AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study
Jitin Jami NVS, Kardoš J, Schenk O, Köstler H (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: IEEE Computer Society
Conference Proceedings Title: IEEE PES Innovative Smart Grid Technologies Conference Europe
Event location: Grenoble, FRA
ISBN: 9798350396782
DOI: 10.1109/ISGTEUROPE56780.2023.10407905
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. 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.
Involved external institutions
Switzerland (CH)How to cite
APA:
Jitin Jami, N.V.S., Kardoš, J., Schenk, O., & Köstler, H. (2023). AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study. In IEEE PES Innovative Smart Grid Technologies Conference Europe. Grenoble, FRA: IEEE Computer Society.
MLA:
Jitin Jami, Naga Venkata Sai, et al. "AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study." Proceedings of the 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023, Grenoble, FRA IEEE Computer Society, 2023.
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