The Center for Renewable Resource Integration at UC San Diego

Transcription

1 The Center for Renewable Resource Integration at UC San Diego Carlos F. M. Coimbra solarwind.ucsd.edu Jan Kleissl and Byron Washom UCSD Center of Excellence in Renewable Resources and Integration UC San Diego 1

2 The Problem of Solar Meteorology 2

3 3

4 Full Spectrum Forecasting Engines Time Horizons Intra-minute Intra-hour Intra-day Intra-week Applications Load Wind Solar GHI/DNI/GTI Types of Input Data No telemetry at all Met time series Forecast time series Ground images Satellite images Coverage Continental scale Regional Substation level Solar/wind farms Sensor level 4

5 The Merced Testbed: 8.5 acres, 1 MW 5

6 1 hour ahead PV power forecasts with no telemetry and no image processing: comparison of forecasting engine skills Performance of traditional forecasting methodologies: Persistent(Dt): PV(t+Dt) = PV(t) S_Persistent(w, CI): PV(t+Dt) = PV(t, w, CI) ARIMA k-nearest-neighbors (knn) No exogenous variables RMSE [kw] R 2 Method Tot P 1 P 2 P 3 Tot P 1 P 2 P 3 Persistent S_Persistent ARIMA knn P 1 : High variability P 2 : Intermediate variability P 3 : Low variability 6

7 1 hour ahead PV power forecasts with no telemetry and no image processing (no exogenous variables) More advanced forecasting techniques: Artificial Neural networks (ANN) ANNs optimized by Genetic Algorithms (GA/ANN) GA/ANN with Fractional Order preprocessing (ENIO ) RMSE [kw] R 2 Method Tot P 1 P 2 P 3 Tot P 1 P 2 P 3 Persistent S_Persistent ARIMA knn ANN GA/ANN ENIO TM ENIO algorithm protected under International Patent Application PCT/US2011/

8 Scatter plots for 1 hour-ahead forecasts (4 seasons) Persistent GA/ANN ARIMA ENIO ANN knn Persistent ARIMA knn ANN Legend: High variability period P1 Intermediate variability period P2 Low variability period P3 GA/ANN 8

9 2 hours ahead PV output forecasts (18 months of data) The ENIO forecast for 2 hours ahead is much better than the 1 hour ahead forecast with traditional methods. RMSE [kw] R 2 Method Tot P 1 P 2 P 3 Tot P 1 P 2 P 3 Persistent ENIO Persistent ENIO 9

10 Forecasting with Exogenous Variables Intra-hour forecasting of DNI using sky images or intra-day forecasts using satellite images, telemetry, NSW, NDFD, etc. Use high frequency images taken with total sky imagers Images are processed and converted into numerical datasets and fed to ENIO algorithm High-fidelity forecasts are produced by single pass through ENIO forecasting engine, which enhances accuracy of image translation Image capture 10

11 ENIO 15 minutes ahead for GHI 11

12 Remote Sensing-Assisted Forecasts 1 hour to 6 hours head 24 Nov :15:00 Davis, CA X: 204 Y: 65 Index: RGB: 0.588, 0.588, Nov :45:00 Merced, CA 12

13 GHI [kw/m 2 ] Remote Sensing-Assisted Forecasts 30 minutes ahead Measured GHI f1 GHI f time [30 minutes] 13

14 s = 1 U/V s = 1 U/V s = 1 U/V s = 1 U/V AI-Assisted Remote Sensing Forecasts minutes ahead Persistence Satellite only Satellite and GHI Combined Persistence Satellite only Satellite and GHI Combined N w Persistence Satellite only Satellite and GHI Combined N w N w Persistence Satellite only Satellite and GHI Combined N w 14

15 The State of the Art for Medium to Long Time Horizons Scheduling Global Horizontal Irradiance (GHI) Direct Normal Irradiance (DNI) Marquez and Coimbra (2011) Forecasting of Global and Direct Solar Irradiance Using Stochastic Learning Methods, Ground Experiments and the NWS Database Solar Energy, doi: /j.solener

16 The Merced Testbed for Intra-Minute Forecasts SunPower Single-Axis Tracking; 1 MW 16

17 Converting Spatial Resolution into Temporal Resolution for Intra-Minute Forecasts with 56 of Prof. Cerpa s Lab (UC Merced) Edge Sensors 17

18 Intra-Minute Forecasts Based on Coimbra s Lab (UCSD) Stochastic Learning and Cerpa s Lab (UCM) Edge Sensing 18

19 Points Worth Mentioning The Evolutionary Non-Integer Order (ENIO ) algorithm offers unprecedented robustness and forecasting skills using the most diverse sets of inputs (no exogenous variables, no telemetry, with or without image processing, remote sensing and/or meteorological inputs, etc.) One of the most difficult tests for a forecasting engine is the uni-variate forecast (no exogenous inputs), for which the ENIO method outperforms all other methods, including very sophisticated and much slower GA/ANN algorithms that scan the whole space of solutions for optimal parameters. ENIO s flexibility in handling different inputs, and its continuous learning capability make it a perfect forecasting/modeling engine to reduce translation errors from deterministic models in real-time. The ENIO algorithm has been productized by SolAspect for solar, wind and load forecasting, and is ready to interact with other models and inputs. 19

20 Time Tool: Space and Time Scales of Forecasts Sky Imager NWP Satellite 1 day Clouds there ft, +-3% Clouds exist +- 2 miles, +- 10% Clouds likely CAISO 1 hour 10 min EPRI 1 min FPL 10 sec 1 sec Distance Point Yards / meters km / miles 10 miles 1000 miles

21 Sempra Generation Copper Mountain 48 MW Thin Film PV Plant, Largest in the U.S. TSI deployment site PCS 33 1,800 m PCS 41

22 Sample Results Existing Technology NAM UCSD High Resolution UCSD- WRF WRF WRF

23 MAE / MAE (GFS) [-] NWP Forecast Results Intra-day Forecast Horizon [hours] Day -Ahead Pers. GFS WRF MBE (W m -2 ) MAE (W m -2 ) RMSE (W m -2 ) STDERR (W m -2 ) Assimilation of clouds produces more accurate irradiance forecasts Intra-day - MAE is as low as 50% as GFS - Consistently more accurate than the NAM for first 9 hours Day-ahead - WRF-CLDDA has MAE 50 W m -2 lower than GFS for day-ahead forecasts