Fast Preventive Generation Scheduling by Aggregating Scenario-Insensitive Decision Variables

With the gradually increasing proportion of renewable energy in the power system, the impact of meteorology, especially extreme weather conditions on the secure and stable operation of the new power system has become more and more significant. This paper focuses on resilience enhancement at the operation level of the power system, using a stochastic unit commitment model to deal with potential wind turbine faults coupled with multiple line outages caused by hurricane. To address the high dimension issue arising from large number of scenarios, this paper proposes a scenario-insensitive decision variables aggregation method to accelerate solution. Based on the synthetic power grid data in Texas, USA, numerical test shows effectiveness of proposed method.