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Probabilistic operation cost minimization of Micro-Grid

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  • Sharma, Sharmistha
  • Bhattacharjee, Subhadeep
  • Bhattacharya, Aniruddha

Abstract

In recent years due to the increasing integration of Renewable Energy Sources (RES) into the Micro-Grid (MG), necessity of Battery Energy Storage (BES) has increased quickly and size of BES plays vital role in this regard. Present paper aims to minimize total operation cost of MG in presence of BES of optimal size, by considering uncertainties present in the MG. Here, 2 m point estimate method (PEM) has been applied to model the uncertainties in load demand, market prices and available power from RES in the MG, as it is computationally efficient and reliable probabilistic method. Moreover, Gram-Charlier expansion is used to provide more accurate probability distribution of MG operation cost. Classical techniques may be applied here to solve the problem, but these techniques may increase complexity of the problem and hence may affect the accuracy. As evolvement of soft computing techniques are not restricted by the complexity of system model, therefore Swine Influenza Model Based Optimization with Quarantine (SIMBO-Q) and Whale Optimization Algorithm (WOA) have been applied here to minimize operation cost of MG. Simulation results obtained by SIMBO-Q and WOA prove the effectiveness of the algorithms. Here incorporation of BES of optimum size reduces operation cost of MG effectively.

Suggested Citation

  • Sharma, Sharmistha & Bhattacharjee, Subhadeep & Bhattacharya, Aniruddha, 2018. "Probabilistic operation cost minimization of Micro-Grid," Energy, Elsevier, vol. 148(C), pages 1116-1139.
    • Handle: RePEc:eee:energy:v:148:y:2018:i:c:p:1116-1139
    DOI: 10.1016/j.energy.2018.01.164
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    Cited by:

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    3. Ho-Sung Ryu & Mun-Kyeom Kim, 2020. "Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty," Energies, MDPI, vol. 13(22), pages 1-25, November.
    4. Mansour-lakouraj, Mohammad & Shahabi, Majid, 2019. "Comprehensive analysis of risk-based energy management for dependent micro-grid under normal and emergency operations," Energy, Elsevier, vol. 171(C), pages 928-943.
    5. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    6. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    7. Chakraborty, Amit & Ray, Saheli, 2024. "Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid," Renewable Energy, Elsevier, vol. 222(C).
    8. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    9. Bishwajit Dey & Fausto Pedro García Márquez & Sourav Kr. Basak, 2020. "Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm," Energies, MDPI, vol. 13(13), pages 1-23, July.
    10. Mo, Qiu & Liu, Fang, 2020. "Modeling and optimization for distributed microgrid based on Modelica language," Applied Energy, Elsevier, vol. 279(C).
    11. Incheol Shin, 2020. "Approximation Algorithm-Based Prosumer Scheduling for Microgrids," Energies, MDPI, vol. 13(21), pages 1-16, November.

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