First works that focus on the energy trading model chain have shown that equivalent results can be obtained with the analytical approach. For example, end-to-end machine learning (ML) models can be trained to minimize the downstream decisions regret or even directly learn a mapping from data to decisions. In the last years the new paradigm of “Prescriptive Analytics” emerged, where data-driven approaches integrated the two steps. Further, forecast accuracy might not align with performance optimality. For example, when trading the production of a virtual power plant (VPP) in day ahead and ancillary service markets, one may need as many as 11 models (energy and market quantities forecasting and stochastic optimization). The classical “Forecast then Optimise” approach may involve a complex model chain of multiple models that one has to tune and maintain.
Operational management of energy systems in time scales of a few minutes to days ahead involves decision making that results from two major steps: (1) leveraging contextual information to forecast uncertain input quantities like electricity demand, weather-dependent renewable production (wind, solar, hydro…), electricity market quantities (prices, imbalances…), and (2) optimization, where the forecasts are used as input to optimization tools for congestion management, economic dispatch, unit commitment, electricity trading, reserves estimation and other applications. Title: "Development of prescriptive analytics for energy systems"
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