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Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

Author

Listed:
  • Masoud Dashtdar

    (Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr 7515895496, Iran)

  • Aymen Flah

    (National Engineering School of Gabès, Processes, Energy, Environment and Electrical Systems, University of Gabès, LR18ES34, Medinine 6072, Tunisia)

  • Seyed Mohammad Sadegh Hosseinimoghadam

    (Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr 7515895496, Iran)

  • Hossam Kotb

    (Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21526, Egypt)

  • Elżbieta Jasińska

    (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

  • Radomir Gono

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708-00 Ostrava, Czech Republic)

  • Zbigniew Leonowicz

    (Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

  • Michał Jasiński

    (Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

Abstract

An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%.

Suggested Citation

  • Masoud Dashtdar & Aymen Flah & Seyed Mohammad Sadegh Hosseinimoghadam & Hossam Kotb & Elżbieta Jasińska & Radomir Gono & Zbigniew Leonowicz & Michał Jasiński, 2022. "Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony," Sustainability, MDPI, vol. 14(11), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6759-:d:829350
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    References listed on IDEAS

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    Cited by:

    1. Yi Zhang & Tian Lan & Wei Hu, 2023. "A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response," Sustainability, MDPI, vol. 15(19), pages 1-22, October.
    2. Priyadharshini Ramu & Sivasankar Gangatharan & Sankar Rangasamy & Lucian Mihet-Popa, 2023. "Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique," Sustainability, MDPI, vol. 15(16), pages 1-28, August.
    3. Tuyen Nguyen-Duc & Linh Hoang-Tuan & Hung Ta-Xuan & Long Do-Van & Hirotaka Takano, 2022. "A Mixed-Integer Programming Approach for Unit Commitment in Micro-Grid with Incentive-Based Demand Response and Battery Energy Storage System," Energies, MDPI, vol. 15(19), pages 1-26, September.

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