BEFORE THE PUBLIC UTILITIES COMMISSION OF NEVADA
Application of Nevada Power Company d/b/a NV EnergySeeking Acceptance of the First Amendment to its 2013-2032 integrated resource plan and its Energy Supply Plan Update for 2015, which include an emissions reduction and capacity replacement plan filed pursuant to NRS § 704.7311 et seq.
Docket No. 14-05___
VOLUME 4 OF 15
EMISSIONS REDUCTION AND CAPACITY REPLACEMENT PLAN NARRATIVES
DESCRIPTION PAGE NUMBER
NARRATIVES
Load Forecast, Market Fundamentals, Fuel and Purchase Power
Price Forecasts and Demand Side Programs REDACTED 2 Supply Side Plan, Transmission Plan, Economic Analysis, And
NARRATIVE
LOAD FOR
ECAST, MARKET FUNDAMENTALS, FUEL
AND PURCHASE POWER PRICE FORECASTS & DEMAND
SIDE PROGRAMS
NEVADA POWER COMPANY d/b/a NV ENERGY 2014
EMISSIONS REDUCTION AND CAPACITY
REPLACEMENT PLAN
LOAD FORECAST, MARKET FUNDAMENTALS, FUEL
AND PURCHASE POWER PRICE FORECASTS &
DEMAND SIDE PROGRAMS
SECTION 1. LOAD FORECAST ...6
FORECAST SUMMARY ... 6
A. LOAD FORECAST METHODOLOGY ... 10
B. COMPARISON WITH THE 2014 ESP UPDATE FORECAST ... 11
C. FORECAST MODEL DEVELOPMENT ... 17
D. 1. Forecast Drivers ...17
2. Forecast Models...32
3. Customer Class Sales and Customer Forecasts ...40
4. System Energy, Sales and Peak Forecasts ...47
FORECAST SCENARIOS ... 49
E. DSM/DR USED IN THE FORECASTS VS. FINAL DSM-DR ... 50
F. EXTREME WEATHER TRANSMISSION PEAK FORECAST ... 51
G. SECTION 2. MARKET FUNDAMENTALS ...52
POWER FUNDAMENTALS ... 52
A. 1. WECC Capacity and Energy ...52
2. Future Generation Additions ...57
3. Renewable Energy in the Western States ...58
4. Challenges in Meeting RPS Targets ...59
5. Resource Adequacy ...60
6. Regional Power Price Trends ...61
7. Future Price Drivers ...64
NATURAL GAS FUNDAMENTALS ... 66
B. 1. Demand ...68
2. Supply ...73
3. Transport and Storage ...89
4. Price Trends ...95
COAL FUNDAMENTALS ... 97 C.
1. Supply ...97
2. U.S. Coal and Consumption...104
3. Coal Transportaion ...105
SECTION 3. FUEL AND PURCHASED POWER PRICE FORECASTS ...107
BASE GAS PRICE FORECAST ... 107
A. BASE MARKET IMPLIED HEAT RATE FORECAST ... 109
B. BASE POWER PRICE FORECAST ... 110
C. HIGH AND LOW GAS PRICE FORECASTS ... 111
D. HIGH AND LOW POWER PRICES ... 113
E. CAPACITY PRICE FORECAST FOR MARKET PURCHASES ... 114
F. COAL PRICE FORECAST ... 115
G. FORECASTS & MODELING OF POTENTIAL CARBON COSTS .... 116
H. SECTION 4. DEMAND SIDE PLAN ...123
ENERGY EFFICIENCY AND DEMAND RESPONSE ... 123 A.
Table of Figures
Figure LF-1: Historical (Weather Normalized) and Forecasted Sales (GWH) ... 7
Figure LF-2: Historical and Forecasted Customer Counts and Average Use Per Customer ... 8
Figure LF-3: Historical (Weather Normalized) and Forecasted Energy and Demand ... 9
Figure LF-4: Forecast Process ... 10
Figure LF-5: Nevada Power System Peak Forecasts ... 14
Figure LF-6: Nevada Power System Energy Forecasts ... 14
Figure LF-7: Population Growth Rate Forecast Comparison ... 15
Figure LF-8: Real Household Income Growth Rate Forecast Comparison ... 16
Figure LF-9: Real Gross Metro Product (“GMP”) Growth Rate Forecasts ... 16
Figure LF-10: Non-Manufacturing Employment Growth Rate Forecasts ... 17
Figure LF-11: Historical Class Sales Trends ... 18
Figure LF-12: Residential Customers (2003 to 2013) ... 19
Figure LF-13: Weather Normalized Residential Average Use (2003 to 2013) ... 20
Figure LF-14: Small C&I Customers (2003 to 2013) ... 21
Figure LF-15: Small C&I Average Use (2003 to 2013) ... 22
Figure LF-16: Large C&I Sales (2003 to 2013) ... 22
Figure LF-17: Average Annual Hotel/Motel Rooms (2003 to 2013) ... 23
Figure LF-18: Clark County Population (2003 to 2023) ... 24
Figure LF-19: Real per Household Income (2003 to 2023) ... 25
Figure LF-20: Employment (2003 to 2023)... 26
Figure LF-21: Real Output (2003 to 2023) ... 26
Figure LF-22: Prices (2003 to 2023) ... 27
Figure LF-23: Total Residential Energy Intensity (2003 to 2013) ... 28
Figure LF-24: Total Commercial Energy Intensity (2003 to 2023) ... 29
Figure LF-25: Annual Calendar CDD (Las Vegas, 2003 to 2023) ... 30
