A Holistic Approach to Forecasting Wholesale Energy Market Prices

TL;DR

This paper introduces a novel, data-driven methodology for forecasting wholesale energy market prices by leveraging publicly available grid data and the principles of optimal power flow, achieving high accuracy and market insight.

Contribution

It develops a new approach combining statistical learning and grid structure analysis to predict prices using only public data, improving transparency and decentralization.

Findings

Strong correlation between grid mix and market prices

Accurate day-ahead price predictions demonstrated

Method approaches industry benchmark performance

Abstract

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.