The Case for Simple Simulation: Stochastic Market Simulation to Assess Renewable Business Cases

Abstract

Purpose of Review

This paper argues that, for the purpose of determining business cases for renewable energy assets, simple simulation methods can be very valuable. We demonstrate this through the application of a stochastic agent-based energy market model which, instead of a modelling the explicit drivers behind future investments, treats future capacity levels as stochastic parameters.

Recent Findings

In the last decade, increasingly complex models have been proposed to analyze the interaction between investment in and operation of energy assets by multiple market participants under uncertainty. This includes multi-stage optimization, equilibrium, and agent-based models. These models have their uses but are often not directly suitable for informing real-world investment analysis. They still do not capture all relevant uncertainties and features of real-world investment decision-making, are difficult to interrogate and explain to non-technical decision-makers, and have a high computational cost.

Summary

We have applied a newly developed stochastic energy market simulator, EYE, to the Dutch energy system, to demonstrate the usefulness of a simpler approach. We find that being able to easily include a wide range of uncertainties has clear value, as there are important interactions between uncertainties. We also note that results from a modelling exercise like this are easily explainable, and can help decision-makers. We suggest that future research into energy systems models needs to focus not just on complexity, but also on simplicity and the needs of real-world decision-makers, without losing sight of the multi-level nature of energy system investment. Choices between adding more specific realism and simplifying to allow for, e.g., capturing a broader range of uncertainties need to be made much more explicitly.