Optimal Power Flow using Glowworm Swarm Optimization |
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| Affiliation: | 1. Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India;2. Department of Electrical Engineering, Dr. B C Roy Engineering College, Durgapur, West Bengal, India;1. Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai 600077, India;2. School of Electrical Engineering, VIT University, Chennai Campus, Chennai 600127, India;1. Constantine Electrical Engineering Laboratory, LEC, Department of Electrical Engineering, University of Constantine 1, 25000 Constantine, Algeria;2. Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;1. Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia;2. Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt;3. Department of Electrical Engineering, Faculty of Engineering, AL-Azhar University, Cairo, Egypt |
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| Abstract: | 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. |
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| Keywords: | Optimal Power Flow Generation cost Emission Transmission loss Multi-Objective Optimization Evolutionary algorithms |
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