Enhancement of performance indices on realistic load models with distributed generators in radial distribution network

The location and capacity of Distributed Generators (DG) are the two crucial factors that play a vital role in distribution networks towards reducing power losses. Independent implementation of DG injects either active power or reactive power and has attracted the interest of many researchers. However, some researchers have studied this important optimization problem with constant power load model or with the independent implementation of voltage dependent load models. In practical distribution network, loads provided by distribution utilities are mostly ZIP load models. So it is necessary to study the effect of DG on ZIP loads in distribution system to reduce power losses. In this study, an investigation has been performed to observe the effect of ZIP load models on the implementation of DG. The main objective of the study is to reduce the power losses with the implementation of DG considering ZIP load models. In this study, two cases are investigated to reduce the power losses with different scenarios. In Case-A, ten different scenarios are considered to study the effect of ZIP load model on the implementation of DG. Seven different independent ZIP load models have been proposed to study the effect of DG with different performance indices such as real power loss index (RPLI), the reactive power loss index (RePLI), voltage deviation index (VDI) and the real power injected by DG (P-DG). Optimal placement and sizing of DG is a difficult non linear combinatorial optimization problem. Many evolutionary algorithms have been used to reduce the power losses with independent implementation of DG. However, evolutionary algorithms often suffer from some limitations, so quantum inspired evolutionary algorithm (QiEA) is used to overcome these limitations. An adaptive quantum inspired evolutionary algorithm (AQiEA) which is the updated version of QiEA is used to find the optimal location and capacity of DG. In Case-B, effectiveness of the proposed algorithm is tested on two IEEE benchmark test bus system with three different Scenarios. Multi objective function is formulated with real power loss, reactive power loss, voltage deviation index, and total power injected by DG. Tabulated results demonstrate that AQiEA is performing better in all aspects such as real power loss, active power injected by DG & percentage power loss reduction as compared with Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS), Jaya Optimization (JO), Ant Lion Optimization (ALO) and Dragon Fly Optimization (DFO).