Optimal Power Flow of power systems using Harris Hawks Optimization and Salp Swarm Algorithm
Optimal Power Flow (OPF) is one of the most significant tools used over a decade to till date in energy management system for reliable operation and planning of modern power system. The main objective is to adjust all the controlling parameters by satisfying equality and inequality constraints in or...
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/93110/1/FK%202021%2067%20IR.pdf |
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| Summary: | Optimal Power Flow (OPF) is one of the most significant tools used over a decade to till date in energy management system for reliable operation and planning of modern power system. The main objective is to adjust all the controlling parameters by satisfying equality and inequality constraints in order to optimize several objective functions. The recent deregulation of power industry, growing energy demand, limitations of extension of existing transmission and distribution line have intensified the acute implementation of optimization techniques. Moreover, the deploying available natural resources and ever-increasing concern of the environmental pollutant gases, such as CO2, emission during power generations and its serious impact on environment has gained more attention. This thesis has proposed recently developed Harris Hawks Optimization (HHO) and Salp Swarm Algorithm (SSA) to solve single- and multi-objective OPF problems considering fuel cost, power loss and environment emission. Additionally, the proposed methods solved multi-objective OPF problem with the help of no preference weighted sum method. Standard IEEE-30-bus and 57-bus test system data have been studied to justify the effectiveness of the proposed methods for single- and multi-objective OPF problems considering fuel cost, power loss and environment emissions. Additionally, no preference weighted sum method has been employed to solve multi-objective OPF problems simultaneously. The obtained results showed the decent improvement comparing to other swarm-based techniques like Whale Optimization Algorithm (WOA), Math Flame (MF), and Glowworm Optimization Algorithm (GOA) in terms of convergence performance and quality. As per the results, the proposed HHO technique outperforms to give the fuel cost of 801.829$/h improving by 0.01% indicating the best optimal solution for single-objective solution among the various methods presented. Likewise, power loss and environment emission improved predominantly by 0.37 % and 3.72% respectively. Multi-objective OPF results recorded at 0.02% to 0.55% for different cases. Lastly, three objectives were scrutinized together where the performance increased by 0.45% comparing to benchmark method. |
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