Optimal sizing and siting distributed generation resources using a multi objective algorithm

Abstract

Abstract:The restructuring of the electrical market, improvement in the technologies of energy production, and energy crisis have paved the way for increasing applications of distributed generation (DG) resources in recent years. Installing DG units in a distribution network may result in positive impacts, such as voltage profile improvement and loss reduction, and negative impacts, such as an increase in the short-circuit level. These impacts depend on the type, capacity, and place of these resources. Therefore, finding the optimal place and capacity of DG resources is of crucial importance. Accordingly, this paper is aimed at finding the optimal place and capacity of DG resources, in order to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level. The proposed formulation of this paper significantly increases the convergence and the speed of the finding the answers. Furthermore, to select the optimal weighting coefficients, an algorithm is proposed. The weighting coefficients are decided on according to the requirements of each network and deciding on them optimally prevents the arbitrarily selection of these resources.The genetic algorithm is used to minimize the objective function and to find the best answers during the investigation.Finally, the proposed algorithm is tested on the Zanjan Province distribution network in Iran and the simulation results are presented and discussed.

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

“…Distributed generation uncertainties (Zangiabadi et al 2011) have been taken in account for the placement of DG. Alonso et al (2012), Rahim et al (2012), DoagouMojarrad et al (2013) and Hosseini et al (2013) proposed evolutionary algorithms for the placement of distributed generation. Nekooei et al (2013) proposed Harmony Search algorithm with multi-objective placement of DGs.…”

Optimizing DG penetration in distribution networks concerning protection schemes and technical impact

“…Real-Coded Genetic Algorithms (RCGA) is a highly parallel search method applied to direct the population towards convergence at the global optimum solution [14][15][16][17][18][19]. This algorithm requires four basic elements: initial population, evaluation function, selection, and genetic operators (crossover and mutation).…”

A dynamic programing‐based framework for distribution system restoration considering load uncertainties

“…In order to explore the performance of the proposed framework, it has been implemented on a practical sub‐transmission system in Zanjan, Iran . This network whose single‐line diagram is shown in Figure is composed of 16 distribution feeders.…”