Forecasting the European wind drought of winter 2016/17 Case study

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Presentation transcript:

Forecasting the European wind drought of winter 2016/17 Case study Llorenç Lledó llledo@bsc.es

Winter 2016/17 Oct 2016 Nov 2016 Dec 2016 Jan 2017

2016-Q4 anomaly

Event impacts Lack of wind power production Mince precipitations and less water for hydropower Cold temperatures increase electricity demand Power prices rise (coincidental with nuclear stop in France)

Why? Fast evolution Slow evolution Bad luck ATMOSPHERE SOIL MOISTURE SEA ICE EXTENT OCEAN CIRCULATION SNOW COVER Bad luck A slow component is forcing the atmosphere

A practical example: US drought Q1-2015 SST and precip High SSTs in western tropical pacific ↓ Enhanced convection in the area Upward flow produces divergence at 200hPa Rossby wave propagates to North America Results in persistent low winds January-March 2015 GH @200hPa (images for March 2015)

How to forecast wind averages? Use a coupled Earth System Model: - Forecast slow evolution fields + impact on fast evolution - Several ensemble members to get rid of uncertainty Bias adjust/calibrate ensemble members Average whole season to filter out noise and get signal Distribute the ensemble members into 3 categories Compute probabilities A simple example of what we can do.

DJF 2016/17 forecasts www.bsc.es/ess/resilience

Point forecasts www.bsc.es/ess/resilience

Potential applications O&M planning Energy trading Balance supply and demand Cash flow anticipation We want to serve society. We want to identify problems. First identify stakeholders.

Stay tuned Find the case study report at C4E website: http://clim4energy.climate.copernicus.eu/wind-power Check the 2016/17 DJF forecasts at: www.bsc.es/ess/resilience http://c4e-visu.ipsl.upmc.fr/ Further investigation of the case under:

Forecasting the European wind drought of winter 2016/17 Case study Llorenç Lledó llledo@bsc.es

Capacity factors Capacity factor is a good indicator of wind power generation. Is independent of: number of installed turbines nameplate capacity of installed turbines Using manufacturer power curves for three turbines representing IEC classes. Fed with: 6-hourly model data, sheared at 100m.

SKILL ASSESSMENT Skill assessment for DJF (1981-2013) Displaying: Ranked Probability Skill Score [RPSS] “A prediction has no value without an estimate of forecasting skill based on past performance”

SITE SPECIFIC FORECASTS Retrospective forecasts for JJA at Site1 (2000-2015) CRPSS = -0.01 RPSS = 0.08 EnsCorr = 0.44