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

TL;DR

This paper investigates which types of probabilistic forecasts are necessary for effective bidding and scheduling in energy markets, emphasizing the sufficiency of simpler forecasts in most scenarios.

Contribution

It provides a systematic analysis of forecast types needed for different energy market optimization problems, guiding practitioners and researchers in selecting appropriate probabilistic models.

Findings

Expected price forecasts are generally sufficient for schedule optimization.

Marginal distributions of renewable energy and demand are often needed.

Full joint distributions are required mainly for bidding curve optimization.

Abstract

Probabilistic forecasting in combination with stochastic programming is a key tool for handling the growing uncertainties in future energy systems. Derived from a general stochastic programming formulation for the optimal scheduling and bidding in energy markets we examine several common special instances containing uncertain loads, energy prices, and variable renewable energies. We analyze for each setup whether only an expected value forecast, marginal or bivariate predictive distributions, or the full joint predictive distribution is required. For market schedule optimization, we find that expected price forecasts are sufficient in almost all cases, while the marginal distributions of renewable energy production and demand are often required. For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases. This work helps practitioners choose the simplest type of forecast that can still achieve the best theoretically possible result for their problem and researchers to focus on the most relevant instances.