IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p428-d1570707.html
   My bibliography  Save this article

Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads

Author

Listed:
  • M A Sasi Bhushan

    (Department of EEE, Puducherry Technological University, Puducherry 605014, Puducherry, India)

  • M. Sudhakaran

    (Department of EEE, Puducherry Technological University, Puducherry 605014, Puducherry, India)

  • Sattianadan Dasarathan

    (Department of EEE, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamilnadu, India)

  • Mariappane E

    (Department of ECE, Christ Institute of Technology, Villianur Commune, Puducherry 605502, Puducherry, India)

Abstract

A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method.

Suggested Citation

  • M A Sasi Bhushan & M. Sudhakaran & Sattianadan Dasarathan & Mariappane E, 2025. "Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads," Energies, MDPI, vol. 18(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:428-:d:1570707
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/428/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/428/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed Ali Zdiri & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Abdulaziz Almalaq & Fatma Ben Salem & Hsan Hadj Abdallah & Ahmed Toumi, 2022. "Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System," Energies, MDPI, vol. 15(11), pages 1-20, June.
    2. Jiang, Xin & Jin, Yang & Zheng, Xueyuan & Hu, Guobao & Zeng, Qingshan, 2020. "Optimal configuration of grid-side battery energy storage system under power marketization," Applied Energy, Elsevier, vol. 272(C).
    3. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    4. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    5. Elham Mahdavi & Seifollah Asadpour & Leonardo H. Macedo & Rubén Romero, 2023. "Reconfiguration of Distribution Networks with Simultaneous Allocation of Distributed Generation Using the Whale Optimization Algorithm," Energies, MDPI, vol. 16(12), pages 1-19, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    2. Mahmoud Ghofrani, 2025. "Sensitivity Analysis of Distribution Network Reconfiguration Optimization for Electric Vehicle and Renewable Distributed Generator Integration," Energies, MDPI, vol. 18(8), pages 1-23, April.
    3. Toufani, Parinaz & Nadar, Emre & Kocaman, Ayse Selin, 2022. "Short-term assessment of pumped hydro energy storage configurations: Up, down, or closed?," Renewable Energy, Elsevier, vol. 201(P1), pages 1086-1095.
    4. Hafiz, Faeza & Rodrigo de Queiroz, Anderson & Fajri, Poria & Husain, Iqbal, 2019. "Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach," Applied Energy, Elsevier, vol. 236(C), pages 42-54.
    5. Zhou, Hou Sheng & Passey, Rob & Bruce, Anna & Sproul, Alistair B., 2021. "A case study on the behaviour of residential battery energy storage systems during network demand peaks," Renewable Energy, Elsevier, vol. 180(C), pages 712-724.
    6. Baohong Jin & Zhichao Liu & Yichuan Liao, 2023. "Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model," Energies, MDPI, vol. 16(6), pages 1-21, March.
    7. Zhaonian Ye & Yongzhen Wang & Kai Han & Changlu Zhao & Juntao Han & Yilin Zhu, 2023. "Bi-Objective Optimization and Emergy Analysis of Multi-Distributed Energy System Considering Shared Energy Storage," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    8. Zhang, Kai & Li, Jingzhi & He, Zhubin & Yan, Wanfeng, 2018. "Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 356-369.
    9. Zheng, Menglian & Wang, Xinhao & Meinrenken, Christoph J. & Ding, Yi, 2018. "Economic and environmental benefits of coordinating dispatch among distributed electricity storage," Applied Energy, Elsevier, vol. 210(C), pages 842-855.
    10. Rabea Jamil Mahfoud & Nizar Faisal Alkayem & Emmanuel Fernandez-Rodriguez & Yuan Zheng & Yonghui Sun & Shida Zhang & Yuquan Zhang, 2024. "Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks," Mathematics, MDPI, vol. 12(2), pages 1-33, January.
    11. Qibo He & Changming Chen & Xin Fu & Shunjiang Yu & Long Wang & Zhenzhi Lin, 2024. "Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information," Energies, MDPI, vol. 17(19), pages 1-20, September.
    12. G. Srinivasan & Kumar Reddy Cheepati & B. Srikanth Goud & Mohammed Alqarni & Basem Alamri & Ch. Rami Reddy, 2024. "Optimizing Techno-Economic Framework of REGs in Capacitive Supported Optimal Distribution Network," Energies, MDPI, vol. 17(23), pages 1-31, November.
    13. Dai, Bin & Wang, Honglei & Li, Bin & Li, Chengjiang & Tan, Zhukui, 2024. "Capacity model and optimal scheduling strategy of multi-microgrid based on shared energy storage," Energy, Elsevier, vol. 306(C).
    14. Jie Ji & Xin Xia & Wei Ni & Kailiang Teng & Chunqiong Miao & Yaodong Wang & Tony Roskilly, 2019. "An Experimental and Simulation Study on Optimisation of the Operation of a Distributed Power Generation System with Energy Storage—Meeting Dynamic Household Electricity Demand," Energies, MDPI, vol. 12(6), pages 1-16, March.
    15. Chen, Xi & Liu, Zhongbing & Wang, Pengcheng & Li, Benjia & Liu, Ruimiao & Zhang, Ling & Zhao, Chengliang & Luo, Songqin, 2023. "Multi-objective optimization of battery capacity of grid-connected PV-BESS system in hybrid building energy sharing community considering time-of-use tariff," Applied Energy, Elsevier, vol. 350(C).
    16. Song, Xiaoling & Wu, Han & Zhang, Huqing & Guo, Jianxin & Zhang, Zhe & Peña-Mora, Feniosky, 2025. "Can retail electricity pricing promote microgrid operators to leverage shared energy storage services among internal aggregators?," Energy, Elsevier, vol. 314(C).
    17. Meng, He & Jia, Hongjie & Xu, Tao & Wei, Wei & Wu, Yuhan & Liang, Lemeng & Cai, Shuqi & Liu, Zuozheng & Wang, Rujing & Li, Mengchao, 2022. "Optimal configuration of cooperative stationary and mobile energy storage considering ambient temperature: A case for Winter Olympic Game," Applied Energy, Elsevier, vol. 325(C).
    18. Wu, Yaling & Liu, Zhongbing & Liu, Jiangyang & Xiao, Hui & Liu, Ruimiao & Zhang, Ling, 2022. "Optimal battery capacity of grid-connected PV-battery systems considering battery degradation," Renewable Energy, Elsevier, vol. 181(C), pages 10-23.
    19. Kuznetsova, Elizaveta & Anjos, Miguel F., 2021. "Prosumers and energy pricing policies: When, where, and under which conditions will prosumers emerge? A case study for Ontario (Canada)," Energy Policy, Elsevier, vol. 149(C).
    20. Hu, Junjie & Wang, Yudong & Dong, Lei, 2024. "Low carbon-oriented planning of shared energy storage station for multiple integrated energy systems considering energy-carbon flow and carbon emission reduction," Energy, Elsevier, vol. 290(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:428-:d:1570707. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.