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.

翻译：准确的价格预测对于市场参与者优化其运营调度和投标策略至关重要，尤其是在当前电价波动加剧、传统方法可预测性下降的背景下。节点边际定价（LMP）机制广泛应用于现代电力市场，传统方法依赖于最优潮流（OPF）求解器。然而，对于大型电网而言，该过程计算耗时且计算强度过高。基于机器学习（ML）的预测可为LMP预测提供高效工具，尤其在含可再生能源等间歇性电源的能源市场中。本研究评估了主流机器学习与深度学习模型在多个电网中预测LMP的性能，并基于多种场景评估了这些模型预测LMP的准确性与鲁棒性。结果表明，ML模型预测LMP的速度比传统OPF求解器快4-5个数量级，误差率仅为5-6%，突显了ML模型在借助现代高性能计算集群中多核CPU和GPU等硬件基础设施时，对大规模电力系统LMP预测的潜力。