Figure LF-26: Annual Calendar HDD (Las Vegas, 2003 to 2023) ... 30
Figure LF-27: Annual DSM Savings Estimates (2005 to 2023)... 31
Figure LF-28: Residential Average Use Model Results ... 33
Figure LF-29: Residential Customer Forecast Model ... 34
Figure LF-30: Small C&I Average Use Model ... 35
Figure LF-31: Small C&I Customer Forecast Model ... 36
Figure LF-32: Large C&I Sales Forecast Model ... 37
Figure LF-33: Public Authority Sales History and Forecast (MWh) ... 38
Figure LF-37: Indexed Monthly Large C&I Sales, Real GMP, Hotel/Motel Rooms Employment and the Large C&I Economic Variable ... 42
Figure MF-1: NERC Regions ... 52
Figure MF-2: WECC Sub-Regions ... 53
Figure MF-3: WECC Capacity by Fuel Type (2013) ... 54
Figure MF-5: U.S. Natural Gas Consuption for Electric Generation ... 56
Figure MF-6: WECC Generation Under Construction (MW) ... 57
Figure MF-7: WECC Natural Gas-Fired Under Construction (MW) ... 58
Figure MF-8: Renewable Standards in Western States ... 58
Figure MF-9: 2013 WECC Power Supply Assessment (Summer) ... 60
Figure MF-10: Historic Power Prices at Mead ... 61
Figure MF-11: Historic Natural Gas Prices ... 62
Figure MF-12: Historical Monthly Market Implied Heat Rates at Mead ... 63
Figure MF-13: Historical Monthly Spark Spreads at Mead ... 64
Figure MF-14: Projection of Import Reliance of Major Economies ... 67
Figure MF-15: U.S. Dry Gas Consumption (trillion cubic feet per year) ... 69
Figure MF-16: U.S. Energy Demand by Fuel... 71
Figure MF-17: U.S. Energy Demand By Sector ... 71
Figure MF-18: Proved Natural Gas Reserves ... 74
Figure MF-19: North American Shale Gas Plays and Formations ... 75
Figure MF-20: World Shale Gas Resources ... 76
Figure MF-21: U.S. Natural Gas Supply ... 78
Figure MF-22: U.S. Natural Gas Production (trillion cubic feet per year) ... 79
Figure MF-23: U.S. and Canadian Gas and Oil Rig Count ... 80
Figure MF-24: U.S. and Canadian Dry Gas Production ... 82
Figure MF-25: Western Gas Production (2012-2015) ... 83
Figure MF-26: U.S. LNG Imports ... 84
Figure MF-27: Global Natural Gas Prices ... 85
Figure MF-28: U.S. LNG Exports ... 86
Figure MF-29: Proposed/Potential LNG Terminals ... 87
Figure MF-30: U.S. Pipeline Imports from Canada ... 88
Figure MF-31: Weekly Gas Storage for US (Lower 48) ... 92
Figure MF-32: Weekly Gas Storage for West Region ... 93
Figure MF-33: Western Underground Natural Gas Storage ... 94
Figure MF-34: Oil Prices vs Gas ... 95
Figure MF-35: Historical Natural Gas Prices ... 96
Figure MF-36: Operating Coal Mines in Central Utah ... 97
Figure MF-37: Operating Coal Mines in Western Colorado ... 99
Figure MF-38: Operating Coal Mines in the Powder River Basin ... 102
Figure PF-1: Annual Average Gas Price Forecast [REDACTED] ... 108
Figure PF-2: Average Market Implied Heat Rate Forecast [REDACTED] ... 110
Figure PF-3: Average Annual Power Price Forecast – Mead [REDACTED] ... 111
Figure PF-7: Nevada Power Projected Coal Prices [REDACTED] ... 116
Figure PF-8: Carbon Allowance Price for mid-Carbon Scenario ... 117
Figure PF-9: Natural Gas Price Adjustments for mid-Carbon Scenario (Henry Hub) ... 118
Figure PF-10: Examples of Modeling Power Price Impacts from Carbon ... 119
Figure PF-11: Example Carbon Costs to Power Prices ... 120
Figure PF-12: Purchased Power Price Increases due to Carbon (mid-Carbon Case) [REDACTED]... 121
Figure DSM-1: Nevada Power Approved 2015 Targets (dollars) ... 124
SECTION 1. LOAD FORECAST
Nevada Power has prepared a new load forecast for the 2014 Emissions Reduction and Capacity Replacement Plan (“2014 ERCR Forecast” or “2014 ERCR”). The 2014 ERCR Forecast updates the 2014 Energy Supply Plan Update (“2014 ESP Update Forecast” or “2014 ESP”), which was completed in January 2013 and filed with the Commission on September 1, 2013 as part of Docket No. 13-08009. The 2014 ERCR Forecast was completed in November 2013 and incorporates actual sales and load data through August 2013. Nevada Power seeks approval of the 2014 ERCR Forecast for purposes of performing long-term integrated resource planning.
