Long-term Modeling & Leveraging Your Load Research Data

Transcription

1 Long-term Modeling & Leveraging g Your Load Research Data Overview of challenges and tools used for hourly load forecasting across the AEP System April 2009 Mark Harris, Senior Economic Forecast Analyst American Electric Power Company (918) mharris@aep.com

2 Forecasting Challenges Hourly data through operating companies, 11 states, 15 retail jurisdictions, numerous FERC customers, and 3 power pools. Hourly demand by revenue class & end-use Ability to update the models quarterly Weather Load Shapes Diversity (coincident, non-coincident) Calibration 2009 EFG Long-term Modeling 2

3 AEP Service Territory PJM SPP ERCOT 2009 EFG Long-term Modeling 3

4 Forecast Pieces Company State(s) # of FERC s Appalachian Power VA, WV 6 Columbus Southern OH 3 Indiana Michigan IN, MI 8 Kentucky Power KY 2 Ohio Power OH 3 Kingsport TN 0 Wheeling WV 0 Public Service Co. OK 2 Southwestern Power Co. AR, LA, TX 9 Texas Central TX 3 Texas North TX 9 Revenue Class/End-Use Heat Cool Other Lighting g Total Residential x x x x Commercial x x x Industrial x Other Retail x 9 Retail segments 2009 EFG Long-term Modeling 4

5 Peak Forecast Process Overview Traditional (1 Retail + FERC) New (9 Retail + FERC) Retail FERC Retail FERC 2009 EFG Long-term Modeling 5

6 Forecast Process Steps Monthly History Forecast by revenue Hourly system data (SAS) x class Load research class level data (SAS) x Short-term Temperature data (SAS) x Sales Calendar data (ND) x x SAS, ARIMA Class level l & end-use hourly profiles (ND) x (18-24 mos.) Monthly sales forecast by class & end use x x Profiles adjusted to sales forecast (LT) x Blending New revenue class & end-use profiles (LT) x Long-term System level hourly load simulation (ND) x x Sales Class & end-use hourly load simulation (ND) x x SAE FERC hourly load simulation (ND) x x Data aggregation and reporting (SAS) x 2009 EFG Long-term Modeling 6

7 Load Research Data Load research sample data were collected across the companies from 2005 through Samples were for residential, commercial, industrial, and public authority (other retail). Loads reflect meter values, without losses. Results usually fall close to billed numbers. Otherwise, loads are scaled to annual billed/accrued. Pros Cons Geographically representative Incomplete for some companies General split of loads by class Meter accuracy is uncertain Quality control of samples Data is not reflective of recent economy 2009 EFG Long-term Modeling 7

8 Example of Forecast Process The next few slides will highlight the major steps used for Southwestern Electric Power Company (SWEPCO). A similar process was used for other companies. SWEPCO Issues Solutions 1) Inadequate load research data Use adjacent company 2) Inaccurate historical state load Simulate data 3) State load forecast requirements Calibrate data 4) Several FERC customers Treat like another end use layer 2009 EFG Long-term Modeling 8

9 Concepts Calibration height of curve Raw Hourly Modeling (ND) Scaling - area under the curve Monthly / Annual Scaling (LT) Refined Hourly Modeling (ND) Scaling / Calibration (LT) 2009 EFG Long-term Modeling 9

10 Key Definitions Profile Hourly model that uses historical data to estimate end-use load shapes. SAE Estimates Energy levels based Statistically Adjusted End-Use models. (examples, Cooling, Heating, etc.) Modified Profile Profile model scaled to match annual SAE EFG Long-term Modeling 10

11 Modeling Process Overview AEP Load Research Profile Models (ND) Res ResCool ResHeat Lighting Com ComCool ComHeat Industrial Itron research Profiles scaled to SAE Estimates (LT) Modified Profile Model (ND) System Profile Model (ND) Scaling & Calibrations (LT) FERC Profile Models (ND) 2009 EFG Long-term Modeling 11

12 Profile Models (ND) AEP Load Research Historical hourly for residential (cooling, heat, & lighting), commercial (cooling, heat), and industrial were developed by Itron. End-use coefficients were taken from their industry library and combined with SWEPCO weather, creating hourly shapes EFG Long-term Modeling 12

