AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study
This addresses the need for efficient price predictions for market participants in volatile electricity markets, but it is incremental as it applies existing ML methods to a known bottleneck.
This study tackled the problem of slow and computationally intensive Locational Marginal Pricing (LMP) predictions in electricity markets by evaluating machine learning models, finding they can predict LMP 4-5 orders of magnitude faster than traditional methods with a 5-6% error rate.
Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where electricity prices become more volatile and less predictable using classical approaches. The Locational Marginal Pricing (LMP) pricing mechanism is used in many modern power markets, where the traditional approach utilizes optimal power flow (OPF) solvers. However, for large electricity grids this process becomes prohibitively time-consuming and computationally intensive. Machine learning (ML) based predictions could provide an efficient tool for LMP prediction, especially in energy markets with intermittent sources like renewable energy. This study evaluates the performance of popular machine learning and deep learning models in predicting LMP on multiple electricity grids. The accuracy and robustness of these models in predicting LMP is assessed considering multiple scenarios. The results show that ML models can predict LMP 4-5 orders of magnitude faster than traditional OPF solvers with 5-6\% error rate, highlighting the potential of ML models in LMP prediction for large-scale power models with the assistance of hardware infrastructure like multi-core CPUs and GPUs in modern HPC clusters.