FORECAST SUMMARY A.
In 2008, the downturn in the national and local economy began to significantly impact economic performance and electric sales in Nevada Power’s service territory. The economic downturn worsened in 2009 and continued to adversely impact Nevada Power’s sales through 2011. Fueled by an uptick in the tourism industry, sales turned positive in 2012 across all sectors with weather normalized sales up 1.7 percent compared to the previous year. However, in 2013 year-over-year sales fell again as the tourism industry remained sluggish and the Department of Energy’s Nevada Test Site (“Nevada Test Site”) exited Nevada Power’s system to take service from Valley Electric Association.1 In 2013 total sales fell 1.0 percent, with small and large commercial and industrial (“C&I”) weather normalized sales dropping 1.5 percent and 2.4 percent respectively from 2012.
Unlike sales, customer counts continued to modestly increase even during the economic downturn that started in 2008. Residential customer counts were up 1.5 percent in 2012 and 1.2 percent in 2013. Despite continued increases in customer counts, however, end-use efficiency, new appliance and commercial end-use standards, photovoltaic market penetration, and demand side management (“DSM”) program activity will continue to put downward pressure on long-term projections of customer usage (measured in long-terms of kWh/per customer). Residential average use is projected to decline 0.2 percent annually from 2013 through 2023 and commercial average use (small and medium size commercial customers) is projected to decline 0.1 percent annually over this period. In the near-term (2013 through 2016) residential use per customer is forecast to decline 0.5 percent on average as a result of the new lighting standards and continued DSM program activity. New residential lighting standards have had the largest impact on customer usage, as 100 watt and 75 watt incandescent light bulbs were scheduled for phase out in 2013, and 60 watt and 40 watt incandescent light bulbs are scheduled to be phased out in 2014. A full discussion of customer usage is contained in Technical Appendix LF-1.
Overall, Nevada Power expects to see positive sales growth over the next ten years as the economy continues to improve, and Nevada Power adds new customers. Sales are expected to grow at an average annual rate of 1.0 percent over the next ten years. Table LF-1 summarizes historical (weather normalized) and forecasted class sales. Table LF-2 presents residential historical and forecasted customer counts and weather normalized average usage. The forecast begins in 2014. The full forecast extends to 2034 for sales and customers. For clarity, we report only through 2023 in Sections A through D below. Full results are summarized in Technical Appendix LF-1.
The growth rates for the periods 2003 – 2013 and 2013 – 2023 are the average annual growth rate (“AAGR”) across those time periods. The beginning annual growth rate for 2003 – 2013 is the rate for 2003 to 2004, and the ending annual growth rate is 2012 to 2013. For 2013 – 2023, the beginning is 2013 to 2014 and the end is 2022 to 2023. All AAGRs in this document are calculated using this method. For example, an AAGR from 2003-2007 in a figure or table begins with the growth rate from 2003 to 2004 and ends with the growth rate for 2006 to 2007.
FIGURE LF-1: HISTORICAL (WEATHER NORMALIZED) AND FORECASTED SALES (GWH)
Year Res chg Small chg Large chg StLight chg Public chg Total chg
C&I C&I Authority
2003 7,456 3,743 6,292 153 258 17,901 2004 7,742 3.8% 3,923 4.8% 6,548 4.1% 158 3.2% 249 -3.2% 18,620 4.0% 2005 8,512 9.9% 4,243 8.2% 6,986 6.7% 167 5.6% 184 -26.3% 20,091 7.9% 2006 8,907 4.6% 4,410 3.9% 7,270 4.1% 170 2.2% 110 -40.0% 20,867 3.9% 2007 8,979 0.8% 4,573 3.7% 7,472 2.8% 171 0.5% 64 -42.1% 21,259 1.9% 2008 8,884 -1.1% 4,604 0.7% 7,645 2.3% 173 1.0% 58 -9.5% 21,364 0.5% 2009 8,718 -1.9% 4,447 -3.4% 7,596 -0.6% 185 6.7% 55 -4.3% 21,001 -1.7% 2010 8,639 -0.9% 4,358 -2.0% 7,653 0.7% 177 -4.2% 56 0.4% 20,883 -0.6% 2011 8,604 -0.4% 4,352 -0.1% 7,627 -0.3% 170 -4.2% 55 -0.9% 20,807 -0.4% 2012 8,833 2.7% 4,456 2.4% 7,645 0.2% 166 -1.9% 53 -4.4% 21,153 1.7% 2013 8,871 0.4% 4,391 -1.5% 7,458 -2.4% 155 -6.8% 56 6.2% 20,931 -1.0% 2014 8,969 1.1% 4,462 1.6% 7,563 1.4% 154 -0.7% 54 -3.3% 21,202 1.3% 2015 8,985 0.2% 4,477 0.3% 7,588 0.3% 152 -1.0% 54 0.0% 21,256 0.3% 2016 9,010 0.3% 4,525 1.1% 7,633 0.6% 151 -1.0% 54 0.0% 21,372 0.5% 2017 9,083 0.8% 4,595 1.6% 7,703 0.9% 149 -1.0% 54 0.0% 21,585 1.0% 2018 9,207 1.4% 4,678 1.8% 7,789 1.1% 149 0.0% 54 0.0% 21,878 1.4% 2019 9,335 1.4% 4,767 1.9% 7,875 1.1% 149 0.0% 54 0.0% 22,180 1.4% 2020 9,450 1.2% 4,861 2.0% 7,957 1.0% 149 0.0% 54 0.0% 22,471 1.3% 2021 9,565 1.2% 4,956 2.0% 8,015 0.7% 149 0.0% 54 0.0% 22,740 1.2% 2022 9,654 0.9% 5,049 1.9% 8,062 0.6% 149 0.0% 54 0.0% 22,968 1.0% 2023 9,756 1.1% 5,145 1.9% 8,122 0.8% 149 0.0% 54 0.0% 23,227 1.1% 2003 - 13 1.8% 1.6% 1.7% 0.1% -14.2% 1.6% 2013 - 23 1.0% 1.6% 0.9% -0.4% -0.3% 1.0%
The growth rate periods are interpreted as follows:
The historical period 2003 – 2013 includes annual growth rates beginning with the growth from 2003 to 2004 through 2012 to 2013. The forecast period 2013 – 2023 includes annual growth rates beginning with growth from 2013 to 2014 through 2022 to 2023. This interpretation is used throughout the load forecast section and Technical Appendix LF-1.
