Ameren Missouri Peak Load Forecast Energy Forecasting Meeting, Las Vegas. April 17-18, 2013

Transcription

1 Ameren Missouri Peak Load Forecast Energy Forecasting Meeting, Las Vegas April 17-18, 2013

2 Motivation for End Use Peak Forecasting Missouri IRP rules have extremely detailed load analysis and forecasting requirements 4 CSR (4) (A) Load profiles for each day type shall be developed for each end use, for each major class and for the net system load We use SAE to incorporate end use information into our energy sales forecast, why not reflect those impacts on the peak? Example: EISA standard increases the efficiency of lighting and therefore reduces the growth rate of lighting energy Ameren Missouri is a summer peaking utility with a peak hour typically from 4 to 5 pm on a summer weekday Residential lighting is near its minimum utilization during this time Can our forecast reflect the fact that as lighting efficiencies are realized, the peak is not reduced as fast as energy? Most existing methodologies would not capture this effect 2

3 Energy Forecast Monthly forecast of energy sales by customer class and end use (where applicable) Methodology Residential & Commercial Statistically Adjusted End-Use (SAE) Industrial Econometric 3

4 Disaggregation of Forecast into End Uses The SAE forecast can be relatively easily disaggregated into an end use forecast Multiply model coefficients by forecasted xheat, xcool, xother variables Multiply xother forecast by contribution of each end use to the total xother Index 4

5 Residential Forecast by End Use 5

6 Peak Forecast: Build-Up Model Input monthly sales data and hourly profiles to obtain hourly forecasts. Monthly/Annual Energy Forecast Class or End Use Profiles Class Specific Loss Rates Long-Run Load Shape Forecasting System Hourly Forecast Database 6

7 Peak / Hourly Modeling System Hourly loads are modeled by profiling forecasted energy with class or end-use level load shapes and adjusting for losses Peak forecast is be based on maximum hour in the month from above calculation adjusted based calibration factors determined from backcasts 7

8 Build-Up System Load 8

9 End-Use Load Shapes Ameren Missouri acquired Itron s EShapes database These profiles are the best available shapes we are aware of for end-uses but Based on end use load research that is likely over a decade old From different geographic regions It is logical to assume that utilization patterns of end-uses have changed due to Evolving technologies Increasing efficiency New miscellaneous end-uses Changing lifestyles over time Regional differences in life style / weather 9

10 End-Use Load Shape Calibration Ameren Missouri has undertaken an effort to calibrate the EShapes profiles to observed usage patterns from load research Utilizing 2008 data: Scaled EShapes to the end-use energy determined by SAE disaggregation of historical monthly loads Aggregated end-use hourly data into customer class shape Compared hourly bottom-up end-use shapes to 2008 actual load research Analyzed differences between modeled and actual load shapes by Hour of the day Season Day of week 10

11 End-Use Load Shape Calibration End-Use Load Shapes were adjusted to reduce differences between bottom-up end-use class load and load research The goals of the adjustment process were to Respect the integrity of the original shape Adjust the load factor by changing the peak relative to the daily energy Peak was never changed more than 20% Minimize the sum of squared error of the hourly difference series Apply judgment to each adjustment to ensure that it makes sense for the given end use 11

12 Comparison of End-Use Build-Up Residential Load to Load Research 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, RES_TOTAL LR Avg Hourly Difference - End-Use Build-Up vs Load Research 0.00 (50.00) (100.00) (150.00) (200.00) LR-End Use Orig LR-End Use Adj 12

13 Calibrated Residential End-Use Shape Examples Cooling Load Shape Lighting Load Shape Original Adjusted Original Adjusted Heating Load Shape Miscellaneous Load Shape Original Adjusted Original Adjusted

14 Backcast Calibration of Peak Loads Backcast annual peak loads with actual energy (modeled split up of end uses) for the period of time for which system load data is available Average error by year will be used to calibrate forward looking annual peaks This accounts for the fact that profiles forecast the expected value of load at the expected peak temperature, which is different than the expected value of peak load 14

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16 Backcasting/Calibration of Peaks Date LTSystemPeak (MW) Actual Peak (MW) Difference ,648 7, % ,802 7, % ,503 8, % ,400 8, % ,623 8, % ,463 8, % AVG 0.52% MW Calibration factor 9,700 8,700 7,700 6,700 5,700 4,700 3,700 2,700 1, Year LTSystemPeak (MW) Actual Peak (MW) 16

17 Impact of End-Use Shapes How would the system peak forecast be different with class level instead of end-use profile modeling? Analyzed changes in 2016 forecast from 2010 base resulting from detailed modeling Residential Contribution to Annual System Peak Load* Year End-Use Load Shape Class Level Profile Difference ,015 4,015 N/A ,961 3, ,017 4, ,057 4, ,093 4, ,131 4, ,163 4, Impact on Peak Load Forecast of End-Use Modeling ClWash ColorTV Cooling Dishwash ElecCook ElecDHW ElecDryer ElecHeat ElecMisc Freezer Lighting Refrigerator Total Res

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