Enhancing Energy System Models Using Better Load Forecasts

TL;DR

This paper introduces a simple time series model that enhances load forecast data from transmission system operators, leading to more accurate energy system modeling and reduced pricing errors in short-term markets.

Contribution

A novel real-time load data correction method that improves the quality of energy system models without requiring additional input variables.

Findings

Improved load forecasts reduce energy market pricing errors.

Enhanced data quality particularly benefits high-price, tight market conditions.

The method operates successfully in real-time, successively refining incoming data.

Abstract

Energy system models require a large amount of technical and economic data, the quality of which significantly influences the reliability of the results. Some of the variables on the important data source ENTSO-E transparency platform, such as transmission system operators' day-ahead load forecasts, are known to be biased. These biases and high errors affect the quality of energy system models. We propose a simple time series model that does not require any input variables other than the load forecast history to significantly improve the transmission system operators' load forecast data on the ENTSO-E transparency platform in real-time, i.e., we successively improve each incoming data point. We further present an energy system model developed specifically for the short-term day-ahead market. We show that the improved load data as inputs reduce pricing errors of the model, with strong reductions particularly in times when prices are high and the market is tight.