Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System

Articles in Press

Document Type : Research paper

Authors

Department of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran.

Abstract

Optimal load distribution in power systems is crucial for minimizing overall costs while adhering to technical constraints. This process becomes increasingly complex with the integration of wind energy due to the inherent uncertainty in wind turbine production caused by variable wind conditions. This paper presents a novel approach to address these uncertainties within the context of the optimal power flow (OPF) problem by employing Information Gap Decision Theory (IGDT). Unlike traditional scenario-based methods, IGDT provides a computationally efficient and reliable framework for decision-making under uncertainty without extensive probabilistic data. The methodology uses the Weibull probability density function to model wind speed, allowing for realistic estimation of wind farm output power. The Evolutionary Particle Swarm Optimization (EPSO) algorithm, an advanced version of PSO, is utilized to solve the optimization problem, reducing the risk of convergence to local optima. Results are computed under two strategies: risk-averse and risk-taking, represented by immunity functions. These strategies highlight the impact of user demand on adjusting calculation parameters. Comparative analysis with scenario-based probabilistic optimization shows that the IGDT approach enhances system load cost evaluation by 0.12%. This study provides a robust framework for optimal power allocation under uncertainty, ensuring resilient and secure power generation.

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References

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Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 20 December 2024

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History

  • Receive Date: 17 April 2024
  • Revise Date: 10 June 2024
  • Accept Date: 24 June 2024
  • First Publish Date: 20 December 2024

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