEEE Access, vol. 12, pp. 83269–83284, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3412914
H. Kang, S. Jung, H. Kim, J. Hong, J. Jeoung, and T. Hong, "Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning," Renewable and Sustainable Energy Reviews, vol. 185, no. 113655, Oct. 2023. DOI: https://doi.org/10.1016/j.rser.2023.113655
L. F. Grisales-Noreña, O. D. Montoya, and C. A. Ramos-Paja, "An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm," Journal of Energy Storage, vol. 29, no. 101488, Jun. 2020. DOI: https://doi.org/10.1016/j.est.2020.101488
V. Janamala and D. S. Reddy, "Coyote optimization algorithm for optimal allocation of interline –Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration, " Journal of Energy Storage, vol. 41, no. 102981, Sep. 2021. DOI: https://doi.org/10.1016/j.est.2021.102981
X. Wang, Y. Lin, B. Wang, W. Liu, and K. Bai, "Output Voltage Control of BESS Inverter in Stand-Alone Micro-Grid Based on Expanded Inverse Model," IEEE Access, vol. 8, pp. 3781–3791, 2020. DOI: https://doi.org/10.1109/ACCESS.2019.2962530
L. A. Wong, V. K. Ramachandaramurthy, S. L. Walker, P. Taylor, and M. J. Sanjari, "Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm," Journal of Energy Storage, vol. 26, no. 100892, Dec. 2019. DOI: https://doi.org/10.1016/j.est.2019.100892
G. M. Binini, J. L. Munda, and O. M. Popoola, "Optimal location, sizing and scheduling of distributed energy storage in a radial distribution network," Journal of Energy Storage, vol. 94, no. 112499, Jul. 2024. DOI: https://doi.org/10.1016/j.est.2024.112499
G. Talluri, G. M. Lozito, F. Grasso, C. Iturrino Garcia, and A. Luchetta, "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, vol. 14, no. 24, no. 8480, Jan. 2021. DOI: https://doi.org/10.3390/en14248480
A. Faramarzi, M. Heidarinejad, B. Stephens, and S. Mirjalili, "Equilibrium optimizer: A novel optimization algorithm," Knowledge-Based Systems, vol. 191, no. 105190, Mar. 2020. DOI: https://doi.org/10.1016/j.knosys.2019.105190
R. Sowmya, M. Premkumar, and P. Jangir, "Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems," Engineering Applications of Artificial Intelligence, vol. 128, no. 107532, Feb. 2024. DOI: https://doi.org/10.1016/j.engappai.2023.107532
M. Alturki, "Α Combined Metaheuristic Optimization Technique for Optimal Site and Scaling of PVDG System in a Radial Distribution Network," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18371–18379, Dec. 2024. DOI: https://doi.org/10.48084/etasr.8898
Downloads
How to Cite
License
Copyright (c) 2025 Hung Duc Nguyen, Phuong Minh Le, Khoa Hoang Truong

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
