Price formation without fuel costs: the interaction of elastic demand with storage bidding

Link to preprint: https://arxiv.org/TBC (in preparation)

Link to data: https://zenodo.org/records/12759248

Abstract

Studies looking at electricity market design for very high shares of wind and solar often conclude that the energy-only market will break down. Without fuel costs, it is said that there is nothing to set prices. Symptoms of breakdown include long phases of zero prices, scarcity prices too high to be politically acceptable, prices that collapse under small perturbations of capacities from the long-term equilibrium, cost recovery that is impossible due to low market values, high variability of revenue between different weather years, and difficulty operating long-term storage with limited foresight. We argue that all these problems are an artefact of modeling with perfectly inelastic demand. Even with a small amount of short-term elasticity representing today's flexible demand (-5\%), the problems are significantly reduced. The combined interaction of demand willingness to pay and storage opportunity costs is enough to produce stable pricing. Considering a simplified model with just wind, solar, batteries, and hydrogen-based storage, the price duration curve is significantly smoothed with a piecewise linear demand curve. This removes high price peaks, reduces the fraction of zero-price hours from 90\% to around 30\%, and guarantees more price stability for perturbations of capacity and different weather years. Fuels derived from green hydrogen take over the role of fossil fuels as the backup of final resort. Furthermore, we show that with elastic demand, the long-term model exactly reproduces the prices of the short-term model with the same capacities. We can then use insights from the long-term model to derive simple bidding strategies for storage so that we can also run the short-term model with limited operational foresight. We demonstrate this short-term operation in a model trained on 35 years of weather data and tested on another 35 years of unseen data. We conclude that the energy-only market can still play a key role in coordinating dispatch and investment in the future.

Data Sources

The solar and wind time series for 1950-2020 are taken from from Bloomfield and Brayshaw (2021).

The techno-economic assumptions about costs and efficiencies are taken from technology-data (v0.8.1), which largely come from the Danish Energy Agency.

The assumptions about demand elasticity are taken from Hirth et al. (2024) and Arnold (2023).

Installation

Use conda environment manager:

sh conda update conda conda env create -f workflow/envs/environment.fixed.yaml conda activate price-formation

The main dependencies are:

• pypsa (v0.27.1)
• linopy (v0.3.8)
• snakemake (v8.5)
• gurobi (v11.0.2)

Run

From root of repository:

sh snakemake -call --use-conda --conda-frontend conda

Or with specific scenario configuration file:

sh snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml

Cluster

On HPC cluster, run:

sh snakemake -call --profile slurm --use-conda --conda-frontend conda

Compress Results

Use tar to run (excluding report directory):

sh tar -cJf price-formation-results.tar.xz \ config data figures results resources workflow \ .gitignore .pre-commit-config.yaml .syncignore-receive \ .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md

License

The code in this repository is MIT licensed, see ./LICENSE.