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Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

Author

Listed:
  • Maher G. M. Abdolrasol

    (Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Mahammad Abdul Hannan

    (Department of Electrical Power Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • S. M. Suhail Hussain

    (Fukushima Renewable Energy Institute, AIST (FREA), National Institute of Advanced Industrial Science and Technology (AIST), Koriyama 963-0298, Japan)

  • Taha Selim Ustun

    (Fukushima Renewable Energy Institute, AIST (FREA), National Institute of Advanced Industrial Science and Technology (AIST), Koriyama 963-0298, Japan)

  • Mahidur R. Sarker

    (Institute of IR 4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Pin Jern Ker

    (Department of Electrical Power Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

Abstract

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency.

Suggested Citation

  • Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6507-:d:653510
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    3. Harpreet Sharma & Sachin Mishra & Javed Dhillon & Naveen Kumar Sharma & Mohit Bajaj & Rizwan Tariq & Ateeq Ur Rehman & Muhammad Shafiq & Habib Hamam, 2022. "Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector," Energies, MDPI, vol. 15(3), pages 1-21, January.
    4. Ziad M. Ali & Martin Calasan & Shady H. E. Abdel Aleem & Francisco Jurado & Foad H. Gandoman, 2023. "Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review," Energies, MDPI, vol. 16(16), pages 1-41, August.
    5. Taha Selim Ustun, 2022. "Power Systems Imitate Nature for Improved Performance Use of Nature-Inspired Optimization Techniques," Energies, MDPI, vol. 15(17), pages 1-2, August.
    6. Preeti Ranjan Sahu & Rajesh Kumar Lenka & Rajendra Kumar Khadanga & Prakash Kumar Hota & Sidhartha Panda & Taha Selim Ustun, 2022. "Power System Stability Improvement of FACTS Controller and PSS Design: A Time-Delay Approach," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    7. Ganesh Sampatrao Patil & Anwar Mulla & Subhojit Dawn & Taha Selim Ustun, 2022. "Profit Maximization with Imbalance Cost Improvement by Solar PV-Battery Hybrid System in Deregulated Power Market," Energies, MDPI, vol. 15(14), pages 1-21, July.
    8. Sheikh Safiullah & Asadur Rahman & Shameem Ahmad Lone & S. M. Suhail Hussain & Taha Selim Ustun, 2022. "Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems," Sustainability, MDPI, vol. 14(21), pages 1-27, November.
    9. Upasana Lakhina & Irraivan Elamvazuthi & Nasreen Badruddin & Ajay Jangra & Bao-Huy Truong & Joseph M. Guerrero, 2023. "A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid," Sustainability, MDPI, vol. 15(8), pages 1-25, April.

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