Energy distribution network reconfiguration in order to loss reduction using Grey Wolf Optimizar algorithm

Due to changes in the electricity industry and privatization of distribution networks,efficient utilization of network combined with cost reduction as the most important priority isthe electricity distribution companies. Since the distribution network is considered to be thebridge between consumers and the power grid, it is essential to enhance network performanceand deliver high-quality power to subscribers. Distribution networks have the highest lossesamong the three power system components, including generation, transmission anddistribution, due to low voltage levels and consequently high flow throughputs. Due to the highnetwork failure factors and, in addition to the generally radial structure of the network, thereliability of these networks is lower compared to other parts of the power system. For thispurpose, the reconfiguration of the distribution network is a simple and inexpensive way to useexisting facilities without the need for any new equipment in distribution networks for variouspurposes such as: reducing losses, reducing feeder overload and creating load balances,improving Voltage profile, reduce the number of switches & etc. For this reason, in thisresearch, the problem of reconfiguration the distribution network has been formulated as anonlinear optimization problem. The proposed solution for this problem is solved in theframework of the problem of complex nonlinear optimization and switching states using metaheuristicmethods called gray wolf algorithm, bacterial forging algorithm and particle swarmoptimization In order to reduce the losses of lines, the improvement of the voltage profile andthe power transmission lines are solved. This program has been tested on test systems 33 and69 bus. The results prove that the heuristic algorithms used are better than other availablemethods.

کلیدواژه ها:

power distribution network ، reconfiguration ، reliability ، improving voltage profile ، reduction losses ، meta-heuristic algorithm ، Grey Wolf Optimizer