Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System

Abstract

Distribution system supplies power to variety of load depending upon the consumer’s demand, which is increasing day by day and lead to high power losses and poor voltage regulation. The increase in demand can be met by integrating Distributed Generators (DG) into the distribution system. Optimal location and capacity of DG plays an important role in distribution network to minimize the power losses. Some researchers have studied this important optimization problem with constant power load which is independent of voltage. However, majority of consumers at load center uses voltage dependent load models, which are primarily dependent on magnitude of supply voltage. In practical distribution network, the assumption of constant power load can significantly affect the location and size of DG, which in turn can lead to higher power losses and poor voltage regulation. In this study, an investigation has been performed to find the increase in power loss due to the use of inappropriate load models, while solving the optimization problem. Furthermore, an attempt has been made in this study to reduce power losses occurring in large test bus systems with loads being dependent on voltage rather than the constant power load. Different test cases are created to analyse the power losses with appropriate load model and in-appropriate load model (constant power load model). The load at distribution network is not mainly dependent on any single type of load model, it is a combination of all load models.  In this study, a class of mix load viz., combination of residential, industrial, constant power, and commercial load, is also considered. In order to solve this critical combinatorial optimization problem with voltage dependent load model, which requires an extensive search, Adaptive Quantum inspired Evolutionary Algorithm (AQiEA) is used. The proposed algorithm uses entanglement and superposition principles, which does not require an operator to avoid premature convergence and tuning parameters for improving the convergence rate. A Quantum Rotation inspired Adaptive Crossover operator has been used as a variation operator for a better convergence. The effectiveness of AQiEA is demonstrated and computer simulations are carried out on two standard benchmark large test bus systems viz., 85 bus system and 118 bus system. In addition to AQiEA, four other algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), and Ecogeography-based Optimization (EBO) with Classification based on Multiple Association Rules (CMAR)) have also been employed for comparison. Tabulated results show that the location and size of DGs determined using in-appropriate load model (constant power load model) has significantly high power losses when applied in distribution system with different load model (other voltage dependent load models) as compared with the location and size of DGs determined using the appropriate load model. Experimental results indicate that AQiEA has a better performance compared to other algorithms which are available in the literature.