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A genetic algorithm optimization approach for smart energy management of microgrids

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  • Torkan, Ramin
  • Ilinca, Adrian
  • Ghorbanzadeh, Milad

Abstract

Optimal management and planning of microgrids (MG) are the most important goals for operators. In this study, a Multiobjective Genetic Algorithm (MOGA) is applied to the technical and economic problems of the MG. This stochastic programming considers demand response (DR) programs, reactive loads, and uncertainties due to renewable energies. Demand-side management (DSM) is how to manage and schedule the generation and consumption with the objective of cost and greenhouse gases (GHG) emissions minimization. In this work, with the contribution of various customers to demand response programs and reserve schedules, a reduction in the operation cost of the microgrid has resulted. This method facilitates obtaining a complete and comprehensive microgrid model for energy management in the power system, and the results demonstrate that participation in demand response programs and reactive loads can reduce generation, reservation, startup costs, and the amount of pollution. Regarding reservation costs, a 16% reduction was obtained in the presence of the load response, and wind power is a good compromise between cost and pollution among various resources.

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  • Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    • Handle: RePEc:eee:renene:v:197:y:2022:i:c:p:852-863
    DOI: 10.1016/j.renene.2022.07.055
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    2. Pei Lu & Zheng Liang & Xianglong Luo & Yangkai Xia & Jin Wang & Kaihuang Chen & Yingzong Liang & Jianyong Chen & Zhi Yang & Jiacheng He & Ying Chen, 2023. "Design and Optimization of Organic Rankine Cycle Based on Heat Transfer Enhancement and Novel Heat Exchanger: A Review," Energies, MDPI, vol. 16(3), pages 1-34, January.
    3. 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).
    4. Mahshid Javidsharifi & Najmeh Bazmohammadi & Hamoun Pourroshanfekr Arabani & Juan C. Vasquez & Josep M. Guerrero, 2023. "Environmental–Economic Analysis of Multi-Node Community Microgrid Operation in Normal and Abnormal Conditions—A Case Study of Indonesia," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
    5. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    6. Wisam Kareem Meteab & Salwan Ali Habeeb Alsultani & Francisco Jurado, 2023. "Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer," Energies, MDPI, vol. 16(16), pages 1-18, August.

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