Incorporating Large-Scale Distant Wind Farms in Probabilistic Transmission Expansion Planning—Part I: Theory and Algorithm

Abstract

With increment in the penetration of wind energy in power systems, the necessity of considering its impacts on transmission expansion planning (TEP) studies, especially for large scale wind farms, is inevitable. A new multi-objective (MO) optimization transmission expansion planning algorithm considering wind farm generation is presented in this two-paper set. Part I is mainly devoted to derivation of the theory and algorithm. The objective functions used in the TEP studies take into account investment cost, risk cost and congestion cost. The combination of Monte Carlo simulation (MCS) and Point Estimation Method (PEM) is implemented to investigate the effects of network uncertainties. Due to its comparative assessment potential and good handling of the non-convex problems and non-commensurable objective functions, the Non-Dominated Sorting Genetic Algorithm II (NSGA II) is widely used for evaluating the MO optimization problem. Eventually, for selecting the final optimal solution, a Fuzzy decision making approach is applied based on decision maker preferences.