Managing Risk using Rolling Forecasts in Energy-Limited and Stochastic Energy Systems

TL;DR

This paper develops risk-aware linear policies for energy system management with stochastic wind, storage, and fuel limits, using rolling forecasts and risk measures to improve decision-making under uncertainty.

Contribution

It introduces a novel parameter-modified cost function approach for risk-aware policies that reduces computational complexity in stochastic energy system optimization.

Findings

Effective risk control with CVaR and buffered probability measures

Reduced computational load compared to direct risk measure optimization

Successful demonstration on a numerical case study

Abstract

We study risk-aware linear policy approximations for the optimal operation of an energy system with stochastic wind power, storage, and limited fuel. The resulting problem is a sequential decision-making problem with rolling forecasts. In addition to a risk-neutral objective, this paper formulates two risk-aware objectives that control the conditional value-at-risk of system cost and the buffered probability of exceeding a predefined threshold of unserved load. The resulting policy uses a parameter-modified cost function approximation that reduces the computational load compared to the direct inclusion of those risk measures in the problem objective. We demonstrate our method on a numerical case study.