Differential Evolution algorithm for contingency analysis-based optimal location of FACTS controllers in deregulated electricity market

• Published in: Soft computing (Berlin, Germany) Germany), 2019-01, Vol.23 (1), p.163-179
• Main Authors: Balamurugan, K., Muthukumar, K.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Buses; Computational Intelligence; Contingency; Control; Deregulation; Electricity; Engineering; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Optimization; Overloading; Power flow; Power supply; Reactive power; Robotics; Scaling factors; Suppliers
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-018-3141-x

In this paper, a novel heuristic optimization algorithm called differential evolution (DE) technique has been proposed to solve the optimal location of FACTS devices in deregulated electricity market using contingency analysis. The proposed approach deals with the optimal injection of real and reactive power using FACTS devices with an aim to minimize the total system cost, the total number of overloads, excess power flow, the severity of overload and real power loss of the network. To enhance the search behavior of the DE approach, the scaling factor and crossover parameters are empirically selected. To show the superiority of the proposed DE approach and as a step ahead to prove its novelty, the same optimization problem is solved using evolutionary programming method, and the results are compared. The results indicate the proposed DE algorithm is superior in terms of final solution quality, efficiency, convergence rate and robustness. A sample four-bus system and 24-bus EHV southern region of Indian grid system has been considered to illustrate the proposed methodology.