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Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage

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  • Khalid, Muhammad
  • Ahmadi, Abdollah
  • Savkin, Andrey V.
  • Agelidis, Vassilios G.

Abstract

This paper presents a methodology to minimize the total cost of buying power from different energy producers including renewable energy generations particularly within the context of a microgrid. The proposed idea is primarily based on the controlled operation of a battery energy storage system (BESS) in the presence of practical system constraints coupled with our proposed cost optimization algorithm. The complex optimization problem with constraints has been solved using the well-known concept of dynamic programming. The methodology has been assessed using actual power and price data from six different power generation sites and cost reduction has been calculated for a number of BESSs by varying their energy and power capacities. Twofold benefits of the proposed methodology lie in minimizing the total cost along with the constraint-based efficient operation of the BESS. Simulation results depict that the given power demand at a particular region can be fulfilled properly at all times using a BESS and multiple power generation.

Suggested Citation

  • Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    • Handle: RePEc:eee:renene:v:97:y:2016:i:c:p:646-655
    DOI: 10.1016/j.renene.2016.05.042
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    14. Abdullah Al Shereiqi & Amer Al-Hinai & Mohammed Albadi & Rashid Al-Abri, 2020. "Optimal Sizing of a Hybrid Wind-Photovoltaic-Battery Plant to Mitigate Output Fluctuations in a Grid-Connected System," Energies, MDPI, vol. 13(11), pages 1-21, June.
    15. Wenhao Zhuo & Andrey V. Savkin & Ke Meng, 2019. "Decentralized Optimal Control of a Microgrid with Solar PV, BESS and Thermostatically Controlled Loads," Energies, MDPI, vol. 12(11), pages 1-15, June.
    16. Surroop, Dinesh & Raghoo, Pravesh, 2017. "Energy landscape in Mauritius," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 688-694.
    17. Ossai, Chinedu I., 2017. "Optimal renewable energy generation – Approaches for managing ageing assets mechanisms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 269-280.
    18. Karimi, Ali & Aminifar, Farrokh & Fereidunian, Alireza & Lesani, Hamid, 2019. "Energy storage allocation in wind integrated distribution networks: An MILP-Based approach," Renewable Energy, Elsevier, vol. 134(C), pages 1042-1055.
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    20. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    21. Ahmed S. Menesy & Hamdy M. Sultan & Ibrahim O. Habiballah & Hasan Masrur & Kaisar R. Khan & Muhammad Khalid, 2023. "Optimal Configuration of a Hybrid Photovoltaic/Wind Turbine/Biomass/Hydro-Pumped Storage-Based Energy System Using a Heap-Based Optimization Algorithm," Energies, MDPI, vol. 16(9), pages 1-26, April.
    22. Carlos Roldán-Porta & Carlos Roldán-Blay & Guillermo Escrivá-Escrivá & Eduardo Quiles, 2019. "Improving the Sustainability of Self-Consumption with Cooperative DC Microgrids," Sustainability, MDPI, vol. 11(19), pages 1-22, October.

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