Investigation of Wind Power Uncertainty on Transmission Network Expansion Planning

Abstract

The main goal of transmission expansion planning (TEP) is to develop or reinforce the electrical network to fulfill the future electrical load requirement and to integrate new equipment added to the network. TEP is a major subject in smart grid development, where Demand Response Program (DRP) affect long- and short-term power system decisions, and these in turn, affect TEP. First, this paper discusses the effects of Non-Linear Demand Response Program (NDR) on reducing the final costs of a system in TEP. In order to approach real behavior of the loads, three different types of loads including residual, commercial and office-building have been considered. Real data for wind power is extracted from Khaf, Iran. Using Mont-Carlo and based on Empirical Cumulative Distribution Function (ECDF), 1000 scenarios are produced to study the uncertainty characteristic of wind. As there are a lot of scenarios which are time consuming, Radial Based Neural Network Clustering (RBNNC) is used for decreasing the run-time significantly. Then TEP problem is solved using the Teaching-Learning-Based Optimization (TLBO) and Gray Wolf Optimization (GWO) algorithms in order to minimize the costs of generation, losses, and lines. Simulation results show the optimal effect of NDR and wind on postponing the additional cost of investments for supplying peak load.