integrated resource plan presentation · 11.04.2012 · 3 introduction (cont.) • the energy...
TRANSCRIPT
1
Integrated Resource Plan Presentation
Las Cruces, NM
April 11, 2012
2
Introduction
• The energy & load forecast is used to project energy sales and peak load for 20 years.
• Peak load forecast is used to determine how much Generation and Transmission capacity will be needed over time.
• The system expansion profile is used to plan for the capital expenditures required to meet the future system load
3
Introduction (cont.)
• The energy forecast is used to determine the expected level of energy sales and revenue, usually for 2-3 years.
• This information is used by Finance to balance cash flow and financing needs, as well as proving guidance to outside investors and analysts.
4
Load & Energy Forecasts Serve Competing Purposes
• Electric utilities need to have adequate capacity available to meet peak conditions at any point in time, thus capacity planners want an aggressive load forecast.
• Electric utilities need short-term forecast of energy sales and revenues. This is used to provide guidance to senior management, outside investors, analysts and government agencies. Thus, financial planners want a conservative energy forecast.
5
Model Enhancements
• Monthly sales model developed to replace annual forecasting model.
• This gives EPE the ability to model the effects of weather on energy sales, adjust the sales forecast for average weather patterns, and explain the impact of abnormal hot or cool weather on system energy sales.
6
Model Enhancements (cont.)
• Customer growth is explicitly tied to energy sales.
• The effect of increasing refrigerated air conditioning saturation rate on residential use per customer is modeled.
• Interactive weather terms are used to pool monthly data into one equation, increasing the degrees of freedom 12X.
7
Energy Model
8
Texas Residential Model
Texas Residential Use per Customer Equation
UPC_TXres = +rpi *EP_Real_Pers_Inc+acsat +* AC_Sat_Dum + m01*EP_M01HDD_2ma + m02*EP_M02HDD_2ma + m06*EP_M06CDD_2ma + m07*EP_M07CDD_2ma + m08*EP_M08CDD_2ma + m09*EP_M09CDD_2ma + m10*EP_M10CDD_2ma + m12*EP_M12HDD_2ma + AR(1)
Texas Residential Customer Equation
CUS_TXres = + HH *EP_Households + AR(1)
Texas Residential kWh Forecast
KWH_TXres = UPC_TXres * CUS_TXres
9
New Mexico Residential Model
New Mexico Residential Use Per Customer Equation
UPC_NMres = +rpi *LC_Real_Pers_Inc+ m01*LC_M01HDD_2ma + m02*LC_M02HDD_2ma + m06*LC_M06CDD_2ma + m07*LC_M07CDD_2ma + m08*LC_M08CDD_2ma + m09*LC_M09CDD_2ma + m10*LC_M10CDD_2ma + m12*LC_M12HDD_2ma + AR(1)
New Mexico Residential Customer Equation
CUS_NMres = + HH *LC_Households + AR(1)
New Mexico Residential kWh Forecast
KWH_NMres = UPC_NMres * CUS_NMres
10
Texas Commercial Model
Texas Commercial Use per Customer Equation
UPC_TXcom = +rpi *EP_Nonfarm_Empl + m02*EP_M02HDD_2ma + m06*EP_M06CDD_2ma + m07*EP_M07CDD_2ma + m08*EP_M08CDD_2ma + m09*EP_M09CDD_2ma + m10*EP_M10CDD_2ma + Trend90+ AR(1)
Texas Commercial Customer Equation
CUS_TXcom = + HH *EP_Households + AR(1)
Texas Commercial kWh Forecast
KWH_TXcom = UPC_TXres * CUS_TXres
11
New Mexico Commercial Model
New Mexico Commercial Use Per Customer Equation
UPC_NMcom = +rpi *LC_Real_Pers_Inc+ m01*LC_M01HDD_2mam01 + m02*LC_M02HDD_2ma + m04*LC_M04HDD_2ma + m05*LC_M05CDD_2ma + m06*LC_M06CDD_2ma + m07*LC_M07CDD_2ma + m08*LC_M08CDD_2ma + m09*LC_M09CDD_2ma + m10*LC_M10CDD_2ma + m12*LC_M12HDD_2ma + AR(1)
New Mexico Commercial Customer Equation
CUS_NMcom = + HH *LC_Households + AR(1)
New Mexico Commercial kWh Forecast
KWH_NMcom = UPC_NMcom * CUS_NMrom
12
Calibrating the UPC Forecasts
• The forecasted level of energy sales from the model equations were either high or low compared to 2011 due to forecast error.
