Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management

TL;DR

This paper introduces a multistage stochastic programming method using a Fleming-Viot particle approach to improve power system management during rare, prolonged renewable energy shortfalls, enhancing robustness and cost-effectiveness.

Contribution

It presents a novel scenario generation technique for rare event modeling in power systems, enabling more reliable control during extreme renewable shortfalls.

Findings

Enhanced scenario generation for rare events

Improved robustness in power system control

Cost-effective management during renewable shortfalls

Abstract

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 optimization. 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.