European Research Area for Climate Services

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Less information, similar performance : comparing machine learning-based time series of wind power generation to renewables.ninja

Publication . Baumgartner, Johann; Gruber, Katharina; Simoes, Sofia; Saint-Drenan, Yves-Marie; Schmidt, Johannes

ABSTRACT: Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve-based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation, despite their need for accurate location information and bias correction, as well as their insufficient replication of extreme events and short-term power ramps. In this paper, we assessed how time series generated by machine learning models (MLMs) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we applied neural networks to one wind speed input dataset derived from MERRA2 reanalysis with no location information and two with additional location information. The resulting time series and RN time series were compared with actual generation. All MLM time series feature an equal or even better time series quality than RN, depending on the characteristics considered. We conclude that MLM models show a similar performance to RN, even when information on turbine locations and turbine types is unavailable.

2020Journal article

Open access

Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation

Publication . Sessa, Valentina; Bossy, Mireille; Simoes, Sofia

ABSTRACT: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.

2021-12Journal article

Open access

How sensitive is a carbon-neutral power sector to climate change? The interplay between hydro, solar and wind for Portugal

Publication . Fortes, Patricia; Simoes, Sofia; Amorim, Filipa; Siggini, Gildas; Sessa, Valentina; Saint-Drenan, Yves-Marie; Carvalho, Silvia; Mujtaba, Babar; Diogo, Paulo; Assoumou, Edi

ABSTRACT: Climate change will impact renewable resources and electricity demand, usually not jointly considered when designing future decarbonized power systems. This paper assesses how sensitive the Portuguese carbon-neutral power sector is to climate change by 2050 and what are the implications for the formally approved Portuguese Carbon Neutrality Roadmap. The future capacity factors for wind, solar and hydropower and electricity demand response to temperature are estimated for 22 climate projections along the Representative Concentration Pathway 4.5 and 8.5. The eTIMES_PT optimization model is used to assess its combined impact on the cost-optimal configuration of the power sector by 2050. Results show that climate change lowers hydropower generation by 20% (in median terms). Improving spatial and temporal resolution and including future climate patterns, results also in lower cost-effectiveness of solar photovoltaic vis-a-vis the Carbon Neutrality Roadmap. While future climate does not impact onshore wind production, offshore wind power generation is positively affected, being a climate-resilient carbon-neutral option for Portugal. Annual electricity unitary costs at final users (excluding taxes and levies) only increase up to 4% with climate change, but seasonal costs have higher variability. This analysis highlights that climate change affects the cost-optimal annual carbon-neutral power sector and needs to be included in energy planning.

2022-01Journal article

Open access

Competing water uses between agriculture and energy: Quantifying future climate change impacts for the Portuguese power sector

Publication . Fortes, Patricia; Simoes, Sofia; Brás, Teresa; Amorim, Filipa

ABSTRACT: Climate change may increase water needs for irrigation in southern Europe competing with other water uses, such as hydropower, which may likely be impacted by lower precipitation. Climate change will also potentially affect the variability and availability of other renewable energy resources (solar and wind) and electricity consumption patterns. This work quantifies the effect of competition for water use between irrigation and hydropower in the future 2050 Portuguese carbon-neutral power sector and under Representative Concentration Pathway 8.5 climate change projections. It uses the power system eTIMES_PT model to assess the combined effects of climate change on the cost-optimal configuration of the power sectorconsidering changes in irrigation, hydropower, wind and solar PV availability. eTIMES_PT is a linear optimisation model that satisfies electricity demand at minimal total power system cost. Results show that, by 2050, climate change can lead to an increase in annual irrigation water needs up to 12% in Tagus and 19% in Douro watersheds (from 2005 values), with substantially higher values for spring (up to 84%). Combining these increased water needs with the expected reduction in river runoff can lead to a decline in summer and spring hydropower capacity factors from half to three times below current values. By 2050, concurrent water uses under climate change can reduce hydropower generation by 26–56% less than historically observed, mainly in summer and spring. Higher solar PV, complemented with batteries’ electricity storage, can offset the lower hydropower availability, but this will lead to higher electricity prices. Adequate transboundary water management agreements and reducing water losses in irrigation systems will play a key role in mitigating climate impacts in both agriculture and power sector.

2022-10Journal article

Open access