The use of economic and energy system modelling for energy policy analysis

Illustration of city with renewable energy and people

Dr Christian Calvillo, Research Associate, CEP Dr Christian Calvillo
Research Associate, Centre for Energy Policy
christian.calvillo@strath.ac.uk
 Dr Gioele Figus, Research Associate, CEP

Dr Gioele Figus
Research Associate, Centre for Energy Policy
gioele.figus@strath.ac.uk

1 June 2018

Establishing links across the energy system- and economic-modelling communities

The ambitious climate targets set by governments and the interconnection between energy and economic objectives requires careful policy planning and assessment. Among other instruments, energy and climate specialists often rely on models to inform their policy recommendations. Whole energy system models, like TIMES, and energy-economic models, such as Computable General Equilibrium (CGE) have been widely used to guide government decisions and assist the implementation of policies.

Each type of model presents different features and has strengths and limitations. However, the interaction of models, mainly using a ‘soft-linking’ approach, is recognised as a potential solution to overcome some of the drawbacks of the standalone models and to improve the depth of analyses. It consists of using information from one model to inform the other in iteration until the two models are harmonised.

Sharing experiences in the use of TIMES and CGE models

The University of Strathclyde’s Centre for Energy Policy and Fraser of Allander Institute are currently investigating best practices and challenges on the use and links of TIMES/CGE models for the analysis of future climate and energy scenarios in Scotland. The research is supported by the Scottish Government Centre of Expertise on Climate Change (ClimateXChange). Preliminary results from the research were discussed during a one-day workshop on 22 May 2018.

The event brought together stakeholders and researchers from different countries, to discuss practical challenges and share experiences. Presenters in the workshop included: Andrew Mortimer (Scottish Government), Patricia Fortes (New University Lisbon), Matthew Winning (University College London), Anna Krook-Riekkola (Luleå University of Technology), James Glynn (University College Cork), Anna Darmani (InnoEnergy), and the two organisers of the event Gioele Figus and Christian Calvillo (University of Strathclyde).

Each presentation provided a valuable point of view in the debate. Although Portugal, Sweden, Ireland and Scotland may have differing policy objectives, the presentations have highlighted common methodological practices and challenges that are applicable across different countries and regions. 

The process of linking the two models and using them in an iterative fashion raises a number of challenges that the Strathclyde team have summarised as follows:

  • Model calibration, updating and data problems: Using TIMES and CGE is a dynamic process that requires constant updating and development. This requires considerable effort and time. Moreover, the data to calibrate and update the models is not always available, and this further complicates the process.
  • Validation of model improvements: When models are developed to improve the analysis of specific policies (e.g. modifying the CGE model to better represent the power system), it is difficult to check the reliability of the outputs. This is because the data might not be available to validate the changes made.
  • Fundamental differences in the models’ objectives: The two models have been created with different objectives. TIMES chooses the technology mix that minimises the cost of delivering energy to a specific country or region. The CGE considers firms within the same region that produce goods and services by maximising their profit and selling them to consumers. To reconcile the two objectives is not always an easy task.
  • The soft-linking methodology is not straightforward: What to link, and how? Which one is the dominant model? Where to start? Convergence criteria? These are all important questions that do not have a single generic solution, as this depends on the application and analysis objectives.
  • Data harmonisation for soft-linking: Mapping sectors and variables from one model to the other is not straightforward and it is likely to be different in both directions. For instance, the two models express energy in two different units of measure, monetary versus physical units.
  • Sector bias in models: Researchers often focus on improving one single aspect of the two models, such as the treatment of heat or a particular industry. However, this could create imbalances in the solutions and potential reliability of the model, when a holistic approach is not considered.
  • When to stop?: Further improvements are always possible, but it is not always clear when new changes in the models are adding value to the analysis, making it worth the effort, and when changes could bias the solutions.

There are clearly many challenges in the use of these two modelling families and the participants to the workshop have agreed to continue to collaborate and share information. However, some best practices have already emerged. These are:

  • The importance of the baseline scenario. Each model considers a baseline against which counterfactual scenarios can be assessed. The consistency of the baseline in the two models is fundamental for a successful soft-link.
  • Soft-linking is not always the solution. Some questions can be answered directly with one model or the other, removing the need of complicated soft-linking processes. Here, it is fundamental to have specialists with enough knowledge of the two models to assess which instrument is the most appropriate.
  • Clear timeframes for policy decisions help with choosing the appropriate model. As discussed previously, different models have been developed with different objectives and time horizons. For instance, TIMES can be used to answer how the energy system will look in 30 years’ time, but it might not be the best tool to assess the changes in the energy system in next one to two years. It is, therefore, important to understand this in order to use the best tool for the problem at hand.

The main ‘take away’ point from this workshop is that an active and cohesive community of experts is a prerequisite for the correct and informed use of complex modelling techniques to support policy analysis. The participants in the workshop have agreed to increase the level of engagement by setting up forums and shared repositories of documents. Moreover, a second meeting will take place this time next year to extend discussions and consider the progress made by the Strathclyde team and by the rest of the community.

Tags: News & Blogs