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Probabilistic forecasting in combination with stochastic programming is a keytool for handling the growing uncertainties in future energy systems. Derivedfrom a general stochastic programming formulation for the optimal schedulingand bidding in energy markets we examine several common special instancescontaining uncertain loads, energy prices, and variable renewable energies. Weanalyze for each setup whether only an expected value forecast, marginal orbivariate predictive distributions, or the full joint predictive distributionis required. For market schedule optimization, we find that expected priceforecasts are sufficient in almost all cases, while the marginal distributionsof renewable energy production and demand are often required. For bidding curveoptimization, pairwise or full joint distributions are necessary except forspecific cases. This work helps practitioners choose the simplest type offorecast that can still achieve the best theoretically possible result fortheir problem and researchers to focus on the most relevant instances.

Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need?