This page grew out of the Hydro Modelling Breakout Group at the 3rd openmod workshop in London, 2015.
Introduction
Hydroelectricity is still the dominant source of renewable electricity in many countries (around 17% share of European electricity, if you include Norway and Switzerland).
Technically it can be very flexible and smooth out variable production of wind and solar, so for future scenarios with high RES, it’s important to get right.
For market models hydro dispatch strategy can have a big influence on market prices (e.g. in the Alps and Scandinavia), which is hard to capture.
There are different types of hydroelectric plants:
- Run-of-river plants with little storage
- Storage dams with inflow
- Pumped storage
- Mixed variants (e.g. storage dams with inflow and pumping)
Hydroelectricity modelling is complicated by several factors:
- Although it has low marginal cost, the storage allows dispatch to be time-shifted, which makes optimal dispatch strategies complicated
- Plants on rivers can be chained, so that the inflow depends on the output of power plants upstream
- Hydro inflow varies year-by-year (e.g. there are dry and wet years in Scandinavia)
- Modelling inflow from weather data is non-trivial: need to model precipitation, runoff, evaporation, snow melt, etc.
- Water in reservoirs is tapped for other purposes, e.g. irrigation.
- There are other constraints, such as maintaining navigability on rivers, fish ladders, water levels for recreation, water cooling for thermal power plants, etc.
Hydroelectric modelling
In a linear programming setup, hydroelectric dams can be modelled as storage units with state of charge limits, set inflow, controllable spillage and optional pumping.
The chaining of run-of-river plants, multiple turbines fed from the same reservoir, can all be modelled too.
More detail can be found in books such as
A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, "Power Generation, Operation and Control," New York: John Wiley & Sons, Third Edition, 2014.
Hydroelectric power plant static data
Desired data for power plants
- Plant type (run of river, pumped storage, storage dam)
- Dispatch capacity (MW)
- Pumping capacity (if present) (MW)
- Storage capacity (litres or MWh)
- (head) Height of reservoir (m)
- Type of inflow
- Inflow time series
- Legal restrictions on flow levels (maintaining enough water for nature)
- minimal resevoir level
- (black start reserves - capacity reserves)
- Pumped storage efficiency
- Chaining run-of-river
- Some reservoirs have several outlets
Global datasets
Wikipedia list of hydro stations > 2000 MW
List of largest hydroelectric power stations
Wikipedia list of pumped hydro stations > 1000 MW
Wikipedia List of pumped storage hydro stations (greater than 1000 MW)
European datasets
Eurelectric statistics
http://www.eurelectric.org/powerstats2013/
http://www.eurelectric.org/hydro-factsheets
Renpass for Norway and Germany
http://renpass.eu/files/public-docs/renpass_installation/
SQL database of hydroelectric power plants (storage and run of river) in Norway and Germany.
Swiss data
Swiss Federal Office of Energy: Yearly Hydropower statistic for Switzerland:
http://www.bfe.admin.ch/themen/00490/00491/index.html?lang=en&dossier_id=01049
Geographic information on Hydropower in Switzerland:
http://www.bfe-gis.admin.ch/storymaps/WK_WASTA/index.php?lang=de
Swiss Federal Office of Environment Runoff data for Switzerland:
http://www.bafu.admin.ch/wasser/13462/13496/15016/index.html?lang=en
Wikipedia per-country European capacities
Germany
Switzerland (not complete)
Hydroelectric inflow time series data
Deriving time series from weather data
Inflow time series can in principle be derived if precipitation, run-off, temperature for snow melt, transpiration, ground height and orientation, and other water uses are all known.
Reanalysis datasets such as the CFSR contain data on runoff, precipitable water, precipitation rate.
Global datasets
Global Runoff Data Centre
The Global Runoff Data Centre (GRDC) has measured daily average discharge data for 7362 measurement stations worldwide.
Used in Yvonne Scholz PhD thesis, 2012.
WaterGAP
WaterGAP (Water Global Assessment and Prognosis) is a global fresh water resource model, which models both hydrology and the usage of water in five sectors (irrigation, livestock, households, manufacturing and cooling of thermal power plants).
All computations are done with a temporal resolution of 1 day and a spatial resolution of 0.5° geographical latitude × 0.5° geographical longitude, which is equivalent to 55 km × 55 km at the equator.
Currently the years 19** to 2010 are covered; more recent years will be updated soon (as of 2016).
River discharge is available for run-of-river power plants and inflow to storage reservoirs.
The model has been developed by University of Kassel (Germany) since 1996, and since 2003 also at the University of Frankfurt (Germany).
The source code is not free software, nor is it available for download.
Datasets are available on request to researchers.
Wikipedia: WaterGAP
WaterGAP
https://www.uni-frankfurt.de/45218063/WaterGAP
http://www.eea.europa.eu/data-and-maps/data/external/input-data-to-watergap-model-4
Used in Gregor Czisch PhD thesis, 2006.
European datasets
List of datasets at Open Power Systems Data project
http://open-power-system-data.org/data-sources#Hydro_power_data
Inflow in Austria
http://ehyd.gv.at/
Inflow in Bavaria, Germany
Inflow in Bavaria from the Bavarian Hydrological Service
