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A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting

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  • Moradi, Mohammad H.
  • Eskandari, Mohsen

Abstract

Recently, microgrids have attracted considerable attention as a high-quality and reliable source of electricity. In this work energy management in microgrids is addressed in light of economic and environmental restrictions through (a) development of an operational strategy for energy management in microgrids and (b) determination of type and capacity of distributed generation (DG) sources as well as the capacity of storage devices (SD) based on optimization. Net present value is used as an economic indicator for justification of investment in microgrids. The proposed NPV-based objective function accounts for the expenses including the initial investment costs, operational strategy costs, purchase of electricity from the utility, maintenance and operational costs, as well as revenues including those associated with reduction in non-delivered energy, the credit for reduction in levels of environmental pollution, and sales of electricity back to the utility. The optimal solution maximizing the objective function is obtained using a hybrid optimization method which combines the quadratic programming (QP) and the particle swarm optimization (PSO) algorithms to determine the optimum capacity of the sources as well as the appropriate operational strategy for the microgrid. The fuzzy set theory is employed to account for the uncertainties associated with electrical power price. Application of the proposed method under different operational scenarios serves to demonstrate the efficiency of the proposed scheme.

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  • Moradi, Mohammad H. & Eskandari, Mohsen, 2014. "A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting," Renewable Energy, Elsevier, vol. 68(C), pages 697-714.
    • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:697-714
    DOI: 10.1016/j.renene.2014.03.001
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    1. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
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    3. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher, 2012. "Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1268-1281.
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    3. Hye-Rim Kim & Tong-Seop Kim, 2021. "Optimization of Sizing and Operation Strategy of Distributed Generation System Based on a Gas Turbine and Renewable Energy," Energies, MDPI, vol. 14(24), pages 1-28, December.
    4. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    5. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    6. Caldeira, Carla & Swei, Omar & Freire, Fausto & Dias, Luis C. & Olivetti, Elsa A. & Kirchain, Randolph, 2019. "Planning strategies to address operational and price uncertainty in biodiesel production," Applied Energy, Elsevier, vol. 238(C), pages 1573-1581.
    7. Kalim Ullah & Quanyuan Jiang & Guangchao Geng & Rehan Ali Khan & Sheraz Aslam & Wahab Khan, 2022. "Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses," Energies, MDPI, vol. 15(9), pages 1-22, April.
    8. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    9. Kang, Jing & Wang, Shengwei, 2018. "Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations," Applied Energy, Elsevier, vol. 231(C), pages 615-627.
    10. Li-Peng Shao & Jia-Jia Chen & Lu-Wen Pan & Zi-Juan Yang, 2022. "A Credibility Theory-Based Robust Optimization Model to Hedge Price Uncertainty of DSO with Multiple Transactions," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
    11. Shiping Geng & Gengqi Wu & Caixia Tan & Dongxiao Niu & Xiaopeng Guo, 2021. "Multi-Objective Optimization of a Microgrid Considering the Uncertainty of Supply and Demand," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    12. Moradi, Mohammad Hassan & Abedini, Mohammad & Hosseinian, S. Mahdi, 2015. "Improving operation constraints of microgrid using PHEVs and renewable energy sources," Renewable Energy, Elsevier, vol. 83(C), pages 543-552.
    13. Yousef Asadi & Mohsen Eskandari & Milad Mansouri & Andrey V. Savkin & Erum Pathan, 2022. "Frequency and Voltage Control Techniques through Inverter-Interfaced Distributed Energy Resources in Microgrids: A Review," Energies, MDPI, vol. 15(22), pages 1-29, November.
    14. Heidari, Saeed & Hatami, Alireza & Eskandari, Mohsen, 2022. "An intelligent capacity management system for interface converter in AC-DC hybrid microgrids," Applied Energy, Elsevier, vol. 316(C).

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