Research and application of locational wind forecasting in the UK

Transcription

1 1 Research and application of locational wind forecasting in the UK Dr Jethro Browell EPSRC Research Fellow University of Strathclyde, Glasgow, UK

2 2 Acknowledgements Daniel R Drew, Kostas Philippopoulos and others University of Reading, UK Bri-Mathias Hodge, Tarek Elgindy NREL, USA

3 3 Contents Atmospheric Regimes What are they? Examples in wind and solar forecasting Clustering of Wind Power Capacity in GB GB overview Capacity Clustering and its Effects IEA Wind Task 36: Wind Power Forecasting

4 4 Atmospheric Regimes Large-scale meteorological phenomena: Persist for days, weeks and beyond Are associated with particular weather types or anomalies Provide seasonal predictability and information about short-term predictability

5 Christophe Cassou, Euro-Atlantic regimes and their teleconnections, ECMWF Seminar on Predictability in the European and Atlantic regions, 6 9 September Atmospheric Regimes Large-scale regimes fundamental to seasonal/sub-seasonal predictability E.g. El Nino, North Atlantic Oscillation (right) Information: Are we expecting a wet and mild or a cold and dry winter in Europe?

6 6 Atmospheric Regimes Applications in short- and very short-term renewable energy forecasting: 1. Spatial correlation useful in very short-term forecasts; correlation structure depends on regime 2. Structure in day-ahead forecast uncertainty; structure depends on regime

7 7 Atmospheric Regimes 1. Very short-term wind forecasting Forecasts based on recent observations at spatially dispersed locations

8 8 Atmospheric Regimes 1. Very short-term wind forecasting Recent advances focuses on structure and dynamics of this matrix: - Sparsity 1,2 (large scale applications) - Adaptive Updates 3 (slow dynamics) - Regimes 4 (switching, fast dynamics)

9 9 Atmospheric Regimes 1. Very short-term wind forecasting Figure Source: [4]

10 10 Atmospheric Regimes 1. Very short-term wind forecasting Consistent improvement vs competitive benchmarks at all 23 locations 4 Up to 4% improvement 1-hour ahead at some sites* Distinct forecast uncertainty between different regimes *wind speed RMSE, compared to best non-regimebased benchmark Figure Source: [4]

11 11 Atmospheric Regimes 2. Day-ahead probabilistic solar forecasting Uncertainty can vary a lot Can be captured in post-processing and ensemble NWP Uncertainty is often structured Forecast errors may persist for several hours or may be no connection between errors at adjacent time points

12 Clear Partial Cloudy Low Correlation High 12 Atmospheric Regimes 2. Day-ahead probabilistic solar forecasting Structure depends on weather type! E.g. Important when scheduling co-located storage: Persistent errors Large energy requirement Uncorrelated errors Many charge cycles t+0 t+24 t+48 Clear Partial Cloudy

13 13 Clustering of Capacity TSOs and markets benefit from spatial smoothing of forecast errors: Individual Wind Farm >10% MAE/day-ahead GB National Aggregate 3.75% MAE/day-ahead Offshore wind is increasingly clustered in meteorologically small areas disrupting this effect

14 14 Clustering of Capacity Great Britain Peak Demand: 50GW Installed Capacity Nuclear: 9GW Solar: 12GW Coal: 11GW Gas: 27GW Pump Hydro: 3GW Wind: 19GW Other: 13GW Total: 96GW Source: Approximate Numbers from National Grid and ENTSO-E for 2017, figure from National Grid ETYS17,

15 15 Clustering of Capacity Status Capacity [GW] Operational 19.5 Under Construction 4.4 Awaiting Construction* 17.2* Application Submitted* 3.9* *May still require subsidy contract to be viable. Source: Renewable Energy Planning Database (REPD): May 2018,

16 16 Clustering of Capacity What can happen big ramps! 5,6 E.g. Thames Estuary, November 2014 (then 1.5GW, now over 2GW, more under construction ) These ramps were not forecast by the TSO but they could be seen in high-res ensembles it was later found Regimes conducive to ramps can and should be forecast and presented to operators: Post-frontal Convection (as in this case) Thunderstorms High-speed Shutdown Source: [5,6]

17 17 IEA Wind Task 36 Coordination of R&D Best practice guides Advance forecast use Partners from: Representing: Europe (11), China, USA TSOs, National Weather Services, Forecast Vendors, Generators, Traders and R&D/Academia

18 18 WP1: NWP Improvement IEA Wind Task 36 Best practice for model physics and assimilation Document field measurement campaigns available for validation WP2: Benchmarking and Forecast Evaluation Best practice guide on Forecast Trials to appear Collaboration with IEC SC8A WG2 (TR63043) WP3: End Use Review State-of-the-art 7,8 Use of probabilistic forecasts 7,8 : value and communication More documents and resources on the website

19 19 References 1. J. Dowell and P. Pinson, "Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression," in IEEE Transactions on Smart Grid, vol. 7, no. 2, pp , March doi: /TSG Cavalcante, L., Bessa, R. J., Reis, M., and Browell, J. (2017) LASSO vector autoregression structures for very short term wind power forecasting. Wind Energy, 20: doi: /we Jakob W. Messner, Pierre Pinson, Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting, International Journal of Forecasting, doi: /j.ijforecast Browell J, Drew DR, Philippopoulos K. Improved very short term spatio temporal wind forecasting using atmospheric regimes. Wind Energy. 2018; doi: /we Daniel R. Drew, Dirk J. Cannon, Janet F. Barlow, Phil J. Coker, Thomas H.A. Frame, The importance of forecasting regional wind power ramping: A case study for the UK, Renewable Energy, Volume 114, Part B, 2017, Pages doi: /j.renene Daniel R. Drew, Janet F. Barlow, Phil J. Coker, Identifying and characterising large ramps in power output of offshore wind farms, Renewable Energy, Volume 127, 2018, Pages doi: /j.renene Bessa, R.J.; Möhrlen, C.; Fundel, V.; Siefert, M.; Browell, J.; Haglund El Gaidi, S.; Hodge, B.-M.; Cali, U.; Kariniotakis, G. Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry. Energies 2017, 10, doi: /en J. Dobschinski, R. Bessak, P. Du, K. Geisler, S.E. Haupt, M. Lange, C. Möhrlen, D. Nakafuji and M. de la Torre Rodriguez, Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors, IEEE Power and Energy Magazine, vol. 15, no. 6, pp , Nov.-Dec. 2017, doi: /MPE

20 Thanks! Slides, papers and more available at 20