Exploring the past
CanESS is calibrated over historical time from 1978 to 2010 in one year steps. The result of the calibration is a complete historical database of all of the variables in CanESS adjusted to be coherent with the stock-flow and supply/disposition balance accounting identities of CanESS. This database is a consistent synthesis of data from a wide variety of data sources including Statistics Canada publications and Cansim tables, the energy end-use databases compiled by the Demand Policy Analysis Division and the Office of Energy Efficiency (Natural Resources Canada), Transport Canada, Environment Canada, the National Energy Board, Alberta Energy Regulator, the GHGenius life cycle model for Canada as well as a wide variety of technical data from engineering studies.
Generating scenarios for future
CanESS uses a dynamic systems modelling approach to simulate alternate energy system scenarios in the context of the Canadian economy and the demand and supply of fuels for Canada. CanESS scenarios run from the present (2010) to 2100 in one year steps. This long a time horizon is needed to explore the transition of one energy system to another as it is necessary to simulate one if not two turnovers of stocks. The common starting point for the scenario analysis are the existing stocks in 2010 including population, households, buildings, vehicles, appliances, energy currency producing capacity, resources and reserves fuel of primary energy sources that are produced in the model calibration.
How the model works
CanESS focuses on coherency – on creating scenarios assuring consistency between the population, level of economic activity, the services required by the population, the energy system, and the emissions of greenhouse gases and criteria air contaminants. It assures coherency both over time and within time periods through the use of stock-flow accounting rules, vintaged stocks and life tables, supply/disposition balances for fuels and feedstocks, and the explicit representation of energy transformations.
New technologies for producing feedstocks, transforming them into energy currencies, and for transforming energy currencies into energy services can only be introduced as new capacity is required for expansion and/or replacement of the stock. The emissions of greenhouse gases and criteria air contaminant are calculated at point of source within the boundaries of Canada and the year in which they are emitted.
An overview of the computational structure of the CanESS is shown in this diagram:
- At the top of the diagram, the drivers for the energy system are set in terms of population, households and gross domestic product to the time horizon of the simulation – by setting values for migration flows, fertility and mortality parameters, and per capita GDP the user can create demographic and economic scenarios.
- Then the transportation, residential, commercial buildings and industrial models calculate the energy currencies – hydrocarbon fuels, electricity, and hydrogen required to deliver services at a level commensurate with the economic and demographic context. Essentially these models keeps track of the stocks (vehicles, houses, buildings, etc.) and associate conversion efficiencies with the vintages of the stocks. The model user can set the efficiencies of future vintages and the rates at which new or alternative technologies penetrate into the stocks.
- Then these requirements for energy currencies along with those required to produce energy sources are fed to process models that calculate energy feedstocks required to produce the energy currencies. The feedstock production models – for conventional oil, oil sands, natural gas, coal, uranium, and biomass – represent the resources and the rate at which the resources can be produced. Differences between feedstocks required and feedstocks produced are made up by interprovincial and international trade.
A complex and powerful tool
The models are rich in compositional detail. For example, population is disaggregated by age and sex; passenger trips are disaggregated by type (commuting, intercity etc.) length of trip, and mode; road vehicles by size of vehicle, age, engine type. It is also rich in the representation of pathways for producing fuels and feedstocks.
Also all of the component models are provincially disaggregated and account for the supply, demand, and trade (both international and interprovincial) for all fuels and feedstocks.
The richness of the structure of CanESS makes it possible to explore many alternative configurations of the energy system that are coherent with alternative evolutions of the demographic and economic context.
If you have questions regarding the CanESS model, you can contact Bastiaan Straatman (email@example.com), CanESS modeller.