A Review of Virtual Power Plants and Adjacent Research from a Decision-Focused Learning Perspective

Virtual power plant (VPP) operation commonly follows a predict-then-optimize framework, in which forecasts of generation, demand, and prices feed downstream optimization. However, minimizing statistical forecast errors overlooks cost asymmetry across error directions and periods, and the nonlinear response of decisions. This has motivated decision-focused learning, which incorporates downstream cost, profit, and risk into training or uncertainty representation. This paper reviews VPP and adjacent studies from three perspectives. Market participation benefits from identifying profit-sensitive periods and variables; resource operation requires attention to physical and time-coupled constraints and operational feasibility; and uncertainty handling improves when forecasts are aligned with downstream outcomes. Forecasts should therefore be evaluated using statistical metrics together with economic, operational, and risk implications. We further identify when such approaches are advantageous.