GEC Model scenarios
The IEA’s medium- to long-term outlook publications – including the World Energy Outlook (WEO) and Energy Technology Perspectives (ETP) – use a scenario approach relying on the GEC Model to examine future energy trends. The GEC Model is used to explore multiple scenarios, each of which is built on a different set of underlying assumptions about how the energy system might evolve over time. By comparing them, readers can assess whatdrives the various outcomes, and the opportunities and pitfalls that lie along the way. These scenarios are notpredictions, and do not contain a single view about what the long-term future might hold. Instead, the scenariosseek to enable readers to compare different possible versions of the future, and the levers and actionsthat produce them, and to gain insights into the future of global energy.

Global Energy and Climate Model overview

The sectoral energy and emission balances are calculated based on the final energy end uses – the service demand – by determining first the final energy demand needed to serve it, then the required transformations to convert primary energy into the required fuels, and finally the primary energy needs. This is based on a partial equilibrium approach using for some elements a partial-optimisation model, within which specific costs play an important role in determining the share of fuels and technologies to satisfy energy service demand. In different parts of the model, logit and Weibull functions are used to determine the share of technologies based upon their specific costs. This includes investment costs, operating and maintenance costs, fuel costs and in some cases costs for emitting CO2. In certain sectors, such as hydrogen production, specially designed and linked optimisation modules are used.

Energy-intensive sub-sectors For each of the five energy-intensive industry sub-sectors, the modelling framework consists of a series of interacting sub-modules and a core technology model (see Figure 3.3). The sub-modules consist of an activity model, a stock model and a capacity model.

Industry sector model internal module structure and key data flows

Shipping
The methodology of the bottom-up model for shipping energy demand is illustrated Activity projections, a calibrated baseline fleet, and scenario-specific parameters are inputs into a dynamic stock model that calculates the evolution and the energy demand of the global shipping fleet by ship type, propulsion, and fuel. This energy demand is subsequently allocated across regions and allocated to either the international or the domestic shipping sectors.

Shipping model methodology

Hourly models
To quantify the scale of the challenge arising from the integration of high shares of VRE and to assess which measures could be used to minimise curtailment, an hourly model was developed for WEO-2016, to provide further insights into the operations of power systems. The model builds upon the annual projections generated in the GEC Model and makes it possible to explore emerging issues in power systems, such as those that arise as the share of VRE continues to rise. The model then feeds the main GEC Model with information about additional constraints on the operations of different power plants. The model is a classical hourly dispatch model, representing all hours in the year, setting the objective of meeting electricity demand in each hour of the day for each day of the year at the lowest possible cost, while respecting operational constraints.1 All 106 power plant types recorded in the GEC Model and their installed capacities are represented in the hourly model, including existing and new fossil-fuelled power plants, nuclear plants and 16 different renewable energy technologies. The fleet of power plants that is available in each year is determined in the GEC Model and differs by scenario, depending on the prevalent policy framework. These plants are then made available to the hourly model and are dispatched (or chosen to operate) on the basis of the short-run marginal operating costs of each plant (which are mainly determined by fuel costs as projected in the GEC Model) to the extent required to meet demand. The dispatch operates under constraints: there are minimum generation levels to ensure the flexibility and stability of the power system and to meet other needs (such as CHP); the variability of renewable resources (such as wind and solar) determines the availability of variable renewables and, hence, the maximum output at any point in time; and ramping constraints apply, derived from the level of output in the preceding hour and the characteristics of different types of power plants. The hourly dispatch model does not represent the transmission and distribution system, nor grid bottlenecks, cross-border flows or the flow of power through the grid. It therefore simulates systems that are able to achieve full integration across balancing areas in each GEC Model region (e.g. United States, European Union, China and India).

Methodology
The analysis is conducted separately for each region represented in the GEC Model. These regional results are then aggregated to provide data for advanced economies and EMDE. This methodology is applied only to historical data and is not used for future projections. National household energy consumption and expenditure data are harmonised, extrapolated and combined to develop regional energy consumption profiles by income bracket for each fuel used in the residential sector and private transport. In regions facing energy access challenges, these consumption profiles are adjusted to account for energy access data from the GEC Model, based on the assumption that those without access are in the lower income brackets. These profiles are then applied to average household energy consumption data from the GEC Model. End-user prices by fuel and sector are subsequently applied to this data, yielding energy expenditures per household by income bracket.

Overview of affordability analysis methodology applied at each GEC Model region
level

After applying the methodology to all regions of the GEC Model, the results are aggregated to provide data for advanced economies and EMDEs. It is important to note that new disposable income distributions are not constructed at the aggregate level. Instead, aggregate values are calculated as the weighted average of the corresponding quantile data at the regional level, with the weights based on the number of households in each region. For instance, the energy consumption of the poorest 10% in EMDEs is the weighted average of the energy consumed by the poorest 10% in each of the countries within that group. This approach ensures that no single country is overrepresented within any income group.

Source:https://www.iea.org/reports/global-energy-and-climate-model