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AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study

Informazioni aggiuntive

Jami J., Kardoš J., Schenk O., Koestler H.
Contributo in atti di conferenza
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. 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 solutions could provide an efficient tool for LMP prediction, especially in energy markets with intermittent sources like renewable energy. The 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 machine learning models can predict LMP 4-5 orders of magnitude faster than traditional OPF solvers with 5-6% error rate, highlighting the potential of machine learning models in LMP prediction for large-scale power models with the help of hardware solutions like multi-core CPUs and GPUs in modern HPC clusters.
Atti di conferenza
ISGT 2023
Nome conferenza
Innovative Smart Grid Technologies Conference
Luogo conferenza
Université Grenoble Alpes, France
Data conferenza
October 23rd-26th, 2023
Parole chiave
Future Energy Markets, Locational Margin Pricing, Machine Learning, Price Prediction, Uncertainty Man- agement, High Performance Computing