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2020 | OriginalPaper | Chapter

Research on Voltage Control Strategy of Distribution Network Based on Model Prediction Technology

Authors : Qing Wang, Jingwen Ai, Huaying Zhang

Published in: Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control

Publisher: Springer Nature Singapore

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Abstract

With the development of the economy and the improvement of people’s living standards, energy has become an increasingly indispensable substance. The large-scale development and utilization of fossil energy has caused problems of energy crisis and environmental pollution. In order to solve these two problems, it is necessary to develop and utilize renewable energy sources, and access distributed power sources in the distribution network to form an active distribution network. The active distribution network contains a large number of distributed power sources, energy storage units and reactive power compensation equipment. Although it provides more convenience for voltage and reactive power regulation of the distribution network, the intermittent and volatility of renewable energy sources will also be Its voltage has a big impact. In order to solve the voltage dip and overvoltage problem of active distribution network, a new active distribution network voltage control strategy is proposed by using model prediction control technology. Inaccurate system modeling is reduced by establishing active distribution bus voltages, distributed power supplies, and energy storage device prediction models. With the minimum adjustment cost as the control target, the second-order cone relaxation method is used to optimize the action behavior of each control device. Finally, the IEEE33 node simulation example is built on the matlab simulation platform. The results show that the proposed voltage control strategy has good control effect and robustness.

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Metadata
Title
Research on Voltage Control Strategy of Distribution Network Based on Model Prediction Technology
Authors
Qing Wang
Jingwen Ai
Huaying Zhang
Copyright Year
2020
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-13-9779-0_56