基于智能软开关的主动配电网电压模型预测控制优化方法

doi:10.19595/j.cnki.1000-6753.tces.210858

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Abstract The power output of distributed generators and loads in distribution networks has strong volatility and randomness, which makes the voltage overruns and abnormal problems more serious. In this paper, a real-time active distribution network voltage optimization strategy using model predictive control (MPC) based on the soft open point (SOP) has been proposed, which uses only electrical information of both ends of SOP. Firstly, a predictive model of network voltage and power loss sensitivity is established based on the reference values given by the long-term optimization. Then a rolling optimization model with an SOP is established, in which the objective function is to minimize the voltage deviation and power losses. With the rolling-horizon approach, the power exchange between both ends of SOP is optimized and the predictive model is corrected through the voltage deviation feedback. This method effectively solves the problem that existing voltage optimization methods rely on global information are difficult to cope with rapid power fluctuations. Case studies on the IEEE 33-node system and a regional distribution system are carried out to verify the effectiveness of the proposed method. Results show that the voltage of the distribution network is enhanced and the power losses are significantly reduced, which provides a technical reference for operating new distribution networks.

Key words： Model predictive control voltage optimization active distribution network sensitivity soft open point

Received: 14 June 2021

PACS:

TM76

Fund:中国电机工程学会“青年人才托举工程”（ICB-2020-187）和深蓝计划（SL2020MS018）资助项目

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	Articles by authors
	Gao Congzhe
	Huang Wentao
	Yu Moduo
	Chen Yang
	Tai Nengling

Cite this article:

Gao Congzhe,Huang Wentao,Yu Moduo等. A Model Predictive Control Method to Optimize Voltages for Active Distribution Networks with Soft Open Point[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3263-3274.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210858 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I13/3263

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