FIGURE LF-2: HISTORICAL AND FORECASTED CUSTOMER COUNTS AND AVERAGE USE PER CUSTOMER
Year Res chg Res Avg Use chg Small C&I chg Small C&I Avg Use chg Custs (kWh) Custs (kWh) 2003 606,187 12,299 78,794 47,498 2004 633,907 4.6% 12,213 -0.7% 83,149 5.5% 47,179 -0.7% 2005 667,742 5.3% 12,747 4.4% 87,819 5.6% 48,318 2.4% 2006 700,309 4.9% 12,718 -0.2% 92,367 5.2% 47,741 -1.2% 2007 720,116 2.8% 12,469 -2.0% 96,579 4.6% 47,345 -0.8% 2008 724,663 0.6% 12,259 -1.7% 99,089 2.6% 46,466 -1.9% 2009 725,557 0.1% 12,015 -2.0% 99,446 0.4% 44,716 -3.8% 2010 729,565 0.6% 11,842 -1.4% 100,270 0.8% 43,466 -2.8% 2011 737,500 1.1% 11,666 -1.5% 100,712 0.4% 43,213 -0.6% 2012 748,226 1.5% 11,805 1.2% 101,410 0.7% 43,937 1.7% 2013 757,052 1.2% 11,718 -0.7% 102,981 1.5% 42,635 -3.0% 2014 768,297 1.5% 11,674 -0.4% 104,166 1.2% 42,839 0.5% 2015 777,535 1.2% 11,556 -1.0% 105,522 1.3% 42,428 -1.0% 2016 786,458 1.1% 11,456 -0.9% 107,187 1.6% 42,213 -0.5% 2017 795,438 1.1% 11,419 -0.3% 109,138 1.8% 42,106 -0.3% 2018 804,507 1.1% 11,444 0.2% 111,269 2.0% 42,046 -0.1% 2019 813,309 1.1% 11,478 0.3% 113,514 2.0% 41,996 -0.1% 2020 821,678 1.0% 11,500 0.2% 115,772 2.0% 41,990 0.0% 2021 829,994 1.0% 11,525 0.2% 117,962 1.9% 42,015 0.1% 2022 837,917 1.0% 11,521 0.0% 120,076 1.8% 42,047 0.1% 2023 845,641 0.9% 11,537 0.1% 122,075 1.7% 42,142 0.2% 2003 - 13 2.2% -0.5% 2.7% -1.1% 2013 - 23 1.1% -0.2% 1.7% -0.1%
Customer class energy forecasts are combined with class hourly profiles and aggregated to generate the system hourly load and peak forecasts. The hourly load and peak forecasts are adjusted for net metering impacts associated with solar photovoltaic (“PV”) technology, a small amount of expected electric vehicle (“EV”) load, demand response (“DR”) program impacts, and line losses. Table LF-3 shows historical (weather normalized) and forecasted sales and demand (both uninterrupted and adjusted for DR). Energy efficiency, PV and DR impact the loads significantly in the period from 2014 through 2017 resulting in lower annual growth than after 2017.
FIGURE LF-3: HISTORICAL (WEATHER NORMALIZED) AND FORECASTED ENERGY AND DEMAND
Demand (MW)
Year Energy (GWh) chg After DR chg
2003 19,299 4,781 2004 3.5%19,968 4,944 3.4% 2005 3.9%20,752 5,226 5.7% 2006 7.7%22,354 5,673 8.6% 2007 2.2%22,843 5,650 -0.4% 2008 -0.1%22,822 5,640 -0.2% 2009 -3.3%22,061 5,490 -2.7% 2010 -1.6%21,718 5,495 0.1% 2011 -0.3%21,659 5,556 1.1% 2012 3.2%22,363 5,571 0.3% 2013 -1.1%22,123 5,630 1.1% 2014 -0.2%22,089 5,656 0.5% 2015 0.3%22,144 5,629 -0.5% 2016 0.8%22,313 5,654 0.4% 2017 0.8%22,492 5,713 1.0% 2018 1.4%22,803 5,795 1.4% 2019 1.4%23,129 5,883 1.5% 2020 1.5%23,477 5,963 1.4% 2021 1.0%23,714 6,043 1.3% 2022 1.0%23,954 6,108 1.1% 2023 1.1%24,227 6,180 1.2% 2003 - 13 1.4% 1.7% 2013 - 23 0.9% 0.9%
LOAD FORECAST METHODOLOGY B.
