Integrated Evolutionary Path-Finder Optimization Technique for Dynamic Economic Dispatch

Authors

  • Muhammad Haiqal Ghani, Ismail Musirin, Siti Rafidah Abdul Rahim, Muhamad Hatta Hussain, S. S. Sivaraju, A. V. Senthil Kumar, Nor Laili Ismail

DOI:

https://doi.org/10.17762/msea.v71i3.207

Abstract

Dynamic economic dispatch (DED) is considered as the main element in the making plans of the energy system for achieving optimum operation and control of the power system. At certain duration of time, it measures or predicts the optimum operation of generating units at any planned load requirement. Any unpredictable events, such as unit failures and shifts in demand are faced by the real-life power grid. Some meta-heuristic optimization methods have been used successfully to solve complex economic dispatch problems using meta-heuristic without any need for some mathematical characteristics. Several optimization techniques are employed to solve the ED problems. However, some techniques are not reliable enough to achieve the optimal solution. Thus, this paper proposes a new meta-heuristic technique, termed the Integrated Evolutionary path- finder optimization (IEPFO) technique to solve DED problems. In this study, Evolutionary Programming (EP), Pathfinder Algorithm (PFA) and IEPFO optimization engines are developed. Validation process was performed on the IEEE 26-bus reliability test system (RTS) model. Comparative studies have been conducted to reveal the merit of the proposed IEPFO over the traditional EP and the PFA techniques, implying its merit over the others.

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Published

2022-06-09

How to Cite

Muhammad Haiqal Ghani, Ismail Musirin, Siti Rafidah Abdul Rahim, Muhamad Hatta Hussain, S. S. Sivaraju, A. V. Senthil Kumar, Nor Laili Ismail. (2022). Integrated Evolutionary Path-Finder Optimization Technique for Dynamic Economic Dispatch. Mathematical Statistician and Engineering Applications, 71(3), 692 –. https://doi.org/10.17762/msea.v71i3.207

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