• Actual Energy Sales2011 = Predicted Sales2011 + Error2011
• The 2011 error was then deducted from the forecast going forward to calibrate the forecast for the last known year.
• Predicted Energy Sales2011 = Actual Sales2011
13
Texas Other Public Authority Model
Texas Public Authority Energy
KWH_TXOPA = +GMP *EP_GMP + m05*EP_M05CDD + m06*EP_M06CDD + m07*EP_M07CDD + m08*EP_M08CDD + m09*EP_M09CDD + m10*EP_M10CDD + Bliss_Growth_Dum +AR(1)
Texas Commercial Customer Equation
CUS_TXOPA = + nfe *EP_Nonfarm_Empl + AR(1)
14
NM Other Public Authority Model
New Mexico Public Authority Energy
KWH_NMOPA = +GMP*LC_GMP + m05*LC_M05CDD + m06*LC_M06CDD + m07*LC_M07CDD + m08*LC_M08CDD + m09*LC_M09CDD + m10*LC_M10CDD
NM Public Authority Customer Equation
CUS_NMOPA = + nfe*LC_Nonfarm_Empl + AR(1)
15
Industrial Model
• Industrial Customers are assumed to remain constant.
• Industrial load is not weather sensitive.
• Monthly industrial loads vary significantly month to month making it difficult to use statistical methods.
• Decided to estimate industrial energy sales on an annual basis.
• Energy sales are estimated directly, rather than using use per customer equations.
16
Industrial Model
Texas Industrial KWH Equation (annual)
KWH_TXind = + mfg * EP_Mfg_Empl + Dum98 + AR(1)
Texas Industrial Customer Equation (Constant)
CUST_TXind = 41
New Mexico Industrial KWH Equation (annual)
KWH_NMind = + mfg * LC_Mfg_Empl(-1) + AR(1)
New Mexico Industrial Customer Equation (Constant)
CUS_NMind = 7
17
Street & Highway Lighting Model
Texas Street & Highway KWH Equation
KWH_TXs&h = + pop* EP_population + effic* effic_dum+AR(1)
Texas Street & Highway Customer Equation (Constant)
CUST_TXs&h = 212
New Mexico Street & Highway KWH Equation
KWH_NMs&h = + pop* LC_population + AR(1)
NM Street & Highway Customer Equation (Constant)
CUS_NMs&h = 18
18
Weather
• El Paso weather has been warming over time.
• The summer weather in El Paso has been extremely hot, in terms of CDD, in 2010 & 2011
• Since weather changes, sometimes dramatically from year to year, it is necessary to use the average weather for several years to smooth out the annual variability and use the average weather in the forecasting equation.
• For the purpose of generating the energy forecast, ten-year average weather for El Paso and Las Cruces is used in the forecast.
19
1,500
1,700
1,900
2,100
2,300
2,500
2,700
2,900
3,100
3,300
De
gre
e D
ay
s P
er
Ye
ar
El Paso Annual CDD & HDD
CDD
HDD
Linear (CDD)
Linear (HDD)
20
Out of Model Adjustments
• Military Activity
• Fort Bliss
• White Sands
• Conservation & Load Management
• Losses
• Rio Grande Electric Cooperative
21
Fort Bliss Assumptions
• Reduced incremental load forecast (-42 MW from 2015 forward vs. 2011 Forecast)
• Troop strength at build out levels
• Delays in construction projects
• Uncertain impact of “Net Zero” Program (could lower future energy sales to the base).
22
Cumulative Difference in Out-of-Model Adjustment (2012-2011)
0
5
10
15
20
25
30
35
40
45
50
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
MW
23
Energy & Customer Forecast Summary
YearNative System Energy
(GWh) Percent Growth Customers
2011 8,362 378,547
2012 8,303 -0.71% 385,406
2013 8,507 2.46% 392,539
2014 8,706 2.34% 399,679
2015 8,888 2.09% 406,837
2016 9,065 1.99% 413,968
2017 9,211 1.61% 420,697
2018 9,359 1.61% 427,081
2019 9,507 1.58% 433,385
2020 9,679 1.81% 439,738
2021 9,819 1.45% 446,000
24
25
Historical Accuracy of Energy Forecast from Annual Models
• The forecasts generated from the historical annual models tended to over forecast energy.
