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A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system

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  • Rana, Md Masud
  • Romlie, Mohd Fakhizan
  • Abdullah, Mohd Faris
  • Uddin, Moslem
  • Sarkar, Md Rasel

Abstract

A novel peak load shaving algorithm has been proposed in this study for peak shaving application in hybrid PV-BESS connected Isolated Microgrid (IMG) system. This algorithm will help an IMG system to operate its generation systems optimally and economically along with PV generation unit. An IMG model has been developed in MATLAB/Simulink environment with actual load data, conventional Gas Turbine Generator (GTG), Photovoltaic (PV) generation system and Battery Energy Storage System (BESS). The proposed algorithm has been tested with the designed IMG model. To evaluate the effectiveness of the algorithm, simulation case studies have been conducted with actual load data and actual PV generation data. The simulation results demonstrate that the algorithm can minimize the limitations of the existing methods and it can use the PV generation system effectively. Peak shaving service can be found from this algorithm as a simple and forcible way. A comparative analysis has been conducted of the proposed algorithm with the conventional methods. The comparison results can easily reflect that the proposed algorithm can ensure the optimal use of PV generation system and can serve peak shaving service effectively. It can also ensure an economic and environment friendly system where the existing algorithms are limited to serve these. The proposed algorithm can mitigate the available power issues for hybrid PV-BESS connected system and it can also minimize the operating cost by dispatching the generation units optimally.

Suggested Citation

  • Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014055
    DOI: 10.1016/j.energy.2021.121157
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    References listed on IDEAS

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    Cited by:

    1. Castillejo-Cuberos, A. & Cardemil, J.M. & Escobar, R., 2023. "Techno-economic assessment of photovoltaic plants considering high temporal resolution and non-linear dynamics of battery storage," Applied Energy, Elsevier, vol. 334(C).
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    3. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
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    5. Joel Alpízar-Castillo & Laura Ramirez-Elizondo & Pavol Bauer, 2022. "Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review," Energies, MDPI, vol. 16(1), pages 1-31, December.
    6. Bonginkosi A. Thango & Pitshou N. Bokoro, 2022. "Battery Energy Storage for Photovoltaic Application in South Africa: A Review," Energies, MDPI, vol. 15(16), pages 1-21, August.
    7. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
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    9. Francisco Durán & Wilson Pavón & Luis Ismael Minchala, 2024. "Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid," Energies, MDPI, vol. 17(2), pages 1-21, January.

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