A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects. (15th October 2016)

• Record Type: Journal Article
• Authors: Secui, Dinu Calin
• Is Part Of: Energy. Volume 113(2016)
• Page Start: 366
• Page End: 384
• Publication Date: 2016-10-15
• Subjects: Economic dispatch -- Modified SOS algorithm -- Valve-point effects -- Transmission losses -- Prohibited zone -- Large-scale EcD problem; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.07.056
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes a new metaheuristic algorithm, called Modified Symbiotic Organisms Search (MSOS) algorithm, to solve the economic dispatch problem considering the valve-point effects, the prohibited operating zones (POZ), the transmission line losses, multi-fuel sources, as well as other operating constraints of the generating units and power system. The MSOS algorithm introduces, in all of its phases, new relations to update the solutions to improve its capacity of identifying stable and of high-quality solutions in a reasonable time. Furthermore, to increase the capacity of exploring the MSOS algorithm in finding the most promising zones, it is endowed with a chaotic component generated by the Logistic map. The performance of the modified algorithm and of the original algorithm Symbiotic Organisms Search (SOS) is tested on five systems of different characteristics, constraints and dimensions (13-unit, 40-unit, 80-unit, 160-unit and 320-unit). The results obtained by applying the proposed algorithm (MSOS) show that this has a better performance than other techniques of optimization recently used in solving the economic dispatch problem with valve-point effects. Highlights: A new modified SOS algorithm (MSOS) is proposed to solve the EcD problem. Valve-point effects, ramp-rate limits, POZ, multi-fuel sources, transmission losses were considered. The algorithm is tested on five systems having 13, 40, 80, 160 and 320 thermal units. MSOS algorithm outperforms many other optimization techniques.