Distributed Photovoltaic Power Siting and Capacity Sizing Based on Improved Particle Swarm Optimization Algorithm

A photovoltaic grid integration planning method that balances economy and safety is proposed for the site selection and capacity determination of distributed photovoltaic power sources accessed to the distribution grid. Considering three objectives of voltage deviation, network loss, and investment cost, a multi-objective constrained optimization model for distributed photovoltaic access distribution network is constructed with constraints of power flow, node voltage, and access capacity. A distributed photovoltaic access distribution network location and capacity optimization method based on improved particle swarm optimization is proposed. Taking the IEEE33 node distribution system as an example, simulation and comparative analysis are conducted to verify its effectiveness and feasibility.

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Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/scems63294.2024.10756479
Citations
1
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.17
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Distributed Photovoltaic Power Siting and Capacity Sizing Based on Improved Particle Swarm Optimization Algorithm

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1 citations
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article

Distributed Photovoltaic Power Siting and Capacity Sizing Based on Improved Particle Swarm Optimization Algorithm

Kui Ma, Qun Yu, Xinze Zhang, Tianle Wang, Yan Wang
article en
1 citations

Abstract

A photovoltaic grid integration planning method that balances economy and safety is proposed for the site selection and capacity determination of distributed photovoltaic power sources accessed to the distribution grid. Considering three objectives of voltage deviation, network loss, and investment cost, a multi-objective constrained optimization model for distributed photovoltaic access distribution network is constructed with constraints of power flow, node voltage, and access capacity. A distributed photovoltaic access distribution network location and capacity optimization method based on improved particle swarm optimization is proposed. Taking the IEEE33 node distribution system as an example, simulation and comparative analysis are conducted to verify its effectiveness and feasibility.

Shanghai Electric (China) (CN), Shandong University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 43%
Power Systems and Renewable Energy
0.17
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