AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study
Informazioni aggiuntive
Tipo
Contributo in atti di conferenza
Anno
2023
Lingua
Inglese
Abstract
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
Mese
ottobre
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
