AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866908557141606400 |
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| author | Jami, Naga Venkata Sai Jitin Kardoš, Juraj Schenk, Olaf Köstler, Harald |
| author_facet | Jami, Naga Venkata Sai Jitin Kardoš, Juraj Schenk, Olaf Köstler, Harald |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10080 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study Jami, Naga Venkata Sai Jitin Kardoš, Juraj Schenk, Olaf Köstler, Harald Computational Engineering, Finance, and Science Computer Science and Game Theory Machine Learning 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. |
| title | AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study |
| topic | Computational Engineering, Finance, and Science Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2306.10080 |