Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management
For power system operators, this method improves robustness against rare but critical weather events without explicit numerical results.
This work addresses robust power system control under prolonged renewable energy shortfalls by using a Fleming-Viot particle method to bias scenario generation in multistage stochastic programming, achieving cost-effective and robust control of conventional power plants.
High intermittent renewable penetration in the energy mix presents challenges in robustness for the management of power systems' operation. If a tail realization of the distribution of weather yields a prolonged period of time during which solar irradiation and wind speed are insufficient for satisfying energy demand, then it becomes critical to ramp up the generation of conventional power plants with adequate foresight. This event trigger is costly, and inaccurate forecasting can either be wasteful or yield catastrophic undersupply. This encourages particular attention to accurate modeling of the noise and the resulting dynamics within the aforementioned scenario. In this work we present a method for rare event-aware control of power systems using multi-stage scenario-based stochastic programming. A Fleming-Viot particle approach is used to bias the scenario generation towards rare realizations of very low wind power, in order to obtain a cost-effective control of conventional power plants that is robust under prolonged renewable energy shortfalls.