A new intelligent decision-maker method determining the optimal connection point and operating conditions of hydrogen energy-based DGs to the main grid |
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| Affiliation: | 1. Bursa Technical University, Smart Grid Lab., Department of Electrical and Electronics Engineering, 16300, Bursa, Turkey;2. TEIAS 2nd Regional Directorate Facility and Control Chief Engineering, Bursa, Turkey;1. Department of Chemical Engineering, Kocaeli University, 41001, Kocaeli, Türkiye;2. AYARGEM, Alternative Fuels R&D Center, Kocaeli University, 41001, Kocaeli, Türkiye;1. Gazi University, Department of Energy Systems Engineering, Ankara, Turkey;2. Ni?anta?? University, Department of Industrial Engineering, Istanbul, Turkey;1. Canakkale Onsekiz Mart University, Faculty of Science and Arts, Department of Chemistry, Terzioglu Campus, Canakkale, 17100, Turkey;2. Nanoscience and Technology Research and Application Center, Canakkale Onsekiz Mart University Terzioglu Campus, 17100, Canakkale, Turkey;3. Hacettepe University, Faculty of Science, Department of Chemistry, Beytepe Campus, 06800, Ankara, Turkey;4. Department of Chemical and Biomolecular Engineering, University of South Florida, Tampa, FL, 33620, USA;5. Department of Ophthalmology, Morsani College of Medicine, University of South Florida, 12901 B. Downs Blvd., MDC 21, Tampa, FL, 33612, USA;1. ?skenderun Technical University, Faculty of Engineering and Natural Science, Mechatronics Department, Hatay, Turkey;2. Gaziantep University, Engineering Faculty, Mechanical Engineering Department, Gaziantep, Turkey;3. ?skenderun Technical University, Faculty of Engineering and Natural Science, Mechanical Eng. Department, Hatay, Turkey;4. Ministry of Energy and Natural Resources, Ankara, Turkey;1. Bursa Technical University, Faculty of Engineering and Natural Sciences, Department of Mechanical Engineering, Mimar Sinan Campus, Bursa, 16310, Turkey;2. Istanbul Technical University, Energy Institute, Maslak, TR-34469, Istanbul, Turkey |
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| Abstract: | This study presents a new two-step intelligent decision-maker method using hydrogen energy-based distributed generators (HEDGs) to contribute to the reliability, durability, and stability of power transmission system in Bursa. In the first stage, the proposed method uses the power flow parameters evaluation (PFPE) algorithm to define the possible appropriate connection point of HEDGs by determining the electrical parameters. Then, to determine the conditions in which the HEDGs connected to the grid should be switched on, the power flow data such as load status, bus bar powers, and, line capacities are evaluated with the artificial neural network (ANN)-based method with a scaled conjugate gradient (SCG) algorithm. With the proposed intelligent two-step decision-maker method, HEDGs are connected to the points determined using the PFPE algorithm, and then the appropriate operating conditions for which HEDGs should be enabled are determined by the ANN with SCG. Different combinations of load status, bus bar powers, and line capacities values are applied to the ANN input and important features are determined. The ANN with SCG can predict the operating conditions of HEDGs with 96.8% accuracy in the test set and, 98.4% accuracy in the validation set. Thanks to the developed holistic PFPE&ANN approach, optimum connection points and suitable operating conditions can be determined, which ensures reliability and safety for HEDGs in overload and/or failure conditions. |
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| Keywords: | Hydrogen energy Energy management Neural network Grid integration Distributed generation |
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