Consistent with prior forecasts, the 2014 ERCR Forecast was developed from a set of monthly econometric models estimated for each of the three primary revenue classes – residential (“RES”), small C&I, and large C&I. Street Lighting (“STLight”) and Public Authority forecasts are based on simple trend models.
The system hourly load and peak forecast is derived using a “build-up” approach. Customer class sales forecasts (i.e., RES, small C&I, and large C&I, STLight and Public Authority) are combined with customer class hourly profiles and aggregated to a system hourly load forecast. The system load profile is then adjusted for PV net metering impacts, EV load growth, and line losses. Resulting energy and peak forecasts are then adjusted to account for the effects of DR. Figure LF-4 depicts the forecast process.
The forecasting process entails first constructing the underlying forecast database and evaluating historical usage trends and economic and population projections. The forecast database includes historical billed sales, number of customers, population and economic data, prices, weather conditions and historical end-use saturation and efficiency trends. For the RES, small C&I, and large C&I revenue classes, the data is then used to estimate monthly regression models that incorporate structural changes (i.e., changes in end-use intensities) as well as economic conditions, price, and weather through a Statistically Adjusted End-Use (“SAE”) modeling framework. Average use models are estimated for RES and small C&I revenue classes, and total sales models are estimated for the large C&I class. For RES and small C&I, average use forecasts are combined with customer forecasts to generate a total sales forecast. Simple trend models are used to forecast sales for the other revenue classes including STLight and Public Authority.
The number of hotel/motel rooms is used as a driver variable in forecasting large C&I sales. The number of rooms is forecasted separately based on individual customer assessments from discussions with Nevada Power’s Major Account Executives (“MAEs”) and the Las Vegas Convention and Visitor’s Authority (“LVCVA”).
COMPARISON WITH THE 2014 ESP UPDATE FORECAST C.
This section describes the changes in the forecast between this ERCR Forecast and the Nevada Power 2014 ESP Update forecast filed in 2013. For this ERCR Forecast, inputs to Nevada Power’s 2014 ESP Update Forecast were updated as follows:
1. Global Insight Local Economy Inputs. The 2014 ESP Update Forecast used the January 2013 IHS/Global Insight (“Global Insight”) Las Vegas-Paradise Metropolitan Statistical Area (“MSA”) quarterly population, household, Real Personal Income, Employment and Real Gross Metro Product forecasts. The ERCR Forecast uses the September 2013 Global Insight MSA quarterly population, household, Real Personal Income, and Real Gross Metro Product forecasts. See Technical Appendix Item LF-1 for further discussion of these inputs.
2. Hotel/Motel Rooms. This 2014 ERCR Forecast uses a forecast of hotel/motel room additions based on a LVCVA construction bulletin updated in May 2013, news reports, and discussions with Nevada Power’s MAEs. The 2014 ESP Update Forecast used the forecast of hotel/motel room additions based on the September 2012 Resort and Attractions Construction bulletin. Two properties with more than 500 rooms were included in the 2014 ERCR Forecast: the Downtown Grand (former Lady Luck – 650 rooms), which opened November 1, 2013, and the SLS (former Sahara – 1,600 rooms) scheduled to open in the fall of 2014. The proposed Genting Group’s Resorts World Las Vegas hotel and casino (first phase 3,500 rooms) on the site of the former Echelon property was not included in the base forecast. Genting Group has the financing but awaits a favorable decision
from the Gaming Control Board regarding its application for a gaming license before commencing construction2.
3. Demand Side Management. In the 2014 ESP Update Forecast, DSM reductions were based on January 2013 preliminary budgets developed for that ESP Update. DSM reductions in the 2014 ERCR Forecast are based on the 2014 DSM budget (with estimated reductions for 2015 also), and expected reductions approved for the 2014 and 2015 budget by the Nevada Commission in Docket No. 13-07002. DSM savings for 2015 are expected to be 14 GWh greater than the 2014 ESP Update Forecast. DR reduction estimates used in the 2014 ERCR Forecast are the same as the 2014 ESP Update. (See Technical Appendix LF-1 for further details) 4. Demonstration Programs. The 2014 ERCR Forecast of net metering reductions,
including SolarGenerations and WindGenerations projects are significantly higher than those included in the 2014 ESP Update Forecast. The primary assumption change is that the revised forecasts reflect the solar PV increases mandated by Assembly Bill 328 (“AB 328”) passed by the Nevada legislature in 2013. In addition, the 2014 ERCR Forecast includes three proposed five MW capacity roof top units for one of the gaming companies: one each for 2014, 2015, and 2016. The 2014 ERCR Forecast reductions are 57 GWh more in 2015 than the 2014 ESP Update Forecast. The 2014 ESP Update Forecast assumed one megawatt of large customer solar for 2015 outside of the Company’s generation projects beginning in 2014, while this 2014 ERCR Forecast assumes two megawatts annually beginning in 2017.
