PyPSA / PyPSA/powerplantmatching

UK hydro plants incorrectly labeled as "Run-Of-River" despite being reservoir or pumped storage

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Python
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Description

Description

In the UK portion of the powerplant dataset used in powerplantmatching, several hydroelectric power stations are classified with:

  • component (or Set) = "store" (indicating dispatchable hydro), and
  • technology = "Run-Of-River"

This classification is inconsistent. Run-of-river (RoR) hydro plants do not have meaningful storage capacity and should not be modeled as dispatchable store components. Many of these stations are in fact reservoir-based or pumped-storage (PHS) hydro plants and should be reclassified accordingly. This problem also exists in the data of other countires.

Affected entries

Image

Public data sources

Suggested fix

  • Update the technology field to "Pumped Storage" or "Reservoir" where appropriate in manual_correction.csv
  • Optionally implement a preprocessing check to catch inconsistencies where component == "store" but technology == "Run-Of-River"

Why this matters

Incorrect classification leads to incorrect assumptions about generator and storage capacity in PyPSA-Eur. These misclassified hydro plants may distort model outcomes, particularly in scenarios involving high renewable penetration and reliance on dispatchable resources.

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Inspect the affected UK entries in manual_correction.csv and compare their technology and component/Set values with the listed public sources for Dinorwig, Ffestiniog, Sloy, Foyers, Glendoe, and Errochty. Reclassify reservoir and pumped-storage plants consistently, then check whether a preprocessing validation is appropriate for store components labeled Run-Of-River.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data-engineering
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
42/100

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