Machine Learning, Artificial Intelligence, Electricity, Energy, Capacity, Ancillary Services, Reconstructability Analysis, Bayesian Networks, Support Vector Machines, Neural Networks
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be reduced by over 25% with the margin of error currently used in the industry while also significantly improving closeness and exceedance reliability metrics. The reduction in INC and DEC capacity requirements would yield an approximate cost savings of $4 million annually for one of 19 Western Energy Imbalance market participants. Reconstructability Analysis performs the best among the machine learning methods tested.
This work is marked with CC0 1.0 Universal
Harris, Marcus; Kirby, Elizabeth; Agrawal, Ameeta; Pokharel, Rhitabrat; Puyleart, Francis; and Zwick, Martin, "Data From: Machine Learning Predictions of Electricity Capacity" (2022). Systems Science Faculty Datasets. 1.
The data supports an in-review manuscript published in Energies (DOI and full citation will be provided upon publication).