5. Population Forecast. The 2010 Census estimated the population of Clark County at about 1,951,000. This figure was used as the benchmark for 2010 in both the 2014 ESP Update and 2014 ERCR Forecasts. The population forecast released by Global Insight in September 2013 projects a population growth of 1.8 percent annually from 2013 through 2015 for the MSA. The UNLV Center for Business and Economic Research (“CBER”) July 2013 long-term forecast was 1.1 percent for 2013, 1.5 percent for 2014 and 1.2 percent for 2015. The CBER June 2013 short-term forecast projected population growth of 1.5 percent for 2013 and 2.0 percent for 2014. Given that residential customer growth was 1.2 percent from 2012 through 2013, Global Insight’s projections of 1.8 percent for 2014 and CBER’s short term forecast growth rate or 2 percent for 2014 appeared too high. Therefore the company used the CBER long term growth rate for 2014.3The 2014 ESP Update forecast assumed population growth rates of 1.5 percent for 2014 and 1.8 percent for 2015. The CBER long term growth rates were used for the entire 2014 ERCR Forecast, except for 2013 where the CBER short term growth rate was used. The CBER long-term population forecasted growth rates were 2“The next Strip mega-resort is underway”, Howard Stutz, Casino City Times, March 14, 2014.
reasonable and smoothing between various forecasts was unnecessary as was done for the 2014 ESP Update Forecast. See Technical Appendix Item LF-1 for a full discussion of the population forecast.
6. Weather Normalization. The 2014 ESP Update Forecast and 2014 ERCR Forecast assume normal weather (20-year average). Normal weather concepts include monthly heating degree-days (“HDD”) and cooling degree-days (“CDD”), and peak day temperatures. The 2014 ESP Update Forecast used the period January 1993 through December 2012, while the 2014 ERCR Forecast uses the historical period September 1993 through August 2013.
7. Historical Data. The 2014 ESP Update Forecast utilized historical sales and customer counts through December 2012, while the 2014 ERCR Forecast utilized historical data through August 2013.
8. Distribution Only Service. The 2014 ESP Update Forecast included a reduction of 20,860 MWh for the City of North Las Vegas premises transitioning to distribution-only service (“DOS”) on July 1, 2013 and 66,000 MWh for the Nevada Test Site (representing 10 months of usage), which took service from Valley Electric Association on November 1, 2012. The 2014 ERCR Forecast includes a reduction of 12,126 MWh for the Nevada Test Site (representing 2 months of usage)4 for that customer and the 20,860 MWh for the City q of North Las Vegas premises, which did not take DOS service until October 1, 2013. The 2014 ERCR Forecast also includes the projected 13,706 MWh for the City of Henderson premises expected to take DOS service in 2014.
9. The 2014 ERCR Forecast electricity price estimates were updated from the 2014 ESP Update Forecast using more current revenue forecasts provided by the Company’s Financial Planning and Analysis Department.
10. The 2014 ESP Update Forecast and 2014 ERCR Forecast each assume approximately one percent of new cars on the road in 2015 are electric plug-in vehicles. This results in a sales increase of approximately 6,400 MWh by 2015, or about 0.03 percent of total sales.
The methodology for developing the extreme weather transmission peak forecast has not changed. In the 2012 Integrated Resource Plan (“IRP”) the forecast adjustment factor of 4.23 percent was deemed reasonable by all parties based on peak demand modeling results. The ERCR peak load adjustment is calculated by applying the 4.23 percent adjustment factor to the ERCR base load forecast.
Figure LF-5 is a comparison of the summer peak demand and Figure LF-6 is the system energy comparison for the 2014 ERCR Forecast and the 2014 ESP Update Forecast (extended to 2033). 4 Technically, based on prior practice, no reduction should have taken place for the test site after 2013. The entire
For clarity, the 2014 ERCR Forecast is labeled “Nov 13 Fcst” or “Nov-13” and 2014 ESP Update Forecast is labeled “Jan-13 Fcst” or “Jan-13.” “WN” represents weather normalized data.
FIGURE LF-5: NEVADA POWER SYSTEM PEAK FORECASTS
FIGURE LF-6: NEVADA POWER SYSTEM ENERGY FORECASTS
5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23
NVE South Peak (MW)
Recorded WN Nov 13 Fcst Jan 13 Fcst 20,000 20,500 21,000 21,500 22,000 22,500 23,000 23,500 24,000 24,500 25,000
NVE South System Energy (GWH)
Recorded WN
Nov 13 Fcst Jan 13 Fcst
The system peak forecasts are very similar, with the 2014 ERCR Forecast being higher in the near term due to a higher weather normalized peak demand than was expected in 2013. The 2014 ERCR energy forecast is lower than the 2014 ESP Update Forecast. As discussed above, the primary drivers of the lower energy forecast are the reduction in sales in the small and large C&I class, and the impact of DSM on usage per customer.
