The Case for Simple Simulation: Stochastic Market Simulation to Assess Renewable Business Cases
article
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. (C) 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Topics
Agent-based modellingEnergyMonte CarloUncertaintyAutonomous agentsComputational methodsDecision makingMonte Carlo methodsPower marketsRisk assessmentStochastic modelsStochastic systemUncertainty analysisAgent-based modelBusiness caseDecision makersEnergy systemsMonte carloReal-worldSimple++Investments
TNO Identifier
987602
ISSN
21963010
Source
Current Sustainable/Renewable Energy Reports
Publisher
Springer Nature
Files
To receive the publication files, please send an e-mail request to TNO Repository.