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<p>This paper tackles the Optimum Reactive Power Dispatch (ORPD) problem, a key aspect of Optimal Power Flow, with the aim of minimizing real power losses and voltage deviations while satisfying operational constraints. With the growing reliance on renewable energy sources (RES) such as wind and solar, managing reactive power has become increasingly critical for maintaining voltage stability. Unlike conventional methods that focus solely on thermal generators, this study incorporates uncertainty in load demand and RES generation using probability density functions. A Monte Carlo simulation is used to generate multiple scenarios, followed by scenario reduction to keep computation efficient. To address both deterministic and stochastic versions of the ORPD problem, a hybrid metaheuristic approach combining Artificial Ecosystem-Based Optimization (AEO) and Moth Flame Optimization (MFO) is proposed. Four operating cases are examined on the IEEE 30-bus system: power loss minimization (Case A), voltage deviation minimization (Case B), and their robustness variants with modified generator settings (Case A1 for power loss minimization and Case B1 for voltage deviation minimization). Similarly, on the IEEE 57-bus system, Cases C and D address the same objectives, while Cases C1 and D1 evaluate robustness by setting three thermal units to zero active power. The hybrid AEO–MFO algorithm is benchmarked against several well-known techniques, showing faster convergence and better performance. Results from the IEEE 30-bus and 57-bus test systems confirm the effectiveness of the proposed method. The lowest power losses for Cases (A) and (A1) are 4.4138&#xa0;MW and 4.862871&#xa0;MW, respectively, using the proposed technique. In contrast, the best voltage deviation values were obtained in Cases (B) and (B1), with values of 0.0907 and 0.092257 per unit, respectively, in the IEEE 30-bus system. Similarly, in the IEEE 57-bus system, the lowest power losses for Cases (C) and (C1) are 18.4883&#xa0;MW and 23.3865&#xa0;MW, respectively. Additionally, minimal voltage deviation values of 0.6431 and 0.6138 per unit were achieved in Cases (D) and (D1), respectively. These outcomes demonstrate that the hybrid algorithm effectively solves complex ORPD problems and outperforms existing methods.</p>

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Hybrid AEO–MFO for optimal reactive power dispatch: addressing time-varying load demand and uncertainty in renewable energy sources

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Amal Amin Mohamed,
  • Salah Kamel,
  • Mohamed H. Hassan

摘要

This paper tackles the Optimum Reactive Power Dispatch (ORPD) problem, a key aspect of Optimal Power Flow, with the aim of minimizing real power losses and voltage deviations while satisfying operational constraints. With the growing reliance on renewable energy sources (RES) such as wind and solar, managing reactive power has become increasingly critical for maintaining voltage stability. Unlike conventional methods that focus solely on thermal generators, this study incorporates uncertainty in load demand and RES generation using probability density functions. A Monte Carlo simulation is used to generate multiple scenarios, followed by scenario reduction to keep computation efficient. To address both deterministic and stochastic versions of the ORPD problem, a hybrid metaheuristic approach combining Artificial Ecosystem-Based Optimization (AEO) and Moth Flame Optimization (MFO) is proposed. Four operating cases are examined on the IEEE 30-bus system: power loss minimization (Case A), voltage deviation minimization (Case B), and their robustness variants with modified generator settings (Case A1 for power loss minimization and Case B1 for voltage deviation minimization). Similarly, on the IEEE 57-bus system, Cases C and D address the same objectives, while Cases C1 and D1 evaluate robustness by setting three thermal units to zero active power. The hybrid AEO–MFO algorithm is benchmarked against several well-known techniques, showing faster convergence and better performance. Results from the IEEE 30-bus and 57-bus test systems confirm the effectiveness of the proposed method. The lowest power losses for Cases (A) and (A1) are 4.4138 MW and 4.862871 MW, respectively, using the proposed technique. In contrast, the best voltage deviation values were obtained in Cases (B) and (B1), with values of 0.0907 and 0.092257 per unit, respectively, in the IEEE 30-bus system. Similarly, in the IEEE 57-bus system, the lowest power losses for Cases (C) and (C1) are 18.4883 MW and 23.3865 MW, respectively. Additionally, minimal voltage deviation values of 0.6431 and 0.6138 per unit were achieved in Cases (D) and (D1), respectively. These outcomes demonstrate that the hybrid algorithm effectively solves complex ORPD problems and outperforms existing methods.