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 utilizing 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 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 a remarkable proximity to the state-of-the-art industry benchmark, while assuming a fully decentralized, market-participant perspective. Finally, we recognize limitations of the proposed and other evaluated methodologies in predicting large price spike values.

Additional Metadata
Keywords Locational Marginal Price (LMP), Electricity price forecast, Wholesale energy markets, Statistical learning, Big data, Compressed sensing, Transmission line matrix methods, Power transmission lines, Topology, Power grids, Statistical learning, Optimization, Shape
Stakeholder Google, San Bruno, CA, USA
Persistent URL dx.doi.org/10.1109/TPWRS.2019.2921611
Journal IEEE Transactions on Power Systems
Citation
Radovanovic, A, Nesti, T, & Chen, B. (2019). A holistic approach to forecasting wholesale energy market prices. IEEE Transactions on Power Systems, 34(6), 4317–4328. doi:10.1109/TPWRS.2019.2921611