Risk-Averse Optimal Scheduling Model of a Virtual Power Plant Considering Uncertainties

A risk-averse optimal scheduling model is introduced to manage the virtual power plant (VPP). This optimization problem models energy transactions between market participants and VPP operator, aiming to minimize VPP’s operational costs. Considering various uncertainties, a multistage stochastic programming approach is formulated. Nonanticipativity constraints are applied to capture the stochastic nature of uncertainties, ensuring that decisions made at each stage rely only on the information available up to that point, without foresight into future data. The proposed scheme operates over a rolling-window horizon divided into intervals, producing sequential decisions as uncertainties unfold, rather than determining them all at once. By incorporating conditional-value-at-risk (CVaR) metric, scheduling goal is modified to minimize a risk-averse function linked to VPP operating costs.