Pan-European Electricity Demand Modelling

Transcription

1 Pan-European Electricity Demand Modelling Laurent Dubus & Sandra Claudel (EDF R&D) Matteo De Felice (ENEA) Alberto Troccoli (UEA & WEMC) 06 March 2018

2 Outline 1. Our targets 2. Modelling approach: data & models 3. Historical Period: examples & validation 4. Demand Projections: examples 5. Conclusions

3 ECEM: A GLOBAL APPROACH Define Models / Transfer Functions Select / Gather relevant Datasets ENERGY (WP3) Hydro Power Solar Power Demand Thermal Power? Wind Power

4 1.OUR TARGETS q EU Countries Electricity Demand q Daily time resolution q , based on climate reanalysis q , based on climate projections & energy mixes scenarios

5 2.MODELLING APPROACH - PRINCIPLE Data (Bias Adjustment) Energy Data Simulated energy variables on long periods: Past (Reanalysis) Months ahead (Seasonal Forecasts) Long-term ( Projections) By using public and homogeneous data over the whole of Europe

6 2.MODELLING APPROACH - DATA Mainly from ENTSO-E for several reasons: - Transparency portal: most countries, hourly data, consistency in data formats... - Annual statistics: official & validated data But we used also other data sources: - The World Bank (annual demand data, long time series) - National TSOs (UK National Grid, FR RTE...) - thewindpower.net for wind farms database and power curves

7 2.DEMAND MODELLING HISTORICAL PERIOD ENTSO-E Monthly load data ~ ENTSO-E Daily load data ~ GAM model 1 Detrended Monthly Time-Series Daily interpolate & substract Detrended Daily Time-Series Full Demand (incl. trend) Daily interp & substract Detrended Daily Demand GAM model 2 using variables Detrended Anomaly Remove long-term mean

8 2.DEMAND MODELLING HISTORICAL PERIOD Training Validation France RMSE MWh MWh MAPE 2.1% 2.2%

9 2.DEMAND MODELLING - PROJECTIONS The detrended models are used with climate variables projections è detrended demand From this, the mean annual cycle is removed to compute relative anomalies and the 5 ehiwgway demand scenarios are used to generate «actual» demand daily time series

10 2.DEMAND MODELLING - PROJECTIONS

11 3.HISTORICAL PERIOD: EXAMPLES

12 3.HISTORICAL PERIOD: EXAMPLES

13 3.HISTORICAL PERIOD: EXAMPLES Seasonal averages, trend included Jan 1985 Dec 2010 Jan 2010 Feb 2012 Monthly Anomalies Apr 2011

14 3.HISTORICAL PERIOD: VALIDATION

15 3.HISTORICAL PERIOD: VALIDATION MAPE Daily Load UK Switzerland Sweden Spain Slovenia Slovakia Serbia Romania Portugal Poland Norway Netherlands Montenegro Macedonia Luxemburg Lithuania Latvia Italy Ireland Hungary Greece Germany Finland Estonia Denmark Czech Republic Croatia Bulgaria Bosnia-Herzegovinia Belgium Austria 0,00% 2,00% 4,00% 6,00% 8,00% 10,00% 12,00% MAPE_Validation MAPE_Training

16 4.PROJECTIONS: FRANCE Historical with trend Historical detrended Projection no trend Projection scenario 1

17 4.PROJECTIONS: ANOMALIES France Spain UK Germany

18 4.PROJECTIONS: SPAIN SEASONAL CONTRAST Summer Winter

19 4.PROJECTIONS: ANOMALY FULL PLOTS

20 4.PROJECTIONS: ANOMALY FULL PLOTS

21 Summary The models built and validated on the historical period have been used for projections Power & Energy levels are dependant on assumptions on ehighway2050 scenarios (linear interpolation from 3 values) But Detrended Demand and Demand Anomalies are independant of these assumptions Limitations: Our dataset doesn t take into account technology changes impacts on demand structure (energy efficiency...) But it shows how simulated climate change may impact energy demand Main signals: - decrease in winter energy consumption due to increase in temperatures - increase in summer energy consumption due to increase in temperatures for countries where demand depends significantly on temperature

22 And now? èremind this is a Proof of Concept! q Read carefuly the documentation to make sure you re fully aware of the limitations q You can use the demonstrator and download the data q Please give us feedback and report any issue you may find q C3S Energy products should come to an operational phase in the next 2 years, based on what the two Energy PoCs have produced (including new energy scenarios)

23 Thank You

24 2.DEMAND MODELLING HISTORICAL PERIOD GAM model - examples modeltrend = f (s(t2m) + s(ssrd) + s(ws) + s(rh) + s(year,by=as.factor(month)) + s(moh) ) modeldaily = f( s(t2m) + s(ssrd,by=(t2mcold)) + s(ssrd,by=(t2mwarm)) + s(ws,by=(djf))+ s(rh,by=(t2mcold)) + s(posan, bs = 'cc', k = 30,by=as.factor(WeekendWeek)) + as.factor(dayinweek):as.factor(ferie) )