Figures LF-7 through LF-10 graph the changes in the major driver variables for the class sales models. As noted above, the population forecast for the 2014 ERCR Forecast (shown as “Nov-13”) is taken completely from the CBER July 2013 long term forecast. The 2014 ESP Update (shown as “Jan-13”) forecasted population is a combination of the December 2012 CBER short-term and 2012 CBER long-short-term forecasts. Note that some economic variable history has been revised by the federal government, hence the differences in the variables for the two forecasts prior to 2013.
FIGURE LF-7: POPULATION GROWTH RATE FORECAST COMPARISON
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23
Clark County Annual Population Growth
Nov-13 Jan-13
FIGURE LF-8: REAL HOUSEHOLD INCOME GROWTH RATE FORECAST COMPARISON
FIGURE LF-9: REAL GROSS METRO PRODUCT (“GMP”) GROWTH RATE FORECASTS -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 2019 20 20 20 21 2022 20 23
Clark County Real Household Income
Growth
Nov-13 Jan-13 -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23Clark County Real GMP Growth
Nov-13 Jan-13
FIGURE LF-10: NON-MANUFACTURING EMPLOYMENT GROWTH RATE FORECASTS
Additional comparisons are contained in Technical Appendix LF-1. FORECAST MODEL DEVELOPMENT
D.
This Section provides a description and discussion of four facets of the forecast model development: 1. Forecast Drivers; 2. Forecast Models; 3. System Energy, Sales and Peak Forecasts; and 4. Customer Class Sales and Customer Forecasts.
1. FORECAST DRIVERS
The forecast database was updated with actual data through August 2013. Data elements are described below.
Sales and Customer Data
Monthly billed sales and customer data are extracted from the Company’s billing system. Monthly average use is calculated from the billed sales and is used in estimating monthly average use forecast models for the RES and small C&I classes. RES customer and average use models are estimated with data covering the period January 1998 through August 2013. Small C&I and large C&I class models are estimated with monthly data from January 2003 through August 2013. 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23 % G r o w t h
Clark County Non-Manufacturing
Employment Growth and Forecast
Nov-13 Jan-13
Since 2007, total sales have declined on average 0.3 percent per year. Due to large hotel openings in 2009 through 2011, offsetting the impact of the recession, the large C&I class sales growth has been generally flat since 2007.5 Nevada Power saw the largest decline in sales right after the start of the recession in late 2008. Weather normalized sales fell 1.7 percent in 2009, 0.4 percent in 2010 and 0.3 percent in 2011. Small C&I sales fell 3.4 percent in 2008 and 2.0 percent in 2009. Residential sales growth slowed as well, falling 0.7 percent in 2009 and 0.2 percent in 2010. Large C&I sales fell 0.6 percent in 2009 but grew 0.7 percent in 2010 with the opening of the City Center hotel and casino. From 2007 through 2013, residential sales have declined an average of 0.2 percent annually. Sales appeared to be recovering with 2012 residential sales up 2.7 percent, small C&I sales up 2.4 percent, but small C&I and large C&I sales growth stalled in 2013 with both classes registering negative growth rates. Figure LF-11 shows historical class sales trends.
FIGURE LF-11: HISTORICAL CLASS SALES TRENDS
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 GWh Res Small C&I Large C&I Other AAGR 2003-07 2007-13 Res 4.8% -0.2% Small C&I 5.1% -0.7% Large C&I 4.4% 0.0% Other -13.0% -1.8% Total 4.4% -0.3%
Residential. The recession had a significant impact on customer growth. Residential customer growth leveled off significantly starting in 2008. Customer annual growth averaged just 0.6 percent between 2007 and 2011. This compares with 4.4 percent annual customer growth before the recession. Customer growth appears to be building again with 1.5 percent growth in 2012 and 1.2 percent for 2013. Figure LF-12 illustrates the customer trend.
FIGURE LF-12: RESIDENTIAL CUSTOMERS (2003 TO 2013)
400,000 450,000 500,000 550,000 600,000 650,000 700,000 750,000 800,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 AAGR 2003-07 4.4% 2007-13 0.8%
Since 2007 average use per customer has been declining at an annual rate of 1.0 percent per year. Between 2003 and 2007, average use increased 0.3 percent annually. We expect to see average use continue the downward trend in the near term as the new lighting standards are phased in. Figure LF-13 shows the residential average use trend.
FIGURE LF-13: WEATHER NORMALIZED RESIDENTIAL AVERAGE USE (2003 TO 2013) 5,000 6,000 7,000 8,000 9,000 10,000 11,000 12,000 13,000 14,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 kwh per customer AAGR 2003-07 0.3% 2007-13 -1.0%
Small C&I. Nevada Power serves approximately 103,000 small C&I customers. Small C&I customer growth has matched residential customer growth with the number of new small C&I customers averaging 4.7 percent annual growth before the recession. The slowdown in small C&I customer growth began in 2009 with annual customer growth of 0.5 percent from 2008 through 2012. Growth of 1.5 percent was recorded from 2012 through 2013 and annual growth in the 1.0 percent to 2.0 percent range is expected over the next few years. Figure LF-14 shows historical small C&I customers.
FIGURE LF-14: SMALL C&I CUSTOMERS (2003 TO 2013)
Figure LF-15 shows small C&I average use. Between 2003 and 2007, average use was virtually flat. Average use declined 1.8 percent annually between 2007 and 2013.
