Abstract
Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.
Highlights
Hydro power is the largest renewable energy resource in Europe, accounting for 325,000 GWh of generation in 2017, equivalent to approximately 42% of EU renewable energy generation or 10.8%of European total net electricity generation [1]
Noticeable itmodel that noticeable lagged that values, both lagged optimal values, lag both and optimal lag lags, and lags, improves the model improves performance the improves inalso all performance cases, the confirming improves in all performance cases, that the climate confirming model improves in all performance factors cases, that the have climate confirming model ainflows in delayed all performance factors cases, that effect have climate confirming ain delayed all factors cases, that effect have climate confirming ainmultiple delayed factors that effect have climate ad based hydro power generation power isisdecision weaker generation than on isdecision inflows weaker than due to onpower due design to power and operator design and operator decision
We developed a new dataset of hydro power generation for reservoir-based and run-of-river generation using only two essential climate variables—temperature and precipitation—and a machine learning methodology, based on random forests, selected for its flexibility and accuracy
Summary
Hydro power is the largest renewable energy resource in Europe, accounting for 325,000 GWh of generation in 2017, equivalent to approximately 42% of EU renewable energy generation or 10.8%. Wind and solar power play an increasingly important role in a low-carbon energy plan Their weather-dependent nature requires a more stable energy source, together with storage solutions, to cope with climate variability. An accessible homogenous hydro power dataset for Europe over several decades, by extending the short periods (normally a few years) of publicly available power generation data, would provide an invaluable input to pan-European network assessment and planning. This is one of the goals of the Copernicus Climate Change Service (C3S) for the Energy.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
