Bacterial Foraging Algorithm & Demand Response Programs for a Probabilistic Transmission Expansion Planning With the Consideration of Uncertainties and Voltage Stability Index

Abstract

The rapid growth of the power system with respect to many uncertainties has made the transmission expansion planning (TEP) problem more tedious. This article proposes a novel holistic method to solve the TEP problem in the deregulated market environment considering uncertainties at the load side. A mixed-integer nonlinear programming model has been considered in solving the raised issue. A bacterial foraging algorithm has been employed to optimize the problem. Considering the nature of the power system, applying the ac power flow model is a necessity in optimizing the problem in order to obtain applicable results. Distributed generations (DGs) have been included at the load side to satisfy the variation in the demand. Demand response programs (DRPs) have been considered to reduce the cost and increase the closeness between customers and the utility side. Furthermore, this work uses the Monte Carlo simulation (MCS) to handle the uncertainties associated with DGs and DRPs at the load side. A voltage stability index study through PQVSI is carried out to ensure the applicability and stability of the considered plan. A comprehensive planning framework is obtained by testing the proposed method on the Colombian 93-bus test system. The use of DGs and DRPs has a significant impact on reducing the overall expansion plan of the network.