Optimal Planning Of Distributed Generation Considering Time Varying Load - Ahmad Syahmi Aiman, Mohd Zuri (2022)
Renewable energy-based Distributed Generation (DG) resources are critical for long-term energy infrastructure since they are non-polluting and limitless. The uncertainties connected with DG resources may result in specific economic and technological issues that need a thorough examination to simplify their integration into the Distribution System (DS). This thesis examines the influence of time-varying load modeling on photovoltaic (PV)-based DG planning. The purpose of this thesis is to present an effective combination method based on Backward Forward Sweep Power Flow (BFSPF) and Mix Integer Optimization by Genetic Algorithm (MIOGA) to solve the network problem in the presence of DG with the purpose of developing power flow for radial distribution system by considering time-varying load, optimize the sitting and sizing of the DG in the radial distribution network for the loss minimization and voltage improvement and make comparison radial distribution network with single and multi-DG by considering time varying load condition. In a radial distribution network, MIOGA is utilized to find the best value for DG size concurrently. The effect of a technique based on the MIOGA algorithm to find the minimal configuration on network actual power losses and voltage profiles is examined. The performance and effectiveness of the recommended technique are demonstrated using the IEEE 33- bus test system. With the installation of PVDG, the simulation results show a reduction in overall power loss and an improvement in voltage magnitudes for the network. PVDG installation can minimize power loss by up to 33%, according to the findings. Aside from lowering losses, installing DG with MIOGA also helps to enhance the voltage profile of the radial distribution network. According to the results, the MIOGA algorithm is best at reducing actual power loss and improving voltage profiles, and it may be utilized to make distribution network planning decisions.