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

Maher G. M. Abdolrasol, Mahammad Abdul Hannan, S. M. Suhail Hussain, Taha Selim Ustun, Mahidur R. Sarker and Pin Jern Ker
Additional contact information
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

Energies, 2021, vol. 14, issue 20, 1-19

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.

Keywords: artificial neural network; virtual power plant; scheduling; energy management; multi-microgrids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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