FORECASTING: A REVIEW OF STATUS AND CHALLENGES. Eric Grimit and Kristin Larson 3TIER, Inc. Pacific Northwest Weather Workshop March 5-6, 2010

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1 SHORT-TERM TERM WIND POWER FORECASTING: A REVIEW OF STATUS AND CHALLENGES Eric Grimit and Kristin Larson 3TIER, Inc. Pacific Northwest Weather Workshop March 5-6, 2010

2 Integrating Renewable Energy» Variable generation driven by the weather adds many challenges for meeting energy demand. Managing the reserve electric capacity and transmission systems becomes more difficult. Example: Wind power in the Bonneville Power Administration (BPA) energy balancing authority area (mostly in the Columbia Basin) Wind power ramp down opposite of load ramp up

3 Short-Term Wind Power Forecasting» State of the practice: Hours to Days Forecast Horizons» Numerical weather prediction with locally trained statistical post-processing (e.g., MOS) Minutes to Hours Forecast Horizons NW P On- Site Data Off- Site Data» Autoregressive statistical models and supervised machine learning techniques» Blending with short-term NWP model output» Adaptive predictor selection for large input data sets, including off-site meteorological observations» Regime-switching models Trained to minimize bulk errors for average power over all forecast intervals (e.g. RMSE over 1-hour periods for 1 yr).

4 Large Set Predictor Selection» Input Data Sources: On-site private wind project, met tower, and turbine data Nearby public met tower data Nearby private wind project and met tower data ASOS and meso-network (MADIS) stations within 300km o NWP model column output» Variables: Meteorological variables: o Wind speed Wind direction Temperature Pressure Large # of NWP model output fields Derived variables: Pressure differences Lags up to 24 hours o Time derivatives» Total Predictor Set: O(100,000) predictors, most useless and/or correlated Requires massive dimension reduction or memory-efficient sparse matrix methods: Dimension reduction: pick top few lagged predictors first for each measurement instrument separately o Memory efficient joint selection: use a LARS, Elastic Net approach

5 Regime Switching Models» Models applied to wind power forecasting Wind direction regime switching space-time model» Gneiting, Larson, et al. (2006) Trigonometric Direction Diurnal Model» Hering and Genton (2009) Markov switching auto- regressive e model» Pinson and Madsen (2009) Evolution of regime marginal probabilities using a Markov Switching Auto- Regressive (MSAR) statistical model for 10-minute, 1-step ahead prediction of wind power at Horns Rev (Denmark). (Pinson and Madsen, International Journal of Forecasting, 2009)

6 Standard Wind Power Forecast Skill» Location Wind Project A (Columbia Gorge, WA)» Time Period and Forecast Details Training Period: 03-May Aug-2008 Test Period: 18-Aug Aug-2009 Forecast Interval: 60 minutes (at HH:00) Forecast Leads: 120, 110,, 70 minutes Wind Project A Exp. # (input data) Train % Imp. RMSE 1(on-site) (on-site, off-site) (on-site, off-site, ASOS) Test % Imp. RMSE RMSE skill score (% Imp.) measured relative to 1-hr persistence forecast

7 Wind Power Ramp Forecasting» Usual goal is to minimize large deviations forecasts are optimized to be conservative» Yields smooth forecasts under-prediction of ramps

8 An Alternative Metric Event-Based Scoring» With categorical (yes/no) forecasts of a binary event, there are four possible outcomes.» Costs can be associated with each outcome. Ramp Forecast Yes No Ramp Observed Yes No Hit False Alarm Miss Correct Negative

9 Example Wind Power Ramp Forecast Skill» Location Wind Project B (Columbia Gorge, WA)» Time Period and Forecast Details Test Period: (6 months) Forecast Interval: 60 minutes (at HH:00) Forecast Lead: 1 day» Location Wind Project C (Columbia Gorge, WA)» Time Period and Forecast Details Test Period: 2007 Forecast Interval: 60 minutes (at HH:00) Forecast Lead: 1 hour Event Ramp Size: > 20% of capacity FCST YES NO OBS YES NO CSI (threat score) = Hits / ( Hits + Misses + Falses) = 0.11

10 Challenges for Ramp Event Prediction and Validation» Timing Example: Day-ahead forecasts of wintertime frontal passage on the west coast can be off by > 6 hr» Magnitude Example: Small errors in offshoreonshore pressure gradients can result in poor land/sea breeze intensity it» Location Example: Thunderstorm locations are difficult to forecast due to poor simulation of their initiation, development and decay processes» Frequency A ramp event is a rare event At the project level, events of 20% or more occur less than 10% of the time State-of-the-art forecasts have more false alarms and missed events than hits

11 Wind Power Ramp Event Capture Alberta Electric System Operator Wind Power Forecast Pilot Study ( ) Forecaster CSI (threat scores) Alberta Region Forecaster (Source: ORTECH Power, Wind Power Forecasting Pilot Project Part B: The Quantitative Analysis Final Report, 2008)

12 A Non-Deterministic Approach Goal: Quantify all the Uncertainties Example Ramp Event Probability Forecast and Observation Time Series DAY AHEAD RAMP EVENT PREDICTION 2-month hourly time series (Jan-Feb 2007)» System developed for BPA research project in 2007» Utilizes NWP ensembles for day-ahead ramp event prediction» Includes statistical calibration (e.g., Bayesian model averaging)

13 Integrating the Ramp Forecast» Goals: User-defined thresholds trigger alerts Full probability distribution input into stochastic unit commitment and economic dispatch system System operation can automatically optimize use of reserve capacity and avoid transmission i bottlenecks A proactive SmartGrid! Risk of down ramp event above users thresholdh

14 Challenges (Future Work)» Improved Ensemble NWP in Short-Range (0-6 hrs) Assimilation of more on-line data» Weather Regime Detection and Transition Prediction Concepts from low-frequency atmospheric regime transition studies» Large-Set Predictor Selection Dimension reduction Mutual information criterion Alternative metrics (asymmetric, event-based scores, etc.)» Forecast Integration and Utilization Packaging for energy management systems Human factors

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