Optimal Power Flow using Glowworm Swarm Optimization

• Published in: International journal of electrical power & energy systems 2016-09, Vol.80, p.128-139
• Main Authors: Surender Reddy, Salkuti, Srinivasa Rathnam, Ch
• Format: Article
• Language: English
• Subjects: Algorithms; Buses (vehicles); Cost engineering; Electric power generation; Emission; Evolutionary algorithms; Generation cost; Minimization; Multi-Objective Optimization; Optimal Power Flow; Optimization; Power flow; Swarm intelligence; Transmission loss
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2016.01.036

•Classical methods have limited capability to solve practical large scale OPF problems.•Swarm Intelligence based algorithms are developed to overcome the limitations of conventional classical methods.•OPF problem is solved with generation cost and emission minimizations as objective functions.•To get the best compromise solution a multi-objective OPF is proposed.•PSO and GSO algorithms are used to solve single and multi-objective based OPF problems. An important objective of the Optimal Power Flow (OPF) problem is to minimize the generation cost and keep the power outputs of generators, bus voltages, bus shunt reactors/capacitors and transformer tap settings in their secure limits. Solving this OPF problem using classical methods suffer from the disadvantages of highly limited capability to solve the practical large scale power system problems. To overcome the inherent limitations of conventional optimization techniques, Swarm Intelligence (SI) methods have been developed. However, the environmental concern, dictate the minimization of emissions of the thermal plants. Individually, if one objective is optimized, other objective is compromised. Hence, Multi-Objective Optimal Power Flow (MO-OPF) problem has been formulated in this paper. Swarm Intelligence methods, such as Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO) have been used to solve the OPF problem with generation cost and emission minimizations as objective functions. The effectiveness of the proposed algorithms are tested on IEEE 30 bus and practical Indian 75 bus systems for cost minimization as objective function, and IEEE 30 bus test system for minimization of cost and emission as objectives. The results obtained from both the networks, the PSO and GSO are compared with each other based on different parameters.