CanESS uses a Dynamic Systems Modelling Approach. It simulates alternate energy system scenarios  in the context of the Canadian economy and the demand and supply of fuels for Canada. This contrasts with the life cycle analysis approach which is intended to compare products/technologies over the life cycle of the product/technologies.

CanESS scenarios run from the present (2006) 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.

CanESS is calibrated over historical time from 1978 to 2006 in one year steps. The result of the calibration is a complete historical data base of all of the variables in CanESS adjusted to be coherent with the stock-flow and supply disposition accounting identities of CanESS. This data base is a synthesis of data from a wide variety of data sources including Statistics Canada censuses and surveys, the energy end-use data bases compiled by the Demand Policy Analysis Division, Natural Resources Canada, and technical data from engineering studies and the GHGenius life cycle model for Canada.

The common starting point for the scenario analysis are the existing stocks in 2006 including population, households, buildings, vehicles, appliances, productive capacity, resources and reserves fuel that are produced in the model calibration.

New technologies for producing feedstocks, transforming them into energy currencies, and for transforming energy currencies into useful energy for the production of 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 released. This is unlike life cycle analysis which attributes all emissions to the products/technologies selected for analysis including those that occur in other countries and accumulates them over the life time of the product/technology.

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 emission 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.

An overview of the computational structure of the CanESS is shown in this diagram.

First, the context for the energy system is set in terms of population, households and gross domestic product to the  time horizon of the simulation – the user can set values for migration flows, fertility and mortality parameters, and per capita GDP. 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 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 sands, natural gas, coal, uranium, and biomass – represent the the resources and the rate at which the resources can be produced. Differences between feedstocks required and feestocks produced are made up by international trade.

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 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.

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.