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- Climate proofing the renewable electricity deployment in EuropePublication . Simões, Sofia; Amorim, Filipa; Siggini, Gildas; Saint-Drenan, Yves-Marie; Sessa, Valentina; Carvalho, S.C.P.; Mraihi, Hamza; Assoumou, EdiClimate and weather conditions influence energy demand. as well as electricity generation, especially due to the strong development of renewable energy. The changes of the European energy mix, together with ongoing climate change, raise a number of questions on impact on the electricity sector. In this paper we present results for the whole of the European power sector regarding on how considering current and future climate variability affects the results of a TIMES energy system model for the whole European power sector (eTIMES-EU) up to 2050. For each member-state we consider six climate projections to generate future capacity factors for wind, solar and hydro power generation. as well as temperature impact on electricity demand for heating and cooling. These are input into the eTIMES-EU model to assess how climate affects the optimal operation of the power system and if current EU-wide RES and emissions target deployment may be affected. Results show that although at EU-wide level there are no substantial changes, there are significant differences in countries RES deployment (especially wind and solar) and in electricity trade.
- How sensitive is a carbon-neutral power sector to climate change? The interplay between hydro, solar and wind for PortugalPublication . Fortes, Patricia; Simoes, Sofia; Amorim, Filipa; Siggini, Gildas; Sessa, Valentina; Saint-Drenan, Yves-Marie; Carvalho, Silvia; Mujtaba, Babar; Diogo, Paulo; Assoumou, EdiABSTRACT: 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.
- Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower GenerationPublication . Sessa, Valentina; Bossy, Mireille; Simoes, SofiaABSTRACT: 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.