Optimal Location of Energy Storage System to Mitigate Wind Power Curtailment

Based on the optimal placement for the energy storage system, this paper suggests a bi-level optimization model with multi-objectives to reduce the wind power curtailment in electrical networks. The goals of the upper-level subproblem based on the genetic algorithm optimal power flow (GA-OPF) are to minimize wind power curtailment, power losses, load shedding, and increase the voltage stability margin. In contrast, the lower-level subproblem seeks to maximize social welfare within the limits of the entire system and various situations. A set of lower-level subproblems representing the conditions of the market clearing is utilized to confine the subproblem of the upper-level under the different scenarios for the load and wind power. An IEEE 24-bus system that has been modified is used to evaluate the effectiveness of the suggested algorithm. The outcomes demonstrate the usefulness of the suggested methodology in supporting planning for power system enhancements to minimize wind power curtailment, integrate wind energy while enhancing the stability of the voltage and loadability, and maximize social welfare.

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Publication Details

Published
2024-12-17
DOI
https://doi.org/10.1109/mepcon63025.2024.10850158
Primary Topic
Power Systems and Renewable Energy
Type
article
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article

Optimal Location of Energy Storage System to Mitigate Wind Power Curtailment

Ahmed N. Sheta, Mohammed A. Saeed, Abdelfattah A. Eladl, Samaa F. Osman
Power Systems and Renewable Energy
article

Optimal Location of Energy Storage System to Mitigate Wind Power Curtailment

Ahmed N. Sheta, Mohammed A. Saeed, Abdelfattah A. Eladl, Samaa F. Osman
article en

Abstract

Based on the optimal placement for the energy storage system, this paper suggests a bi-level optimization model with multi-objectives to reduce the wind power curtailment in electrical networks. The goals of the upper-level subproblem based on the genetic algorithm optimal power flow (GA-OPF) are to minimize wind power curtailment, power losses, load shedding, and increase the voltage stability margin. In contrast, the lower-level subproblem seeks to maximize social welfare within the limits of the entire system and various situations. A set of lower-level subproblems representing the conditions of the market clearing is utilized to confine the subproblem of the upper-level under the different scenarios for the load and wind power. An IEEE 24-bus system that has been modified is used to evaluate the effectiveness of the suggested algorithm. The outcomes demonstrate the usefulness of the suggested methodology in supporting planning for power system enhancements to minimize wind power curtailment, integrate wind energy while enhancing the stability of the voltage and loadability, and maximize social welfare.

Mansoura University (EG)
Affordable and clean energy
Openalex Percentile: Top 71%
Power Systems and Renewable Energy
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