In this chapter we focused on model designs from a system perspective. Using our energy system example, we gradually extended the included elements to end up with a full optimization model.

Optimization models are relatively easy to design and powerful tools to represent even large scale bottom-up type system representations. The main caveat is the need for a single objective that can be minimised or maximised. We learned that social costs or social welfare are common economic approaches to derive reasonable objectives in settings where more than one criteria needs to be considered.

If you think about the challenges we face in energy and environmental policy, there are a lot of problems that can be represented by some type of optimization model whether it is optimal power plant investment, the trade-off between environmental damage and the costs needed for abatement, or the impact that an energy tax would have on energy consumption. But also in other fields and even in your daily life a lot of aspects have properties of optimization problems. Just think about it the next time you buy groceries!

We also captured how a research question – the impact of emission prices and nuclear risk premiums – can be tackled using such an optimization model. Always keep in mind that your research problem comes first and the model needs to be designed to provide insights on this problem, not the other way round.

We will extend the details on modelling design by shifting the focus towards the company and market perspective in the next chapter; in other words, we will shift the focus to perspectives we would like to steer with our policies.