Energy modelling

Energy models provide a factual basis for energy transition policies; they calculate either the effects of policy or an optimal way to achieve a certain goal.

A good factual basis is very important for shaping well-founded policies for the energy transition. The facts in question are the characteristics of different technical options, such as different types of renewable energy, energy saving for all possible applications and CO2 storage. Important aspects are the costs of these technical options and the maximum amount that can be deployed per option, i.e. the potentials. These costs and potentials form the basis of models that are used to calculate the current and future energy system. Models are always a simplified representation of reality and often focus on part of the energy system, such as a certain economic sector, a certain technology or the electricity supply. Shaping the energy transition requires models that encompass the entire energy system. Two types of energy system models used on a national scale are (1) simulation models that calculate the effects of energy and climate policy (such as the calculation of the Dutch Climate Agreement and the annual Climate and Energy Outlook) and (2) optimization models that minimize the costs of achieving a certain goal (such as OPERA).

Energy models do not provide predictions, but an estimate that depends on the input data used.

Results of energy models for calculating policy effects are sometimes seen as predictions. However, the results are not a forecast, but an estimate. It is important to be clear about the input data and the calculation method, and to emphasize that the input data and assumptions used lead to the final result of the estimate. With a certain margin of uncertainty, models can show the contribution of energy saving, the share of different forms of renewable energy and the reduction of greenhouse gas emissions for various policy variants.

National energy models calculate the effects of policy choices against exogenous developments or of policy choices when looking for an optimal way to achieve a certain goal.

With simulation models that calculate the effects of policy, some developments are considered given, such as future economic growth, population growth, international energy and CO2 prices, and technology costs and efficiencies. Policy measures, e.g. energy tax and subsidies, are influenced by the government, and the effects of different variants can be calculated against external developments. The period within which policy calculations are useful is limited to 10 to 15 years. A longer period is not desirable because the effects of the policy in combination with external boundary conditions for the model, such as economic growth and energy prices, lead to an excessive margin of uncertainty.

Figure 1. Example of the result of a simulation model – the development of electricity production up to 2030 (Source: Climate and Energy Outlook 2019)

In addition to models for calculating the effects of energy and climate policy, there are optimization models. Those models are based on technical and economic data on emission reduction options. These models can help you find transition paths to reach a goal at the lowest cost. For example, it is possible to calculate with which technical options you can halve greenhouse gas emissions at the lowest possible costs by 2030, or achieve climate neutrality by 2050. Optimization models are suitable for the longer term because scenarios with differences in exogenous developments can be included, such as differences in economic growth, international energy prices and in volume policy. Uncertainty about technical developments does increase the further into the future one wants to look. These types of models include all greenhouse gas emission reduction options, so all forms of renewable energy, energy saving options in households, offices, industry, transport and agriculture, energy storage methods, CO2 storage and nuclear energy. Learning curves are used to estimate cost reductions and productivity improvements. So-called volume policy, which limits the size of energy-using activities (such as the kilometer charge), is usually not included, because it is assumed that the existing demand for energy-using functions must be met. An exception to this is pricing in the form of energy tax, which does aim to reduce energy consumption. If all options are allowed, the goal will be achieved at the lowest cost, and probably also the fastest and with the least spatial impact. 

Figure 2. Example of result of an optimization model – cost curve with potentials of emission reduction options ordered by price per avoided ton of CO2 (Source: OPERA)
The effects of leaving out options will become clear when using optimization models.

As soon as options are limited or excluded, such as nuclear energy, biofuels, onshore wind, solar meadows, CO2 storage or import of renewable energy, achieving the goal may become more expensive, increase the spatial impact and/or the feasibility may be challenged. Excluding options is a policy choice. Energy models can be used to calculate the effects of those choices. Models simulating policy can be used to analyze the effects on the costs for different sectors and households.

Transparency is of great importance for the acceptance of model outcomes.

Transparency with regard to the operation of energy models and with regard to the technical and economic data of options are important for the acceptance of the model outcomes. The value of the outcome of the model stands or falls naturally with the quality of the techno-economic information about the options. The features of the options can be seen in fact sheets that can be found on this website. Other information, which is important for policy considerations, such as the sustainability of biofuels, is not included. These types of topics are covered in the ‘Themes’ on this site. Descriptions are available for the models that are used for the Dutch Climate and Energy Outlook.

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