Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market

• Published in: Complexity (New York, N.Y.) Vol. 21; no. S1; pp. 90 - 98
• Main Authors: Jalili, Aref, Ghadimi, Noradin
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.09.2016
• Subjects: Algorithms; Case studies; Congestion; Decision making; Evolutionary algorithms; fuzzy mechanism; harmony search algorithm; Multiple objective analysis; operating limits; optimal congestion management; Optimization; Pareto optimum; Pricing; Search algorithms
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1002/cplx.21713

A hybrid technique for solving the congestion management problem in an electricity market based on harmony search algorithm (HAS) and Fuzzy mechanism is presented in this article. This algorithm does not require initial value setting for the variables and does not require differential gradients, thus it can consider discontinuous functions as well as continuous functions. The HAS is a recently developed powerful evolutionary algorithm, inspired by the improvisation process of musicians, for solving single/multiobjective optimization problems. In the proposed technique, each musician plays a note for finding a best harmony all together. Transmission pricing and congestion management are the key elements of a competitive electricity market based on direct access. They also focus of much of the debate concerning alternative approaches to the market design and the implementation of a common carrier electricity system. This article focuses on the tradeoffs between simplicity and economic efficiency in meeting the objectives of a transmission pricing and congestion management scheme. The effectiveness of the proposed technique is applied on 30 and 118 bus IEEE standard power system in comparison with CPSO, PSO‐TVAC, and PSO‐TVIW. The numerical results demonstrate that the proposed technique is better and superior than other compared methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 90–98, 2016