Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling

Stian Backe,Mohammadreza Ahang,Asgeir Tomasgard

doi:10.1016/j.apenergy.2021.117538

Stian Backe, Mohammadreza Ahang + Show 1 more

Open Access

https://doi.org/10.1016/j.apenergy.2021.117538

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Abstract

This paper examines the importance of including operational scenarios representing short-term stochasticity in the long-term capacity expansion models with high shares of variable renewables. As scenario generation routines often are probabilistic, for example based on sampling, it is crucial that they ensure stable results in the capacity expansion model, so that it is the underlying uncertainty that decides the optimal solution, and not the approximation of that uncertainty in the model. However, it is unclear which operational scenario properties that are important to ensure good results and stability in stochastic models. This paper evaluates three sampling-based scenario generation routines in a multi-horizon stochastic capacity expansion problem representing the European electricity system. We compare the use of stochastic versus deterministic modelling with high shares of variable renewables. Further, we perform in-sample and out-of-sample stability tests on 90 scenario trees for each routine, and we compare the routines’ ability to produce stable system costs and capacity investments when approximating the optimal value from the real distribution. Results show that stochastic modelling with more than 80% share of variable renewables leads to more investments in both dispatchable and variable renewable capacity compared to deterministic modelling, which means that stochastic modelling should be used with very high shares of variable renewables. The scenario generation routine based on stratified sampling increases stability with the same number of operational scenarios compared to its alternatives, and scenario generation routines using stratified sampling should be further explored.

Highlights

The European energy roadmap 2050 specifies the level of greenhouse gas (GHG) emissions to be 80–95% of 1990 levels by 2050
We evaluate the value of the stochastic solution (VSS) for a scenario tree with 200 scenarios produced with the basic scenario generation routine (SGR)
This paper presents the impact of different scenario generation routines on results of the multi-horizon power system capacity expansion model EMPIRE

Summary

Introduction

The European energy roadmap 2050 specifies the level of greenhouse gas (GHG) emissions to be 80–95% of 1990 levels by 2050. Giglio [26] presents an early attempt of designing models that support capacity expansion decisions for a facility subject to uncertain demand and facility lifetime. Long-term planning of power systems with high share of VRES should take into account the stochasticity of different processes in order to find robust and stable solutions. Pineda and Morales [15] develop a stochastic expansion model representing a two-stage electricity market to consider wind forecasting errors, and they find that endogenous forecasting uncertainty leads to less VRES compared to no forecasting uncertainty. We are not able to use such a large tree in our model due to computational effort, SGR’s are designed to generate stochastic scenarios representative of true tree. We consider two SGR categories: statistical-based SGRs and measure-based SGRs

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