Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System

Abstract

The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system.

The Role of Artificial Intelligence in Tackling Covid-19 (A Review)

“…The technology of deep learning has evolved over the years. This technology has included various concepts, all concerned with the evolution of machine understanding (Bohre et al, 2015). Besides (Jayasinghe, 2021).…”

Assessment of Techno-Socio-Economic Performances of Distribution Network with Optimal Planning of Multiple DGs

“…The renewable sources are very effective as distributed generations (DGs) due to the social, technical and economic advantages [3][4][5][6][7][8][9] . Therefore, the optimum planning of multiple renewable sources based on distributed generations is taken into account as a power source to reduce the power losses of network (active & reactive-losses) on the basis of DGs capability to consume or supply the powers [7][8][9][10][11][12][13][14][15] . The practical load models considering mixed load, commercial, industrial, residential, and constant loads for optimum allocation of different DGs at optimum power-factor are also explored based on multiple objectives appproach [11][12][13][14][15][16][17][18] .…”

“…Therefore, the optimum planning of multiple renewable sources based on distributed generations is taken into account as a power source to reduce the power losses of network (active & reactive-losses) on the basis of DGs capability to consume or supply the powers [7][8][9][10][11][12][13][14][15] . The practical load models considering mixed load, commercial, industrial, residential, and constant loads for optimum allocation of different DGs at optimum power-factor are also explored based on multiple objectives appproach [11][12][13][14][15][16][17][18] . Generally, the constant loads are assumed in distribution system planning, which may mislead system's actual parameters.…”

“…Generally, the constant loads are assumed in distribution system planning, which may mislead system's actual parameters. Hence, the actual load models dependent on the voltage are planned in the system for optimum allocation of distinct type of DG [16][17] . The DG's optimum site and size on the basis of the minimum value of multi-objective function is presented with BF-PSO (butterfly-particle swarm optimization), PSO (particle swarm optimization) and GA (genetic algorithm) optimization techniques 13,[17][18][19][20] .…”

Effect of Load Model Using Ranking Identification Technique for Multi Type DG Incorporating Embedded Meta EP-Firefly Algorithm

“…The voltage dependent load has a main impact on distribution system planning studies. The load models termed as residential, commercial, industrial and mixed loads will give impact to active and reactive intake power and system losses [3] [4].…”