A framework for evaluating the impact of climate change on the levelised cost of wind energy Daniel
1,2,* Hdidouan,
Iain
2 Staffell,
Robert
2 Gross,
David
3 Brayshaw.
1Science
and Solutions for a Changing Planet DTP, 2Centre for Environmental Policy, Imperial College London, and 3Meteorology Department, University of Reading. *
[email protected]
Framework design
Transposing the change
Central to the performance of wind turbines is site selection; this dictates the amount of wind and proportion of time that the turbine is generating at its rated power output (capacity factor, CF). The CF is calculated using a historical time series of wind speed, by first creating a probability density function and then integrating it with a turbine’s rated power curve in the Virtual Wind Farm (VWF) model [1].
① calculate change
Historic GCM Ⓐ calculate correction
Capacity Factor
LCOE
Future Observed ② apply change
8%
NOAA Historic NOAA Future MERRA Historic MERRA Future
6%
Frequency of Occurance
Figure 1
Ⓑ apply correction
Historic Observed
The levelised cost of energy (LCOE) is a metric used to investigate the potential economic performance of an energy generating asset. Its main components are the discounted lifetime capex and opex divided by the total energy output of the asset [2]. Assuming costs remain constant, the LCOE is inversely proportional to the CF (as CF increases the LCOE decreases); expressed as £ GBP per MWh of energy. This framework is couples the VWF and LCOE model with GIS. This enhances the spatial resolution of the CF and LCOE estimates; producing maps as outputs (figures 1 & 2).
Future GCM
4%
2%
0% 0
5
10 15 20 Wind speed (m/s)
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This framework uses a new technique for transposing projected changes in wind resources shown in blue on top schematic (labels 1 & 2). Here the difference between NOAA historic and future (light blue on bottom graph) is applied to a historic reanalysis of wind speed (MERRA historic [4] to get MERRA future; dark blue on bottom graph). This is opposed to the traditional bias correction (orange labels A & B). This new method normalises GCM output to allow for easier comparison and analysis. The need for this comes from the intricate and unique design of GCMs resulting in varying abilities to simulate historic wind speeds.
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Framework findings • The wind’s response to increased climate forcing is non-linear. • North Atlantic and North Scotland have greatest increase in wind resource (largest fall in LCOE). • South England and English Channel have the greatest decrease in wind resource (largest rise in LCOE) • The stronger the radiative forcing, the greater the change in wind resource; • Climate signals are more pronounced later in the time series. a)
b)
Figure 2 2010 -2030
2040 -2060
2070 -2090
Figure 1: Current CF (Figure 1a) and LCOE (figure 1b) for the UK and surrounding waters. A VWF and LCOE model were used in GIS to create these figures respectively.
Using GCM data Climate change is predicted to effect wind resources. The CMIP5 global climate model (GCM) experiments [3] produced data outputs including time series of projected future wind speed under four reconstructed concentration pathway (RCP) scenarios, from business as usual (8.5) to rapid and deep decarbonisation (2.6) with two in between (4.5, 6.0). GCM data can be used as an input to this framework to provide insights on how climate change may impact the economics of wind energy. Findings presented here use one run from the NOAA-GFDL ESM2G model. The future time series, extending from years 2006-2100, has been sliced into three time periods: 2010-2030, 2040-2060, and 2070-2090 corresponding to current average turbine lifetimes.
Acknowledgements The authors are grateful for help from colleagues at Imperial College London and University of Reading who provided insightful comments. Daniel Hdidouan and Iain Staffell gratefully acknowledge funding from the Natural Environment Research Council and the UK Energy Research Centre (EP/L024756/1) respectively. For the CMIP5 model output, we acknowledge the climate modelling groups, WCRP-WGCM, and GO-ESSP.
Figure 2: Projected changes in LCOE over the UK during (a) 2010-2030; (b) 2040-2060; and (c) 20702090 for scenario RCP 6.0.
References [1] Staffell I and Green R, 2014. How does wind farm performance decline with age? Renewable Energy, 66: 775-786. [2] Heptonstall P, Gross R, Greenacre P and Cockerill T, 2012. The cost of offshore wind: Understanding the past and projecting the future. Energy Policy, 41: 815-821. [3] Dunne JP, John JG, Adcroft AJ, Griffies SM, et al., 2012. GFDL's ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics. Journal of Climate, 25(19): 6646-6665. [4] Olauson J and Bergkvist M, 2015. Modelling the Swedish wind power production using MERRA reanalysis data. Renewable Energy, 76(0): 717-725.
