A cloud-edge collaborative optimization control strategy for voltage in distribution networks with PV stations

Abstract

With the continuous expansion of the power system scale and the continuous development of the power network, the traditional power system management and optimization methods face many challenges. In order to meet the requirements of voltage optimization and adjustment, the optimization problem is divided into cloud front precomputation and edge computing device cooperative optimization computation with the framework of cloud-edge cooperation. The cloud front-end precomputation uses an improved reactive-voltage sensitivity based on an improved modularity function to partition the power system on a 15 min basis and stores the results in the cloud data memory. The voltage threshold device detects the node voltage overrun and triggers the collaborative optimization computation of the edge computing devices, which sends a command to the cloud to call the partitioning result of this time period, and the cloud sends the result to each edge computing device, which determines the area it is responsible for, and adjusts the voltage overrun partitioning by using the mixed-integer second-order conic planning, and ultimately realizes the optimization strategy within the minute-level zone. Since the voltage adjustment is a fine-grained optimization of the local area, it is highly flexible and targeted. Moreover, using the cloud-edge collaboration technology, the intelligent management and optimization of the power system is finally realized. Case analysis and comparative verification show that the method proposed in this paper is accurate and highly efficient.