Electric Power Emergency Material Scheduling Optimization Model Under Natural Disasters

Power system disaster refers to the natural disasters that could cause power blackouts, which is a main risk resource of power system. As power market develops, the vulnerability of power system and disgusting consequences of electrical accidents is becoming an important factor that threatens the stability and reliability of power system. Actively forecast, prevent and response to power system disaster to decrease losses are an important points for power system. Researches about electricity emergency management mainly focus on build emergency management platform, emergency center location and dispatching emergency supplies. Researches that combine emergency center location and follow-up multistage logistic scheme considering the influence of natural disasters are rare.Under this background, this paper summarized the research results about power system disaster risk assessment and electricity emergency management, studied multiple links of electricity emergency management. First, this paper summarized the steps of risk identification and risk analysis. Put forward risk assessment models and weighting method for transmission line icing disaster and debris flows. Then, based on risk assessment this paper took the probability of the occurrence of natural disasters, loss of power shortage and severity as well as material demand into consideration and build an optimization model for reserve point selection and storage amount determination. On this basis this paper built two optimization models for materials deployment after the accident. Wherein the model before the accident meets transportation time constraints and sets minimizing the summation of transportation cost and warehousing costs as its optimization objective. And the first stage model after the accident only considers the current supply-demand relationship and set total loss of power shortage as the optimization objective. But the second stage model also consider the satisfy conditions of next time point so it also takes inventories and goods in transit into consideration and add unmet amount of next time point into the optimization objective as a penalty factor. Though simulation analysis, the feasibility of the models put forward by this paper is proved. The results could meet time constrictions as well as minimize cost and loss, which can provide reference information for electricity emergency management material scheduling decisions.