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.
Highlights
Over recent years, there has been a sharp growth in both energy consumption and population, whereas the conventional energy source price is increasing and its availability is dwindling [1]
artificial neural network (ANN)-based optimization algorithms are applied to develop the scheduling controller used on the virtual power plants (VPP) system for many reasons, such as because of powerful optimization techniques, good search exploration process, fast convergence for solution compared with other conventional optimization techniques, and resistance to trapping in local minima [26,27,28,29]
ANN was trained and has learned 100% of the VPP system data of the binary backtracking search algorithm (BBSA) best schedule as input data, while the testing data was conducted on another loading data condition
Summary
There has been a sharp growth in both energy consumption and population, whereas the conventional energy source price is increasing and its availability is dwindling [1]. ANN-based optimization algorithms are applied to develop the scheduling controller used on the VPP system for many reasons, such as because of powerful optimization techniques, good search exploration process, fast convergence for solution compared with other conventional optimization techniques, and resistance to trapping in local minima [26,27,28,29]. All those previous algorithms have only focused on training and testing ANNs on similar loading conditions, as well as on the feasibility of implementing these optimization techniques [30]. The rest of the paper is as follows: an overview of the MGs and VPP system, ANN algorithm training, trained loading data results, main results and discussion, and the conclusion
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