A Fast Voltage-Reliant and Cost-Effective DG Allocation in Distribution Systems With Variable Loads and Generation

The variability of load power due to voltage fluctuations in distribution networks can significantly impact system performance. Optimally allocating distributed generation (DG) can mitigate these effects by regulating bus voltage levels. However, previous studies addressing DG allocation with voltagedependent loads have relied on nonlinear formulations or oversimplified approximations, which either increase computational complexity or compromise solution accuracy. This paper presents a novel, computationally efficient, and costeffective DG allocation model that accurately captures the relationship between load components, consumer types, DG output, and voltage variations. The proposed framework leverages a mathematical formulation that maintains model linearity, enabling solutions through commercial linear solvers without extensive linearization or approximation errors. The model was validated using the IEEE 33-bus test system, demonstrating superior accuracy, economic efficiency, and significantly reduced computational time compared to conventional DG allocation methods. Key contributions include a voltage-reliant DG placement model that integrates load and generation dependency on voltage, an economically optimized allocation strategy, and a flexible approach applicable even in the absence of detailed load component data. The findings highlight the effectiveness of the proposed model in enhancing network efficiency and optimizing DG placement under realistic operational conditions.

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Publication Details

Journal
IEEE Transactions on Industry Applications
Published
2025-05-20
DOI
https://doi.org/10.1109/tia.2025.3571884
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.73
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article

A Fast Voltage-Reliant and Cost-Effective DG Allocation in Distribution Systems With Variable Loads and Generation

Augustine Awaafo, Francisco Jurado, Abdullah G. Alharbi, Meisam Mahdavi et al.
1 citations
IEEE Transactions on Industry Applications
Smart Grid Energy Management
0.73
article

A Fast Voltage-Reliant and Cost-Effective DG Allocation in Distribution Systems With Variable Loads and Generation

Augustine Awaafo, Francisco Jurado, Abdullah G. Alharbi, Meisam Mahdavi, Nahar Fehaid Alshammari
article en
1 citations

Abstract

The variability of load power due to voltage fluctuations in distribution networks can significantly impact system performance. Optimally allocating distributed generation (DG) can mitigate these effects by regulating bus voltage levels. However, previous studies addressing DG allocation with voltagedependent loads have relied on nonlinear formulations or oversimplified approximations, which either increase computational complexity or compromise solution accuracy. This paper presents a novel, computationally efficient, and costeffective DG allocation model that accurately captures the relationship between load components, consumer types, DG output, and voltage variations. The proposed framework leverages a mathematical formulation that maintains model linearity, enabling solutions through commercial linear solvers without extensive linearization or approximation errors. The model was validated using the IEEE 33-bus test system, demonstrating superior accuracy, economic efficiency, and significantly reduced computational time compared to conventional DG allocation methods. Key contributions include a voltage-reliant DG placement model that integrates load and generation dependency on voltage, an economically optimized allocation strategy, and a flexible approach applicable even in the absence of detailed load component data. The findings highlight the effectiveness of the proposed model in enhancing network efficiency and optimizing DG placement under realistic operational conditions.

IEEE Transactions on Industry ApplicationsVol. 61(6)
Princess Nourah bint Abdulrahman University (SA), Universidad de Jaén (ES), Jouf University (SA), University of Energy and Natural Resources (GH)
Affordable and clean energy
Openalex Percentile: Top 28%
Smart Grid Energy Management
0.73
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