Collaborative deployment of multiple reinforcement methods for network‐loss reduction in distribution system with seasonal loads

Abstract The integration of seasonal loads, such as cereal baking and aquatic‐product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost‐efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed‐integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state‐of‐the‐art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55‐bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

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Publication Details

Journal
Energy Conversion and Economics
Published
2024-09-19
DOI
https://doi.org/10.1049/enc2.12128
Citations
2
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
0.42

Funders

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article

Collaborative deployment of multiple reinforcement methods for network‐loss reduction in distribution system with seasonal loads

Mert Korkali, Lizi Luo, Shuai Lu, Kai Xing et al.
2 citations
Energy Conversion and Economics
Optimal Power Flow Distribution
0.42
article

Collaborative deployment of multiple reinforcement methods for network‐loss reduction in distribution system with seasonal loads

Mert Korkali, Lizi Luo, Shuai Lu, Kai Xing, Cheng Chen, Wenguang Zhao, Xiaoming Wang, Yizhe Xie
article en
2 citations

Abstract

Abstract The integration of seasonal loads, such as cereal baking and aquatic‐product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost‐efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed‐integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state‐of‐the‐art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55‐bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

Energy Conversion and EconomicsVol. 5(5)
Hefei University of Technology (CN), State Grid Corporation of China (China) (CN), Nanjing University of Science and Technology (CN), Shanghai Electric (China) (CN), Southeast University (CN), University of Missouri (US)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 38%
Optimal Power Flow Distribution
0.42
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