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Shaping the Future Electricity System

Strategic Area

Energy Modelling

Status

Active

Partners

  • Electricity Canada
  • Trottier family foundation
  • Ivey Family foundation
  • RBC Foundation
  • International Brotherhood of Electrical Workers (IBEW)

(In-Kind)

  • Carleton University/University of Calgary/Ecole Polytechnique du Montreal
  • Canadian Nuclear Association
  • Canadian Renewable Energy Association
  • Electricity HR
  • First Nations Major Projects Coalition
  • Ivey Foundation
  • Marine Renewables Canada
  • Waterpower Canada

Fund

Energy Innovation Program

Year

2022

EIP Contribution

$ 250,000

Project Total

$ 1,275,668

Location

Calgary, AB

Find out more

The Transition Accelerator

Lead Proponent

The Transition Accelerator

Project Objectives

The project is focused on modelling studies of the electricity sector. The objective is to identify gaps in the current techno-economic meta-analysis. A detailed meta-analysis can identify common themes and components across various net-zero futures and explain the causal relationships that are responsible for varied outcomes.

There will be 3 workstreams that will unfold in parallel:

  1. Meta-analysis of existing modelling results and analysis to identify common themes and components across various net-zero futures in the electricity sector, which will result in the delivery of a final report.
  2. Decision tool - developing a real-world overlay framework to identify considerations for implementing modelling results.
  3. Regional engagements to determine regional specific needs and barriers to electricity system development, resulting in the delivery of a summary report.

Expected Results

The project will provide a detailed comparison of results of various net-zero futures in the electricity sector and provide insights, including:

  • areas of alignment
  • gaps
  • real world overlay
  • robust engagement

The project puts a strong emphasis on understanding why modelling results in the electricity sector are varied. Modelling results are highly sensitive to the inputs, assumptions, and models used to produce them. Understanding these sensitivities will be critical in future analysis of future net-zero pathway assessments and futures.

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