Application of a Multi-Scheme Ensemble Prediction System for Wind Power Forecasting in Ireland

WEPROG ApS, Denmark Weather and Wind Energy Prognosis Corinna Möhrlen, Jess Jørgensen, University College Cork, Ireland Sustainable Energy Research Group, Department of Civil and Environmental Engineering Steven Lang, Brian Ó Gallachóir, E. McKeogh,

INTRODUCTION & RATIONALE ENSEMBLE PREDICTION SYSTEMS (EPS) WIND POWER PREDICTION & UNCERTAINTY RESULTS & VALIDATION CONCLUSIONS

INTRODUCTION Reliable wind power forecasting is vitally important to: * Enable high wind penetration * Decrease costs of balancing power * Maximise CO 2 benefit of wind generation * Ensure power system security and stability, particularly on weakly interconnected grids

IMPORTANCE OF FORECASTING ON IRISH GRID * Total installed generation on network is ~ 6300MW * Maximum demand 4800MW & minimum demand 2000MW * Installed wind generation was 500MW at end of 2005, and an additional 780MW with connection agreements * Further 2700MW applications to connect to grid * Weak interconnection of Republic of Ireland grid with Northern Ireland (NI) grid, and only weak interconnection of NI with Scotland and the rest of UK.

‘TRADITIONAL’ WIND POWER FORECASTING * Persistence * Physical models * Statistical models * Hybrid models of the above Most rely on input weather forecast data from national meteorological services… These deterministic forecasts of wind speed and direction are not usually designed for wind power prediction, and introduce the greatest errors to predicted wind power

ENSEMBLE PREDICTION SYSTEMS (EPS) A group, or ‘ensemble’, of weather forecasts produced in order to quantify the uncertainty of the forecast. Different approaches: * Ensemble Kalman Filter * Singular vector * Breeding vector * Multi-model EPS * Multi-scheme EPS

MULTI-SCHEME ENSEMBLE PREDICTION SYSTEM (MS-EPS) * 75-member, limited area EPS * 75 different Numerical Weather Prediction (NWP) model parameterisations, or ‘schemes’ * Each member’s scheme differs in formulation of fast meteorological processes * Multi-scheme method reduces ensemble bias and quantifies forecast uncertainty

BACKGROUND TO DEVELOPMENT OF MS-EPS * Research at UCC since 2000 * Operational system launched by WEPROG at Energinet.dk (then Eltra), 2003 * Testing in research projects, e.g. Honeymoon, * Currently forecasting ~ 20GW wind power * Operating real-time, world-wide by WEPROG * Ongoing research and development by UCC and WEPROG

WEATHER PREDICTION WITH MS-EPS 12 hour Forecast 10m wind speed, UK and Ireland, 23/1/06

WIND POWER PREDICTION MODULE Converts weather forecast to wind power: 1 – Calibration Step * ‘Training’ of each ensemble member using historical power production data * Direction dependent, time independent power curves produced for each ensemble member 2 - Forecast Step * Predict power using directional power curves

WIND POWER PREDICTION Energinet.dk - Operational System since hour Wind Power Forecast for Eltra area, Denmark, 12/1/06

IRISH RESULTS Validation against data from Golagh wind farm, Co. Donegal, northwest Ireland (complex terrain, high load factor) Photo courtesy B9 Energy

VALIDATION Error Descriptors: * MAE = mean absolute error * Bias * Standard deviation and RMSE All normalised to the installed capacity of the wind farm or the aggregate operational area

Golagh Wind Farm Verification 2/1/05 – 1/5/ Observed power data with 1 hr smoothing

IRISH RESULTS Golagh observed power data is dominated by large fluctuations with amplitude comparable to the EPS spread - similar effects have been observed at Horns Rev: ---- Observed power, raw 15 min data Horns Rev output (from Eltra System Plan 2004)

IRISH RESULTS – Daily Forecasts for Golagh Example 00UTC 48hr forecasts, 2/1/05 – 13/1/05

MS-EPS IS ABLE TO QUANTIFY UNCERTAINTY ---- Observed power data with 1 hr smoothing

QUANTIFICATION OF UNCERTAINTY IS AN IMPORTANT FEATURE OF THE MS-EPS * Physically realistic uncertainty estimate * Grid operators have difficulty dealing with forecasting system which uses single, deterministic weather forecasts from national met services as input to forecasting tool – forecasts can be sometimes ‘way out’ * Minimise balancing generation and associated costs * System security is enhanced with better forecasts and information on uncertainty – assists in operating the system during atypical weather events

IRISH RESULTS Variation of forecast quality at Golagh Wind Farm 2005BiasnMAESDR2R2 V avg (m/sec) Jan-2.3%13.8%19.4% Feb-2.6%10.8%14.4% Mar-1.2%9.8%13.7% Apr0.6%11.0%14.3% AVG-1.4%11.4%15.7% Error statistics generated from hr forecasts 30m agl model wind speed Normalised to wind farm capacity of 15MW

IRISH RESULTS Variation of forecast error with forecast length - Golagh Normalised mean absolute error out to 48 hour horizon SOLID __ Statistical best guess Dashed --- Mean Dotted… Best member

COMPARISON WITH DANISH & GERMAN RESULTS * To study any differences between forecasting for single sites and aggregate areas of wind power production * To investigate the effect of geographical dispersion of turbines on forecasting error

RESULTS – Germany / Denmark / Ireland Area/SiteGermanyDenmark West Golagh (Ireland) Horns Rev (Denmark) Scaled to Represent: 17GW2.5GW15MW160MW Average Load Factor 24%28%35 – 55% nMAE4.4%8.2%12.5%14.5% Standard Deviation 6%12%18%21%

DISTRIBUTION OF ERRORS Frequency distribution of errors for single sites and Danish and German aggregate areas

CONCLUSIONS * Golagh and Horns Rev have significant power output fluctuations and higher forecast errors than aggregate wind power production areas * Forecast errors appear to increase with increasing load factor, due to increasing atypical weather events and the greater number of hours at turbine cut-off

CONCLUSIONS * Study suggests the prediction error in Ireland will be considerably lower with geographical dispersion of wind farms * Forecasting for individual farms is more difficult and less accurate than aggregated wind power forecasts

CONCLUSIONS * The Multi-Scheme Ensemble Prediction System offers the possibility to estimate the uncertainty of the forecasts * This provides operators more security when handling wind power and hence enables higher wind penetration

ACKNOWLEDGEMENTS Sustainable Energy Ireland: Study funds under RE/W/03/006 ESB National Grid: Data provision and support