SOCIAL WELFARE MAXIMIZATION UNDER CONTINGENT CONDITIONS IN DEREGULATED MARKET

Abstract

The security evaluation is a crucial task as it gives the knowledge about the system state in the occurrence of a contingency. Contingency analysis technique is being broadly used to predict the consequence of outages like failures of equipment, transmission line etc, and to take necessary actions to keep the power structure safe and reliable. In this report contingency selection has been performed by calculating performance indices for single transmission line outage. The performance indices have been calculated with Newton Raphson Load Flow (NRLF). Further contingency selection has been done with Radial Basis Function (RBF) Neural Network. The effectiveness of the method has been tested on IEEE-14 bus test systems. The ideal objective function is to maximize the net profit of every market participants' i.e. social welfare maximization. The objective function used here is not linear hence to solve this function a nonlinear optimization technique, Particle Swarm Optimization (PSO) is used to get the optimal solution. To facilitate fair competition among various stack holders in power market, there is a strong need of congestion management to alleviate congestion. Congestion in the transmission lines is one of the technical problems, affecting the market participants the most, in the deregulated environment. In this report congestion is relieved by Thyristor Controlled Series Compensator (TCSC) with social welfare being the objective.