Optimal rescheduling of real power generation for congestion management using teaching-learning-based optimization algorithm

• Main Authors: Sumit Verma, Subhodip Saha, V. Mukherjee
• Format: Article
• Language: English
• Published: SpringerOpen 2018-12-01
• Series: Journal of Electrical Systems and Information Technology
• Online Access: http://www.sciencedirect.com/science/article/pii/S2314717216301143

This paper proposes teaching-learning-based optimization (TLBO) algorithm for congestion management (CM) in a pool based electricity market. Congestion is a principal problem that an independent system operator faces in post deregulated era. The aim of employing TLBO algorithm is to effectively relieve congestion in the line with minimum deviation in initial generation and, hence, congestion cost. Various security constraints such as load bus voltage and line loading are taken into account while dealing with this problem. Inspired by teaching–learning process of classroom, TLBO algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. It only requires common control parameters like population size and number of generation. In this paper, the proposed TLBO algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the TLBO algorithm for obtaining higher quality solution is also established. Keywords: Congestion management, Deregulation, Independent system operator (ISO), Optimal power flow, Price bids, Teaching-learning-based optimization (TLBO)