Optimal Placement and Sizing of Distributed Generation in Distribution System Using Genetic Algorithm with Long-Term Renewable Profiles

This paper proposes an optimization approach for determining the optimal placement and sizing of distributed generation (DG) units, incorporating real-time renewable energy profiles. The case study utilizes real-time renewable data from the national solar radiation database (NSRDB) and the IEEE 33-bus system. A genetic algorithm (GA) is employed to perform the DG placement and capacity optimization. The proposed methodology provides a robust and practical framework for renewable energy management, supporting DG integration while accounting for real-time variations in renewable energy availability.

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Publication Details

Published
2025-07-18
DOI
https://doi.org/10.1109/eeais66172.2025.11170925
Primary Topic
Power Systems and Technologies
Type
article
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article

Optimal Placement and Sizing of Distributed Generation in Distribution System Using Genetic Algorithm with Long-Term Renewable Profiles

H. A. Tillmann Hein, Lujie Yu, Jian Li, Heyu Luo
Power Systems and Technologies
article

Optimal Placement and Sizing of Distributed Generation in Distribution System Using Genetic Algorithm with Long-Term Renewable Profiles

H. A. Tillmann Hein, Lujie Yu, Jian Li, Heyu Luo
article en

Abstract

This paper proposes an optimization approach for determining the optimal placement and sizing of distributed generation (DG) units, incorporating real-time renewable energy profiles. The case study utilizes real-time renewable data from the national solar radiation database (NSRDB) and the IEEE 33-bus system. A genetic algorithm (GA) is employed to perform the DG placement and capacity optimization. The proposed methodology provides a robust and practical framework for renewable energy management, supporting DG integration while accounting for real-time variations in renewable energy availability.

Tianjin University (CN)
Openalex Percentile: Top 74%
Power Systems and Technologies
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