DOI:10.35833/MPCE.2021.000156 |
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Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation |
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Page view: 155
Net amount: 465 |
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Author:
Jingwei Hu1,Xiaoyuan Xu1,Hongyan Ma2,Zheng Yan1
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Author Affiliation:
1.Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Shanghai Jiao Tong University, Shanghai, China;2.Donghua University, Shanghai 201620, China
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Foundation: |
This work was supported by the National Natural Science Foundation of China (No. 52077136). |
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Abstract: |
Transmission network expansion can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation. This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation. The proposed model includes distributionally robust joint chance constraints, which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set. The proposed formulation yields a two-stage robust optimization model with variable bounds of the uncertain sets, which is hard to solve. By applying the affine decision rule, second-order conic reformulation, and duality, we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers. Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method. |
Keywords: |
Affine decision rule ; distributionally robust optimization ; joint chance constraint ; renewable generation ; transmission network expansion planning |
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Received:March 03, 2021
Online Time:2022/05/12 |
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