renewable energy resources. laura cooke.pdf · 2018. 11. 2. · 4. cradden and mcdermott (2018): a...
TRANSCRIPT
INTRODUCTION
Proposed Tool to Quantify Uncertainty in FutureRenewable Energy Resources and Demand
Laura Cooke 1 ([email protected]), Conor Sweeney 1 and Frank McDermott 2
UCD School of Mathematics and Statistics 1, UCD School of Earth Sciences 2
OVERVIEW
ACKNOWLEDGEMENTThis publication has emanated from research conducted with the financial support of Science Foundation Ireland under the SFI Strategic Partnership Programme Grant Number SFI/15/SPP/E3125. The opinions, findings and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of the Science Foundation Ireland.
• Build a tool that gives probabilisitic projections of both energy demand and renewable energy resources in Ireland under different emission scenarios.
Wind capacity model2
Solar capacity model3
Energy Demandmodel4
energy.mit.edu
www.irishtimes.com
Downscaled Climate ensemble data under each
emission scenario
Emission scenarios applied to multi-climate model ensembles give us a range of future projections with uncertainties:
www.iwea.com
• Wind• Shortwave radiation• Temperature
• Temperature• Wind• Illumination
REFERENCES
1. IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
2. Canon et al (2015): Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain. Renewable Energy, 75.
3. Pfenninger and Staffell (2016): Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy, 114.
4. Cradden and McDermott (2018): A weather regime characterisation of Irish wind generations and electricity demand in winters 2009-11. Env. Res. Letters,, 13.
• We are in the early stages of building a tool to quantify projections with uncertainty bounds of wind/solar resources and demand in Ireland.
• Input from potential users at this early stage is welcomed!
• Climate models are run under different emission scenarios.
• Using a range of climate models (ensemble) gives us a probabilistic view of the future for each emission scenario.
• While we have reasonably high confidence in temperature projections, we are less confident about wind and shortwave radiation.
• In planning for future energy systems we need to understand how this probabilistic view of the climate influences renewable resources and demand projections.
• Evaluate a range of regional downscaled (12.5km, 3hrly) climate models against historical atmospheric reanalysis data, MÉRA and ERA-Interim.
• Build wind2 and solar3 capacity models for Ireland using reanalysis data.
• Build hourly energy demand regression model4 for all Ireland using demand data and reanalysis data.
• Apply downscaled (12.5km, 3hrly) ensemble climate projections (2021-2040, 2081-2100) under emission scenarios (RCP2.6, RCP4.5, RCP8.5) to models.
• Concatenate into a tool that quantifies probabilistic projections of wind/solar capacity and demand.
OBJECTIVE
METHODOLOGY
Figure SPM.7 (a) from IPPC AR5 report1 showing multi-model projected change in global annual mean surface temperature and a measure of uncertainty (shading) relative to 1986-2005. Number of models used is indicated for each scenario.
A tool that quantifies wind and solar power supply and demand projections with uncertainties under different emission scenarios.
IPCC Temperature projections under RCP emission scenarios
In addition, provide typical profiles under specific weather regimes or extreme events:
www.irishtimes.com
Eg. Scandinavian Blocking4 regime or Extreme Cold Snap