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Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified

Naima Agouzoul1, Aziz Oukennou2, Faissal Elmariami1

  • 1National Superior School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Oasis, Casablanca, Morocco.

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Summary
This summary is machine-generated.

A modified Dandelion Optimizer (MDO) algorithm effectively solves the stochastic optimal reactive power dispatch (SORPD) problem. This approach reduces expected power losses and enhances voltage stability, especially with renewable energy integration.

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Area of Science:

  • Electrical Engineering
  • Power Systems Optimization
  • Computational Intelligence

Background:

  • Transmission system performance enhancement is critical and achievable through optimal reactive power dispatch (ORPD).
  • Stochastic optimal reactive power dispatch (SORPD) presents challenges due to continuous variations in load demand and renewable energy sources (RERs).

Purpose of the Study:

  • To introduce a modified Dandelion Optimizer (MDO) algorithm for optimizing the SORPD problem.
  • To address stochastic fluctuations in load demand and RER power generation.
  • To reduce expected power losses (SEPL) and enhance expected voltage stability (SEVS) in the IEEE 30-bus system.

Main Methods:

  • The MDO algorithm integrates Quasi-oppositional-based learning (QOBL), Weibull flight motion strategy (WFM), and fitness distance balance (FDB) to improve exploration and exploitation.
  • Monte Carlo simulations and scenario reduction generated 15 scenarios to model uncertainties in load demand and RER power.
  • The SORPD problem was solved for the IEEE 30-bus system, evaluating performance with and without RER integration.

Main Results:

  • The proposed MDO algorithm significantly reduced SEPL and improved SEVS, particularly when RERs were integrated.
  • Simulation results demonstrate the effectiveness of the MDO for SORPD.
  • Comparative analysis showed MDO outperformed Sand Cat Swarm Optimization (SCSO), Gorilla Troop Optimizer (GTO), Harmony Search (HS), and Beluga Whale Optimization (BWO).

Conclusions:

  • The modified Dandelion Optimizer (MDO) provides an effective solution for the stochastic optimal reactive power dispatch problem.
  • The MDO enhances power system stability and reduces losses, offering a robust method for integrating renewable energy sources.
  • MDO demonstrates superior performance compared to other optimization algorithms for SORPD.