Volume 29, Issue 7 e12002
SPECIAL ISSUE PAPER

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Xishan Wen

Corresponding Author

Xishan Wen

School of Electrical Engineering and Automation, Wuhan University, Wuhan, 430072 China

Correspondence

Xishan Wen, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072 China.

Email: [email protected]

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Yi Yu

Yi Yu

School of Electrical Engineering and Automation, Wuhan University, Wuhan, 430072 China

Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong, Special Administrative Region, 999077 China

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Zhao Xu

Zhao Xu

Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong, Special Administrative Region, 999077 China

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Jian Zhao

Jian Zhao

Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong, Special Administrative Region, 999077 China

Department of Electrical Power Engineering, Shanghai University of Electric Power, Shanghai, 200090 China

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Jiayong Li

Jiayong Li

Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong, Special Administrative Region, 999077 China

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First published: 31 March 2019
Citations: 13

Abstract

To counterbalance the significant challenges imposed by renewable distributed generations penetration, this paper discusses the need of distributed energy storage system investment in distribution networks and proposes a robust optimization based storage investment model. The operational constraints of distribution network (e.g., voltage profile and substation capacity limitation) and storage device (e.g., state of energy and charging/discharging limit) are considered to guarantee the technical operation requirements. The proposed model is mathematically formulated as a two-stage robust optimization with uncertainty of renewable distributed generator that is quantified by a polyhedral uncertainty set. The investment-decision variables are optimized in the first stage, and the feasibility in the real-time worst-case scenario is checked in the second stage. A column-and-constraint generation (C&CG) algorithm and the big-M linearization method are employed to solve the associated optimization problem. Numerical experiments on IEEE-37-node and IEEE-123-node distribution networks demonstrate the effectiveness of the proposed model.

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