Abstract
A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorporated. The results generated by the SpDEA are investigated and compared to standard multi-objective differential evolution (MODE) to prove their viability. The best answer is chosen carefully among trade-off Pareto points by using the technique of fuzzy Pareto solution. Two power system networks such as IEEE 30-bus and 118-bus systems as large-scale optimization problems with 129 design control variables are utilized to point out the effectiveness of the SpDEA. The realized results among many independent runs indicate the robustness of the SpDEA-based approach on OPF methodology in optimizing the defined objectives simultaneously.
Highlights
Till this moment, power networks remain one of the most complicated systems in industry due to many reasons, such as variation of generation and load demand, the inclusion of renewable energy systems, and storage devices
The optimal operation of such components to achieve a concise economic target, emission minimization possibility, power loss reduction and other objectives under the power system constraints plays an important role in the power system operation and is named optimal power flow (OPF) [1,2,3,4,5]
Some fitness functions can be sequentially solved under the system operating conditions, including fuel consumption cost (FC), pollution release rate, active power loss, reactive power loss, bus voltage declines and many more
Summary
Power networks remain one of the most complicated systems in industry due to many reasons, such as variation of generation and load demand, the inclusion of renewable energy systems, and storage devices. Several optimization methods have been used to tackle the OPF problem whether the target is single or multiple objective functions, such as genetic procedures [11], particle swarm optimizer [12], imperialist competitive algorithm [13], chaotic invasive weed optimization algorithms [14], JAYA algorithm [15], shuffled-frog-leaping algorithm [16], social spider optimization algorithm [17], chaotic salp swarm optimizer [18], new adaptive partitioning flower pollination algorithm [19], and others [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38] Among these kinds of literature, as mentioned earlier, the reader can notice that an avalanche of articles is presented to solve OPF problems using differential evolution (DE).
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