A Fault Management Method for Active Distribution Network Based on Stochastic Optimization

Abstract

An active distribution network fault management method based on stochastic optimization. Firstly, an uncertainty model is constructed, the system is initialized and initial data are obtained, and then the Monte Carlo method is used to simulate load demand, renewable energy output and fault duration. Scenario data included; then a two-stage model of power optimization and power flow optimization is established, and a risk management plan is equipped at the same time to ensure that the obtained scheduling strategy can maintain good applicability in the face of extreme scenarios. In the solution process, power optimization and power flow optimization are decoupled and calculated alternately, and the difference between the two calculated network loss values ​​is used as the convergence criterion; finally, the calculation result at the final convergence is used as the day-ahead optimal scheduling strategy. The invention can effectively improve the economic benefits of the active distribution network during normal operation and significantly reduce the load cutting cost under fault conditions.

Description

technical field [0001] The invention relates to a stochastic optimization-based active distribution network fault management method. Background technique [0002] With the large-scale use of fossil fuels causing global warming, the probability of extreme natural disasters such as tsunamis and earthquakes has increased significantly, and the resulting large-scale power outages have caused major economic losses to the power grid. The solution to environmental pollution and resource shortage has become urgent, and clean energy renewable energy (Renewable Energy Source, RES), mainly including wind turbine (Wind Turbine WT) and photovoltaic (Photovoltaic array PV) has gradually become a research hotspot and is widely used in power distribution. The penetration rate in the network continues to increase. However, the output of wind turbines at night is often more than that during the day, and the change trend is opposite to the load demand, which has the characteristics of anti-pe...

AI Technical Summary

Problems solved by technology

In practice, the unplanned island time, that is, the duration of the failure, is uncertain and cannot be accurately predicted

This poses a serious challenge to the dispatching strategy of the distribution network to deal with fault situations. When considering the intermittency and volatility of renewable energy and the uncertainty of load demand, this fault management problem becomes more complicated and must be Solved by appropriate random methods

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