Optimum Siting and Sizing of Distributed Generations in Radial and Networked Systems
Abstract This article presents a genetic algorithm-based method to determine optimal location and size of the distributed generations to be placed in radial, as well as networked, systems with an objective to minimize the power loss. Several simulation studies have been conducted on radial feeders, as well as networked systems, considering single-distributed generation and multiple-distributed generations separately to minimize the power loss of the system subjected to no voltage violation at any of the buses. Simulation results are given, and the results are compared with the results of Wang and Hashem Nehrir [“Analytical approaches for optimal placement of distributed generation sources in power systems,” IEEE Trans. Power Syst., Vol. 19, No. 4, pp. 2068–2076, November 2004] and Gozel et al. [“Optimal placement and sizing of distributed generation on radial feeder with different static load model,” Proc. of IEEE International Conference on Future Power Systems (EPS 2005), pp. 1–6, Amsterdam, The Netherlands, 16–18 November 2005] to verify the proposed method.
Keywords :genetic algorithm, distributed generation, optimal location, optimal size, power loss, distribution networks
Abstract—1 Due to environmental concerns2interests in using renewable based distributed generation (DG) have been increased. Because stochastic nature of renewable energy resources such as wind speed and solar radiation, their effects on power system are different from dispachable distributed generation. In this paper, a method for distribution system planning and development is presented in presence of distributed generation with consideration of uncertainties. These uncertainties consist of uncertainties in power generation, failure rates and repair times of equipments as well as uncertainties in electricity market price. The proposed approach is based on optimization model including power loss reduction and reliability improvement. Stochastic generation of renewable DG is modeled by Monte Carlo Simulation (MCS). Uncertainties in failure rates, repair times and electricity price are calculated using fuzzy theory concepts. Also objective values are calculated in Monte Carlo Simulation and optimization is done by genetic algorithm (GA). Index Terms—Distributed generation, Renewable energy, Uncertainty, Risk, Loss, Reliability.