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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Generic multidimensional economic environmental operation of power systems using equilibrium optimization algorithm.

Mohamed T Mouwafi1, Adel A Abou El-Ela1, Amany A El-Hamoly1

  • 1Electrical Engineering Department, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt.

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|May 16, 2025
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Summary
This summary is machine-generated.

The Equilibrium Optimization Algorithm (EOA) effectively minimizes fuel costs and emissions in power systems. This new method outperforms existing techniques, especially for large-scale economic emission load dispatch problems.

Keywords:
Economic emission load dispatchEmissionEquilibrium optimization algorithmGeneration cost functionValve-points effects

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Area of Science:

  • Electrical Power Systems Engineering
  • Optimization Algorithms
  • Environmental Engineering

Background:

  • The economic emission load dispatch (EELD) problem is critical for power system operators, complicated by interconnected systems and non-linear objective functions (OFs).
  • Balancing fuel costs and emissions is a significant challenge due to system complexity and operational constraints.

Purpose of the Study:

  • To introduce and evaluate the Equilibrium Optimization Algorithm (EOA) for solving the EELD problem.
  • To minimize total fuel cost and emissions simultaneously, considering practical constraints like valve point effects (VPE) and transmission losses.

Main Methods:

  • Developed the Equilibrium Optimization Algorithm (EOA), inspired by fluid dynamics, where agents represent particles and concentrations represent positions.
  • Applied EOA to multi-objective EELD problems, optimizing fuel cost and emissions with and without VPE and transmission losses.
  • Tested EOA on standard systems (10, 20, 40, 80 units) and a large-scale 140-unit system, comparing results with PSO, GWO, and DE algorithms.

Main Results:

  • EOA achieved significant reductions in total fuel cost (up to 5.84% without VPE, 3.62% with VPE) and emissions (up to 12.87%).
  • For the 140-unit system, EOA reduced fuel cost by over 7% and emissions by 2.57%, demonstrating superior performance on large-scale systems.
  • Statistical analysis and Wilcoxon signed rank tests confirmed EOA's superiority and reliability compared to other optimization techniques.

Conclusions:

  • The proposed EOA is a highly accurate and efficient method for solving the economic emission load dispatch problem.
  • EOA demonstrates robust performance, especially for large-scale power systems with complex operational constraints.
  • The multi-objective EOA (MOEOA) effectively finds Pareto optimal solutions, balancing competing economic and environmental objectives.