Risk-Aware Value-Oriented Net Demand Forecasting for Virtual Power Plants

TL;DR

This paper introduces a risk-aware net demand forecasting method for virtual power plants that minimizes the risk of high operational costs by integrating a bilevel optimization framework with conditional value-at-risk.

Contribution

It formulates a bilevel program for parameter estimation that accounts for risk in demand forecasting, providing a convex solution for linear models.

Findings

Reduces the risk of high operation costs compared to risk-neutral methods.

Efficient convex formulation for linear forecast models.

Effective risk mitigation demonstrated through numerical results.

Abstract

This paper develops a risk-aware net demand forecasting product for virtual power plants, which helps reduce the risk of high operation costs. At the training phase, a bilevel program for parameter estimation is formulated, where the upper level optimizes over the forecast model parameter to minimize the conditional value-at-risk (a risk metric) of operation costs. The lower level solves the operation problems given the forecast. Leveraging the specific structure of the operation problem, we show that the bilevel program is equivalent to a convex program when the forecast model is linear. Numerical results show that our approach effectively reduces the risk of high costs compared to the forecasting approach developed for risk-neutral decision makers.