13 Profiles Scaled to SAE Estimates (LT) Hourly shapes are scaled to equal the annual SAE estimates and exported to Excel. This modified profile is then modeled again in ND. SAE Estimates Profile 2009 EFG Long-term Modeling 13

14 Modified Profile Models (ND) Uses scaled data to create an hourly shape of end-use load for the entire horizon (history & forecast, ) EFG Long-term Modeling 14

15 System / FERC Profile Models (ND) This creates an hourly shape for the entire horizon (history & forecast, ) EFG Long-term Modeling 15

16 Combining the Data Profiling Models & Adjustments FERCs Arkansas (SWA) Louisiana (SWL) Texas (SWT) How do you combine to create total SWEPCO load, adjust to system sales, and calibrate to specified levels? How do you attain retail detail? METRIX LT 2009 EFG Long-term Modeling 16

17 Metrix LT Data Tables (annual) Source: external short-term sales models & SAE long-term SAE Estimates 2009 EFG Long-term Modeling 17

18 Metrix LT Interval Data (hourly shape) Source: Metrix ND profile models 2009 EFG Long-term Modeling 18

19 Metrix LT Batch Transform Tables Allocates annual energy to interval data (hourly shape) and adds losses 2009 EFG Long-term Modeling 19

20 Metrix LT Scaling Transform Tables Step 1 uses the relationship of actual and simulated historical load to calibrate the hourly system load and components. A circular derivation obtains desired calibration levels and forces the total system load to equal the sum of the parts EFG Long-term Modeling 20

21 Metrix LT Scaling Transform Tables Step 2 forces the calibrated system total to equal the sum of the parts. Step 3 is an optional FERC procedure EFG Long-term Modeling 21

22 Traditional Approach SWEPCO 2009 Peak Forecast=4,913 MW SWT_ferc4 SWT_ferc3 SWT_ferc5 SWA_Retail SWT_ferc2 13 Components SWT_ferc1 SWA_ferc1 SWA_ferc2 SWA_ferc3 SWT_Retail SWL_Retail SWL_ferc2 SWL_ferc EFG Long-term Modeling 22

23 New Metrix (ND & LT) Approach SWEPCO 2009 Peak Forecast=4,913 MW SWA_Res_Other SWA_Res_Lighting SWT_ferc5 SWA_Res_Cool SWA_Res_Heat SWT_ferc4 SWA_Com_Cool SWT_ferc3 SWA_Com_Heat SWA_Com_Other SWT_ferc2 SWA_Industrial 37 Components SWT_ferc1 SWT_Other SWT_Industrial SWA_Other SWA_ferc1 SWA_ferc2 SWA_ferc3 SWL_Res_Other SWL_Res_Lighting SWT_Com_Other SWL_Res_Cool SWT_Com_Heat SWT_Com_Cool SWT_Res_Heat SWT_Res_Cool SWT_Res_Lighting SWT_Res_Other SWL_Res_Heat SWL_Com_Cool SWL_Com_Heat SWL_Com_Other SWL_Industrial SWL_Other SWL_ferc2 SWL_ferc EFG Long-term Modeling 23

24 A New Perspective of Peak Demand by end use residential/commercial combination by revenue class FERC, 1276, Cooling, 1397, 26% 28% Residential, 1658, 34% Heating, 1, 0% Other Retail, 18, 0% Other, 3515, 72% Industrial, 734, 15% Commercial, 1227, 25% 2009 EFG Long-term Modeling 24

25 Organizing the Forecast Provides clarity and adds forecasting efficiency EFG Long-term Modeling 25

26 Thoughts & Conclusions Enhances forecasting transparency. Integration to potential company and government efficiency programs. Underscores the need for improved internal load research sample data. Helps establish a common industry dialogue to improve end-use modeling and load shape development. Due to the size of the output hourly tables, a special macro was written by Itron to export the data to comma delimited files. The *.csv files are compiled in SAS for database, reporting, and diversity purposes.putting putting all the pieces together EFG Long-term Modeling 26