A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System

Zahra Jahangiri (), Mackenzie Judson, Kwang Moo Yi and Madeleine McPherson
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Zahra Jahangiri: Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8P 5C2, Canada
Mackenzie Judson: Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8P 5C2, Canada
Kwang Moo Yi: Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Madeleine McPherson: Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8P 5C2, Canada

Energies, 2023, vol. 16, issue 3, 1-21

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.

Keywords: decision making; deep learning; energy decarbonization; energy planning; K-means clustering; machine learning; power systems; residual neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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