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Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.

Sultan Hassan Hakmi1, Abdullah M Shaheen2, Hashim Alnami1

  • 1Electrical Engineering Department, College of Engineering, Jazan University, Jazan 45142, Saudi Arabia.

Biomimetics (Basel, Switzerland)
|December 22, 2023
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Summary
This summary is machine-generated.

A new Kepler optimization algorithm (KOA) effectively reduces costs for Combined Heat and Power Units Economic Dispatch (CHPUED) problems. This novel approach outperforms other algorithms in large-scale power systems.

Keywords:
CHPUEDKepler optimization algorithmeconomic dispatchlarge 192-unit systemvalve point loading effect

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Area of Science:

  • Power Systems Engineering
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • Combined Heat and Power Units Economic Dispatch (CHPUED) is a complex, non-convex optimization problem.
  • Minimizing production costs in power systems requires efficient scheduling of heat and power generation.

Purpose of the Study:

  • To design and implement a Kepler optimization algorithm (KOA) for solving the CHPUED problem.
  • To address the challenges posed by valve points impacts in large-scale power systems.

Main Methods:

  • Developed a Kepler optimization algorithm (KOA) inspired by planetary motion.
  • Applied KOA to large-scale systems with 48, 96, and 192 units.
  • Compared KOA performance against DMOA, EVO, GWO, and PSO on a 192-unit system.

Main Results:

  • KOA significantly reduced fuel costs in 48, 96, and 192-unit systems.
  • KOA achieved substantial improvements over DMOA (19.43%), EVO (17.49%), GWO (39.19%), and PSO (62.83%) on the 192-unit system.
  • A feasibility study confirmed KOA's robustness in maintaining operating points within boundaries.

Conclusions:

  • The proposed Kepler optimization algorithm (KOA) is a superior method for solving the CHPUED problem.
  • KOA demonstrates high efficiency and robustness for large-scale power systems with valve point effects.
  • KOA offers significant cost reductions and reliable performance compared to existing optimization techniques.