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

  • Amal Amin Mohamed
  • , Salah Kamel*
  • , Mohamed H. Hassan
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number338
JournalNeural Computing and Applications
Volume38
Issue number9
DOIs
StatePublished - May 2026
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2026.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Hybrid artificial ecosystem-based optimization algorithm with moth flame optimization algorithm (AEO–MFO)
  • Optimum reactive power dispatch (ORPD)
  • Renewable energy resources (RERs)
  • Time-varying demand

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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