A new intelligent decision-maker method determining the optimal connection point and operating conditions of hydrogen energy-based DGs to the main grid

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, op-timum connection points and suitable operating conditions can be determined, which ensures reliability and safety for HEDGs in overload and/or failure conditions. & COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

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.The proposed method evaluates power flow parameters to determine the appropriate connection point of HEDGs in the first stage, and then uses artificial neural network (ANN) with scaled conjugate gradient (SCG) algorithm to determine the conditions in which the HEDGs should be switched on.