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Use of Energy Storage to Reduce Transmission Losses in Meshed Power Distribution Networks

Author

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  • Stanisław Mikulski

    (Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznań, Poland)

  • Andrzej Tomczewski

    (Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznań, Poland)

Abstract

One of the challenges which the electrical power industry has been facing nowadays is the adaptation of the power system to the energy transition which has been taking place before our very eyes. With the increasing share of Renewable Energy Sources (RES) in energy production, the development of electromobility and the increasing environmental awareness of the society, the power system must constantly evolve to meet its expectations regarding a reliable electricity supply. This paper presents the issue of deploying energy storage facilities in the meshed power distribution network in order to reduce transmission losses. The presented multi-objective approach provides an opportunity to solve this issue using multi-objective optimisation methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multiobjective Particle Swarm Optimization (MPSO) and Biased Random Keys Genetic Algorithm (BRKGA). In order to increase the efficiency optimisation process, the Pareto Adaptive ? -dominance ( p a ? -dominance) was used. It was demonstrated that the use of energy storages that cooperate with RES can significantly reduce transmission losses.

Suggested Citation

  • Stanisław Mikulski & Andrzej Tomczewski, 2021. "Use of Energy Storage to Reduce Transmission Losses in Meshed Power Distribution Networks," Energies, MDPI, vol. 14(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7304-:d:671751
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    References listed on IDEAS

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    3. Sachin Kumar & Kumari Sarita & Akanksha Singh S Vardhan & Rajvikram Madurai Elavarasan & R. K. Saket & Narottam Das, 2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique," Energies, MDPI, vol. 13(21), pages 1-30, October.
    4. Martin Ćalasan & Tatjana Konjić & Katarina Kecojević & Lazar Nikitović, 2020. "Optimal Allocation of Static Var Compensators in Electric Power Systems," Energies, MDPI, vol. 13(12), pages 1-24, June.
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    Cited by:

    1. Andrzej Tomczewski & Stanisław Mikulski & Adam Piotrowski & Sławomir Sowa & Krzysztof Wróbel, 2023. "Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios," Energies, MDPI, vol. 16(10), pages 1-21, May.
    2. Londe, Mariana A. & Pessoa, Luciana S. & Andrade, Carlos E. & Resende, Mauricio G.C., 2025. "Biased random-key genetic algorithms: A review," European Journal of Operational Research, Elsevier, vol. 321(1), pages 1-22.
    3. Imene Khenissi & Tawfik Guesmi & Ismail Marouani & Badr M. Alshammari & Khalid Alqunun & Saleh Albadran & Salem Rahmani & Rafik Neji, 2023. "Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile," Sustainability, MDPI, vol. 15(2), pages 1-28, January.
    4. Zbigniew Kłosowski & Łukasz Mazur, 2023. "Influence of the Type of Receiver on Electrical Energy Losses in Power Grids," Energies, MDPI, vol. 16(15), pages 1-22, July.

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