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https://github.com/fneum/price-formation

Price Formation in 100% Variable Renewable Energy Systems
https://github.com/fneum/price-formation

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Price Formation in 100% Variable Renewable Energy Systems

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# 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)](https://doi.org/10.17864/1947.000321).



The techno-economic assumptions about costs and efficiencies are taken from

[`technology-data`

(v0.8.1)](https://github.com/PyPSA/technology-data/tree/v0.8.1), which largely

come from the [Danish Energy

Agency](https://ens.dk/en/our-services/technology-catalogues).



The assumptions about demand elasticity are taken from [Hirth et al.

(2024)](https://doi.org/10.1016/j.eneco.2024.107652) and [Arnold

(2023)](https://www.ewi.uni-koeln.de/en/publications/on-the-functional-form-of-short-term-electricity-demand-response-insights-from-high-price-years-in-germany-2/).



## Installation



Use [`conda`](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) 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`](`./LICENSE`).