Integration of Climate Data in the SAVi Energy Model

The document details how climate variables from the Copernicus Climate Data Store (CDS) are integrated into the SAVi Energy model to quantify the impact of climate change on various power generation technologies. It provides a comprehensive literature review of how variables like temperature, wind speed, and precipitation affect the load factor and thermal efficiency of coal, gas, nuclear, biomass, hydro, solar, and wind power, as well as the efficiency of the electricity grid. The report includes a simulation for Johannesburg, South Africa, demonstrating that rising temperatures and changing precipitation patterns can significantly reduce the load factor and revenues for thermal and hydropower assets, while potentially increasing the load factor for wind energy.

• Wind power generation is sensitive to wind speed; turbines fail to produce electricity when wind is too low and lose efficiency or stop generating when wind speed exceeds a cut-out limit.
• Solar PV efficiency is negatively impacted by air temperature and positively influenced by solar radiation and wind speed. Literature suggests a 1°C increase in temperature can decrease efficiency by up to 2%, while a 1 unit (W/m2) increase in irradiance can increase output by 5%.
• Dust and ash accumulation on solar panels significantly reduces efficiency; for example, ash accumulation of 0.4 mg/cm2 can lead to a 1.5% efficiency decrease or nearly 30% energy reduction per hour.
• Hydropower generation is highly sensitive to precipitation and river discharge, with a 1% change in either typically resulting in a 1% change in power generation.
• Nuclear power plant efficiency and output are negatively affected by increasing water temperatures used for cooling. An increase of 1°C in cooling water inlet temperature can decrease thermal efficiency by 0.16% and net power output by approximately 0.39%.
• Electricity grid efficiency declines as air temperature increases, with network capacity falling by approximately 1% for each 1°C of temperature increase.
• In a simulation for Johannesburg, South Africa, climate impacts are projected to cause significant load factor reductions for thermal producers (coal, gas, nuclear) starting between 2050 and 2060, with coal's real load factor potentially dropping from 88.64% to 60.65% by 2100.
• The Johannesburg simulation indicates that while thermal and solar load factors decline, wind power load factors are expected to increase on average by 4.5% by 2100 due to higher future wind speeds.
• Economic analysis of the Johannesburg simulation shows a net reduction between 5.15% and 8.54% in cumulative revenues for most technologies due to lower capacity utilization, except for onshore wind, which shows a 9.88% increase in cumulative revenues.
• Reduced grid efficiency due to temperature increases is projected to reduce average annual revenues for power producers in the Johannesburg context by 3.4% compared to a no-climate scenario, totaling ZAR 10.42 billion in cumulative losses between 2022 and 2100.

APA

Perera, O., Bechauf, R., Casier, L., Lago, S., Perrette, M., Uzsoki, D., Wuennenberg, L., Bassi, A. M., & Pallaske, G. (2020). Integration of Climate Data in the SAVi Energy Model. International Institute for Sustainable Development. https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-energy-model.pdf

Chicago

Perera, Oshani, Ronja Bechauf, Liesbeth Casier, Sergio Lago, Mahé Perrette, David Uzsoki, Laurin Wuennenberg, Andrea M. Bassi, and Georg Pallaske. Integration of Climate Data in the SAVi Energy Model. International Institute for Sustainable Development, 2020. https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-energy-model.pdf.

Wikipedia

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BibTeX

@techreport{perera2020integration, author = {Perera, Oshani and Bechauf, Ronja and Casier, Liesbeth and Lago, Sergio and Perrette, Mahé and Uzsoki, David and Wuennenberg, Laurin and Bassi, Andrea M. and Pallaske, Georg}, title = {{Integration of Climate Data in the SAVi Energy Model}}, institution = {International Institute for Sustainable Development}, year = {2020}, month = sep, url = {https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-energy-model.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }