College of Electrical Engineering, Sichuan University, Chengdu 610065, China

The integration of a high proportion of distributed photovoltaic into distribution networks exacerbates the system's uncertainty. Moreover, it is challenging to accurately acquire data such as the network topology and line parameters of distribution networks, rendering traditional control methods for distribution networks based on precise physical modeling ineffective. With the widespread application of measurement devices in distribution networks, it becomes increasingly easier to obtain operation data of distribution networks. In this paper, a model-free voltage control method for active distribution networks based on measurement data of distribution networks is proposed. Firstly, a Hankel matrix is constructed based on the historical data of the distribution network to establish the relationship between the node voltages of the network and the output power of energy storage. Secondly, using local measurement data and considering uncertain disturbance factors and the attenuation model of the energy storage lifespan, an optimization framework for distribution network voltage under data-enabled predictive control is constructed to achieve the rolling optimization of distribution network voltage within the control cycle. Finally, the effectiveness and superiority of the proposed method are verified through simulations using the IEEE 34-bus standard example and the modified IEEE 123-bus example.

This work is supported by the Fundamental Research Funds for the Central Universities (No. YJ2021163).