A deep learning approach for exploring the design space for the decarbonization of the Canadian electricity system
Date
2023
Authors
Jahangiri, Zahra
Judson, Mackenzie
Yi, Kwang Moo
McPherson, Madeleine
Journal Title
Journal ISSN
Volume Title
Publisher
Energies
Abstract
Conventional energy system models have limitations in evaluating complex choices for
transitioning to low-carbon energy systems and preventing catastrophic climate change. To address
this challenge, we propose a model that allows for the exploration of a broader design space. We
develop a supervised machine learning surrogate of a capacity expansion model, based on residual
neural networks, that accurately approximates the model’s outputs while reducing the computation
cost by five orders of magnitude. This increased efficiency enables the evaluation of the sensitivity
of the outputs to the inputs, providing valuable insights into system development factors for the
Canadian electricity system between 2030 and 2050. To facilitate the interpretation and communication
of a large number of surrogate model results, we propose an easy-to-interpret method using
an unsupervised machine learning technique. Our analysis identified key factors and quantified
their relationships, showing that the carbon tax and wind energy capital cost are the most impactful
factors on emissions in most provinces, and are 2 to 4 times more impactful than other factors on
the development of wind and natural gas generations nationally. Our model generates insights that
deepen our understanding of the most impactful decarbonization policy interventions.
Description
Keywords
decision making, deep learning, energy decarbonization, energy planning, K-means clustering, machine learning, power systems, residual neural networks
Citation
Jahangiri, Z., Judson, M., Yi, K. M., & McPherson, M. (2023). “A deep learning approach for exploring the design space for the decarbonization of the Canadian electricity system.” Energies, 16(3), 1352. https://doi.org/10.3390/en16031352