Fault self-adaptive robust optimization recovery method for flexible power distribution system

Abstract

The invention belongs to the technical field of power distribution network fault recovery, and particularly relates to a fault self-adaptive robust optimization recovery method for a flexible power distribution system. The method comprises the steps of 1, establishing a loss-considered power mathematical model of SNOP based on flexible multi-state switch control and power distribution network power flow; 2, taking the switch state and the SNOP active power as decision variables, establishing a fault optimization recovery model taking the minimum risk of loss of load as a target by consideringthe difference between the loss of the SNOP and the lost load loss of the user; and 3, performing fine adjustment optimization on the optimal scheme of the switching state by adjusting the uncertaintyof the SNOP active power response source and load, taking the reduction of the network loss and the voltage deviation as an objective function, and adaptively adjusting the weight coefficient according to the size of the voltage deviation so as to obtain the SNOP parameter values of the network loss and the voltage.

Description

technical field [0001] The invention belongs to the technical field of distribution network fault recovery, and in particular relates to a fault self-adaptive robust optimization recovery method of a flexible power distribution system. Background technique [0002] With the gradual advancement of the national new energy and electricity reform policies, a large number of distributed energy sources, electric vehicle charging devices, energy storage devices and micro-grids are connected to the distribution network, and the penetration rate continues to increase. The randomness of new energy sources and loads and the characteristics of decentralized control under the characteristics of multi-subject interests are likely to cause large fluctuations in feeder power and voltage limit violations, which pose severe challenges to the operation of distribution networks. At the same time, network reconfiguration based on tie switches is limited by issues such as switch response speed, o...

AI Technical Summary

Problems solved by technology

Yang Lijun and Lu Zhigang divided the wind power output into multiple typical scenarios, and carried out the optimal recovery of distribution network faults based on multiple scenarios. However, the scenario analysis method uses the expected value in each scenario, which cannot simulate the randomness of distributed power output well.

Chen Xinyue, Tang Wei, Chen Yu and others constructed a discrete probability model of photovoltaic power generation, and took the minimum power loss load higher than the confidence level as the objective function to establish a fault recovery model based on chance constrained programming, but the chance constrained method cannot Strictly ensure that the node voltage and branch power meet the constraints, and it is impossible to guarantee the feasibility of constraints for any element in the uncertain set

Images