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Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile

Author

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  • Imene Khenissi

    (Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3036, Tunisia)

  • Tawfik Guesmi

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Ismail Marouani

    (Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3036, Tunisia)

  • Badr M. Alshammari

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Khalid Alqunun

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Saleh Albadran

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Salem Rahmani

    (Research Laboratory of Biophysics and Medical Technology, Higher Institute of Medical Technologies, University of Tunis El-Manar, Tunis 1002, Tunisia)

  • Rafik Neji

    (Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3036, Tunisia)

Abstract

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1004-:d:1026262
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    References listed on IDEAS

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    1. Yang, Dixiong & Li, Gang & Cheng, Gengdong, 2007. "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(4), pages 1366-1375.
    2. 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.
    3. Kumar, Deepak & Rani, Mamta, 2022. "Alternated superior chaotic variants of gravitational search algorithm for optimization problems," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Hassan, Aakash & Al-Abdeli, Yasir M. & Masek, Martin & Bass, Octavian, 2022. "Optimal sizing and energy scheduling of grid-supplemented solar PV systems with battery storage: Sensitivity of reliability and financial constraints," Energy, Elsevier, vol. 238(PA).
    5. Md. Rashedul Islam & Homeyra Akter & Harun Or Rashid Howlader & Tomonobu Senjyu, 2022. "Optimal Sizing and Techno-Economic Analysis of Grid-Independent Hybrid Energy System for Sustained Rural Electrification in Developing Countries: A Case Study in Bangladesh," Energies, MDPI, vol. 15(17), pages 1-21, September.
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    Cited by:

    1. Jahangir Hossain & Aida. F. A. Kadir & Hussain Shareef & Rampelli Manojkumar & Nagham Saeed & Ainain. N. Hanafi, 2023. "A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings," Sustainability, MDPI, vol. 15(13), pages 1-20, July.

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