Time horizon in PyPSA-Eur

[Raul Ernesto]

Dear PyPSA group,

I have a question about the time horizon applied in PyPSA-Eur.

I am currently applying PyPSA-Eur for a study in the Iberian Peninsula (i.e., Spain and Portugal). From the documentation and main papers related to PyPSA-Eur it is clear that the model assumes a short-term time horizon (due to its high resolution in geographic, temporal and techno-economical parameters).
My questions are:

• Could the time horizon of the model be modified towards the long term? 
• What challenges in terms of coding, input data handling and computational effort would be faced in trying it?
• Is it mandatory to reduce the geographical and temporal resolution of the model to make it feasible to run it with a long-term focus on Iberia?
• Does the possible improvement in results by these changes justify all the required effort?

We are starting with PyPSA-EUR in our research group and hope to use it to answer relevant research questions in the Iberian energy system.

Thank you very much for your kind reply

Best regards,

Raúl Gutiérrez

[Fabian Neumann]

Hi Raúl,

What do you mean by short-term horizon?

PyPSA-Eur focuses on long-term capacity expansion planning, either as greenfield, myopic or (very recently) perfect foresight pathway optimisation.

Especially for the latter, you will have to make some compromises on the spatio-temporal resolution.

Best wishes,

Fabian N

[Raul Ernesto]

Hello Fabian,

Thank you very much for your comment, and I am sorry for the delay in getting back to you (I have taken a few days out of the office).

By short-term time horizon, I mean the time frame in which the model is applied. In the literature, PyPSA is considered a short-term model [1] since although it forecasts a target year in the future, it does not analyse the transition in different steps until reaching it [2]. I assume the same consideration also extends to PyPSA-Eur. Furthermore, I understand that due to the high geographical and temporal resolution of PyPSA-Eur, it may be challenging to move it to a long-term horizon.  Consequently, I asked you in that sense

Thanks again for your valuable help

Best regards,
Raul Gutierrez-Alvarez

REFERENCES

[1]  Ringkjøb HK, Haugan PM, Solbrekke IM. A review of modelling tools for energy and electricity systems with large shares of variable renewables. Renewable and Sustainable Energy Reviews 2018;96:440–59. https://doi.org/10.1016/J.RSER.2018.08.002.

[2]  Prina MG, Manzolini G, Moser D, Nastasi B, Sparber W. Classification and challenges of bottom-up energy system models - A review. Renewable and Sustainable Energy Reviews 2020;129:109917. https://doi.org/10.1016/J.RSER.2020.109917.

[Fabian Neumann]

Hi Raul,

The assessment in the papers you reference is outdated. PyPSA and PyPSA-Eur can also do pathway optimisation now with myopic and perfect foresight.

It is correct that you must make compromises between spatial, temporal, technological and pathway steps detail, especially with perfect foresight pathway optimisation.

Best wishes,

Fabian N

[Raul Ernesto]

Hi Fabian,

Thank you very much for the clarification. We will continue working with PyPSA-Eur and return to this valuable group if a new question arises.

Best regards,
Raúl Gutiérrez Alvarez