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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Comprehensive analysis of optimal power flow using recent metaheuristic algorithms.

Ahmed A Zaki Diab1, Ashraf M Abdelhamid2, Hamdy M Sultan3

  • 1Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt. a.diab@mu.edu.eg.

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Summary

This study introduces six metaheuristic algorithms, including the Gradient-Based Optimizer (GBO), to solve the optimal power flow (OPF) problem efficiently. The Gradient-Based Optimizer demonstrated superior performance in solving complex power system optimization challenges.

Keywords:
EnergyFuel costMetaheuristicsOptimal power flowVoltage profileVoltage stability

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence
  • Optimization Techniques

Background:

  • The optimal power flow (OPF) problem is critical for efficient power system operation.
  • Existing metaheuristic algorithms face challenges in solving complex OPF formulations.
  • Standard and conservative operating conditions require robust optimization methods.

Purpose of the Study:

  • To introduce and evaluate six metaheuristic algorithms for solving the optimal power flow (OPF) problem.
  • To assess the effectiveness and robustness of these algorithms under various operating conditions.
  • To propose a novel comparison methodology for evaluating optimization techniques.

Main Methods:

  • Implementation of six metaheuristic algorithms: Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic Control Cuckoo Search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), and Opposition Flow Direction Algorithm (OFDA).
  • Modeling the OPF problem with diverse objectives, constraints, and formulations.
  • Conducting case studies on IEEE 30-bus and IEEE 118-bus standard test systems.
  • Developing a performance evaluation procedure and a new comparison methodology.

Main Results:

  • The Gradient-Based Optimizer (GBO) showed significant potential in efficiently solving various OPF problems.
  • The proposed algorithms were evaluated for their effectiveness and robustness on standard test systems.
  • The new comparison methodology facilitated a thorough assessment of the optimization techniques.

Conclusions:

  • The Gradient-Based Optimizer (GBO) is a highly effective algorithm for addressing the optimal power flow (OPF) problem.
  • The study validates the robustness of the proposed metaheuristic algorithms on benchmark power systems.
  • The developed comparison framework offers a valuable tool for future research in power system optimization.