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Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

Author

Listed:
  • Ali, Aamir
  • Bughio, Ateeq-u-Rehman
  • Abbas, Ghulam
  • Keerio, M.U.
  • Mugheri, N.H.
  • Memon, Shaina
  • Saand, A.S.

Abstract

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Suggested Citation

  • Ali, Aamir & Bughio, Ateeq-u-Rehman & Abbas, Ghulam & Keerio, M.U. & Mugheri, N.H. & Memon, Shaina & Saand, A.S., 2024. "Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm," Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:energy:v:311:y:2024:i:c:s0360544224032390
    DOI: 10.1016/j.energy.2024.133463
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    1. Bozorgavari, Seyed Aboozar & Aghaei, Jamshid & Pirouzi, Sasan & Nikoobakht, Ahmad & Farahmand, Hossein & Korpås, Magnus, 2020. "Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    2. Ahmadi, Bahman & Ceylan, Oguzhan & Ozdemir, Aydogan & Fotuhi-Firuzabad, Mahmoud, 2022. "A multi-objective framework for distributed energy resources planning and storage management," Applied Energy, Elsevier, vol. 314(C).
    3. Tung Tran The & Bao-Huy Truong & Khanh Dang Tuan & Dieu Vo Ngoc, 2021. "A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-20, February.
    4. Hung, Duong Quoc & Mithulananthan, N., 2014. "Loss reduction and loadability enhancement with DG: A dual-index analytical approach," Applied Energy, Elsevier, vol. 115(C), pages 233-241.
    5. Pirouzi, Sasan & Aghaei, Jamshid & Niknam, Taher & Shafie-khah, Miadreza & Vahidinasab, Vahid & Catalão, João P.S., 2017. "Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks," Energy, Elsevier, vol. 141(C), pages 635-651.
    6. Qiu, Jing & Zhao, Junhua & Yang, Hongming & Wang, Dongxiao & Dong, Zhao Yang, 2018. "Planning of solar photovoltaics, battery energy storage system and gas micro turbine for coupled micro energy grids," Applied Energy, Elsevier, vol. 219(C), pages 361-369.
    7. Esmaeili, Mobin & Sedighizadeh, Mostafa & Esmaili, Masoud, 2016. "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, Elsevier, vol. 103(C), pages 86-99.
    8. Grover-Silva, Etta & Girard, Robin & Kariniotakis, George, 2018. "Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm," Applied Energy, Elsevier, vol. 219(C), pages 385-393.
    9. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    10. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    11. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    12. Niknam, Taher & Fard, Abdollah Kavousi & Seifi, Alireza, 2012. "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants," Renewable Energy, Elsevier, vol. 37(1), pages 213-225.
    13. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
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