• The exception to this rule occurred in 2010 & 2011 when the three most recent annual forecasts under projected the actual energy due to the extremely hot summers.
26
Demand Model
27
Demand Model
• Constant System Load Factor Method
• LF = Energy / (Demand x Hours)
• Demand = Energy / (LF x Hours)
• Demand is estimated based on the Constant System Load Factor and the Native System Energy Forecast
28
System Load Factor
• System load factor has been declining since 2000, resulting in a tendency for annual forecasts to under predict future peak demand levels.
• Historically, the annual forecasts used a 5 year average system load factor to project demand, given the year to year fluctuation in its level.
• Subsequently the forecast moved to a 3 year average then to a 1 year average to compensate for under prediction of peak demand due to the declining system load factor.
29
Factors in System Load Factor Decline
• Increasing share of residential sales
• Increasing saturation rate for refrigerated air conditioning
• Loss of manufacturing employment
30
0.500
0.520
0.540
0.560
0.580
0.600
0.620
0.640
0.660
0.680
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Sy
ste
m L
oa
d F
act
or
EPE System Load Factor
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13.50%
10.20%
13.70%
7.90%
54.90%
35.50%
0%
10%
20%
30%
40%
50%
60%
1990 1995 2000 2005 2010 2015
Refrigerated Air Conditioning Saturation Rate
32
0
1
2
3
4
5
6
7
1:00
2:00
3:00
4:0
0
5:00
6:0
0
7:00
8:0
0
9:0
0
10:0
0
11:00
12:00
1:00
2:00
3:00
4:0
0
5:00
6:0
0
7:00
8:0
0
9:0
0
10:0
0
11:00
12:00
Av
g k
W/c
ust
om
er
EPE's Average Residential Cooling Coincident Demand
Evaporative AC Refrigerated AC
Native Peak08/23/201016:00 Hours
2.33 kW
5.54Average Sales for Peak Day:Refrigerated: 65.45 kWhEvaporative: 44.23 kWh
33
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
199
019
90
199
019
91
199
119
92
199
219
92
199
319
93
199
419
94
199
519
95
199
519
96
199
619
97
199
719
97
199
819
98
199
919
99
200
020
00
200
020
01
200
120
02
200
220
02
200
320
03
200
420
04
200
520
05
200
520
06
200
620
07
200
720
07
200
820
08
200
920
09
2010
2010
2010
2011
2011
Em
plo
ym
en
t
El Paso Manufacturing Employment
34
Historical Accuracy of Load Forecast from Annual Models
• The load forecasts from annual forecasting models used in previous EPE forecasts have tended to under forecast future peak loads.
• This is a consequence of using a constant system load factor assumption during a time of decreasing system load factor.
35
Decision to Use 2011 Adjusted Demand Forecast
• Changed to monthly energy forecast model methodology which is untested
• This lowered energy growth forecast to more reasonable levels.
• Lower 2012 energy forecast led to a 2012 demand forecast that is lower than the 2011 demand forecast.
• Historically, the load forecasts from the annual models have already tended to under forecast peak demand
• EPE is already planning large generation and Transmission projects based on the 2011 demand forecast
• Based on these considerations, the 2011 demand forecast was selected as the 2012 demand forecast, but is reduced to reflect the MW difference 2011 & 2012 out of model adjustments.
36
Demand Forecast Summary
• Same demand forecast as 2011 forecast, except demand was lowered to reflect cumulative difference in out of model demand adjustments between 2012 and 2011 forecasts
37
Demand Forecast Summary
YearNative System Peak Demand
(MW) Percent Growth
2011 1,711
2012 1,698 -0.76%
2013 1,746 2.83%
2014 1,797 2.92%
2015 1,849 2.89%
2016 1,913 3.46%
2017 1,967 2.82%
2018 2,019 2.64%
2019 2,063 2.18%
2020 2,111 2.33%
2021 2,172 2.89%
38
39
Future Monthly Model Refinements
• Develop methodology to predict system load factor base on factors such as: refrigerated air conditioning saturation, manufacturing employment, customer mix, energy efficiency, etc.
• Observe monthly model accuracy and correct problem areas.