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