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A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

  • 0Electrical Power and Machines Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt.
Scientific Reports +

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February 6, 2024

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February 6, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a modified white shark optimization (MWSO) algorithm to solve the optimal power flow (OPF) problem, effectively handling uncertainties from renewable energy sources and load demand. The MWSO algorithm demonstrates superior performance over the original WSO for improved grid management.

Area Of Science

  • Electrical Engineering
  • Computational Intelligence
  • Optimization Algorithms

Background

  • The optimal power flow (OPF) problem is crucial for efficient power system operation.
  • Integrating renewable energy sources introduces significant uncertainties into power grids.
  • Existing optimization algorithms may struggle with the complexity and stochastic nature of modern power systems.

Purpose Of The Study

  • To develop and evaluate a novel Modified White Shark Optimization (MWSO) algorithm for solving the OPF problem.
  • To enhance the MWSO algorithm by integrating Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies.
  • To address uncertainties in renewable energy generation and load demand within the OPF framework.

Main Methods

  • The Modified White Shark Optimization (MWSO) algorithm, incorporating GB and QOBL strategies.
  • Modification of IEEE 30-bus and IEEE 57-bus systems by integrating wind and solar PV generators.
  • Modeling of renewable energy variability using Weibull and lognormal distributions.
  • Inclusion of reserve and penalty costs for power output over/underestimation.
  • Analysis of load demand unpredictability using probability density functions (PDF).
  • Consideration of thermal generator ramp rate limits.

Main Results

  • The MWSO algorithm shows improved convergence rate and accuracy compared to the original WSO.
  • The proposed method effectively handles uncertainties from renewable sources and load variations in modified IEEE 30-bus and 57-bus systems.
  • Comparative analysis against six established techniques using 23 benchmark functions demonstrates the MWSO algorithm's effectiveness.
  • Statistical analysis confirms the superiority of MWSO in addressing OPF under uncertain conditions.

Conclusions

  • The MWSO algorithm is a robust and effective tool for solving the optimal power flow problem.
  • The approach successfully integrates renewable energy sources and accounts for demand-side uncertainties.
  • This research provides a valuable contribution to the field of power system optimization and smart grid management.

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