40,000 50,000 60,000 70,000 80,000 90,000 100,000 110,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 AAGR 2003-08 4.7% 2008-13 0.8%
FIGURE LF-15: SMALL C&I AVERAGE USE (2003 TO 2013)
Large C&I. Approximately 1,600 customers in the large C&I class make up Nevada Power’s largest usage customers. Between 2003 and 2008, large C&I sales averaged a 4.0 percent annual increase. From 2008 through 2013 average annual sales dropped by 0.5 percent per year. The largest decline, 2.4 percent, occurred from 2012 through 2013. Without the loss of the Nevada Test Site, the decline in 2013 would have been about 1.6 percent. Figure LF-16 shows large C&I sales.
FIGURE LF-16: LARGE C&I SALES (2003 TO 2013)
39,000 40,000 41,000 42,000 43,000 44,000 45,000 46,000 47,000 48,000 49,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 kwh per customer AAGR 2003-07 0.0% 2007-13 -1.8% 4,500 5,000 5,500 6,000 6,500 7,000 7,500 8,000 M W h Thousands AAGR 2003-08 4.0% 2008-13 -0.5%
Hotel/Motel Rooms. Hotel/motel room growth averaged 1.2 percent growth from 2003 through 2010. The City Center opening in late 2009 and the Cosmopolitan first phase opening in late 2010 caused continued growth even as the economy was declining. The closing of the Sahara in May 2011 offset the opening of the second phase of the Cosmopolitan in the fall of 2011. Since 2010, annual average growth has been 0.3 percent with a number of closings of older small hotels and motels nearly offsetting growth. Figure LF-17 is a summary for the hotel/motel room counts.
FIGURE LF-17: AVERAGE ANNUAL HOTEL/MOTEL ROOMS (2003 TO 2013)
115,000 120,000 125,000 130,000 135,000 140,000 145,000 150,000 155,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Number AAGR 2003-10 2.1% 2010-13 0.3%
Population and Economic Data
Population and economic data (historical and forecast) is provided by Global Insight. Population projections from the CBER are also used in developing the final population projections. Monthly economic data used in forecasting sales include Real Gross Metro Product, Non-Manufacturing Employment, and Real Household Income. Hotel/motel room growth for the near term is taken from the LVCVAs most recent Resorts and Attractions Construction Bulletin as well as newspaper articles and discussions with LVCVA personnel and internal MAEs.
For the 2014 ERCR Forecast, the population growth rate forecast was the same as the CBER July 2013 Long Term Population Forecast (2013-2050). The derivation of the population and the economic data series are discussed in more detail in Technical Appendix LF-1.
Population projections drive the residential customer forecast. Figure LF-18 shows historical and forecasted population.
FIGURE LF-18: CLARK COUNTY POPULATION (2003 TO 2023)
From 2000 through 2007, population growth averaged 4.8 percent per year. As the economy slowed, so did population growth. In 2008 regional population growth slowed to 0.8 percent and actually went negative (-0.8 percent) in 2009 and (-0.1 percent) in 2010. Between 2007 and 2013, population growth averaged just 0.6 percent. Population growth does appear to be
customer growth is strongly correlated with population, customer growth follows a similar projection.
Real Household Income impacts residential average usage per customer. Household Income fell significantly during the recession. Real Household Income fell 9.6 percent in 2009. While steady improvement in household income is expected, forecasted Real Household Income does not reach the 2007 level until 2020. Figure LF-19 shows historical and forecasted Household Income.
FIGURE LF-19: REAL PER HOUSEHOLD INCOME (2003 TO 2023)
Real output and employment drives the small and large C&I customer and sales forecasts. Before the recession the region experienced strong employment and output growth, with employment growth averaging 5.1 percent between 2003 and 2007 and real output annual growth averaged 7.3 percent. By 2008 employment and output growth came to halt; real economic output fell 7.1 percent in 2009.
Nevada is now seeing a recovery in both employment and output. Long-term employment growth is projected to average 2.1 percent per year and output growth is 3.5 percent per year. While economic growth is positive it is significantly lower than that experienced before the recession. It is not until 2017 that employment levels reach the peak 2007 employment level. Figures LF-20 and LF-21 show employment and output projections.
FIGURE LF-20: EMPLOYMENT (2003 TO 2023)
Electricity Prices. Historical and projected real electricity prices are generated for each of the primary revenue classes. Historical prices are constructed from billed revenues and sales. The price series is defined as a 12-month moving average of the real monthly revenue per kWh. The price forecast is based on Nevada Power’s projection of future operating costs and associated revenue requirements. Figure LF-22 shows historical and forecasted electricity prices.
FIGURE LF-22: PRICES (2003 TO 2023)
Since 2008 real electricity prices have declined for both residential and commercial customers. Both are expected to experience slight upward price increases over the next ten-years.
End-Use Saturation and Efficiency Trends. For the last five years, Nevada Power has used an end-use forecasting approach that explicitly incorporates end-use saturation and efficiency projections for both the residential and commercial revenue classes. Itron Inc. develops end-use saturation and efficiency projections for nine U.S. Census Divisions using data generated from the Energy Information Administration’s (“EIA”) Annual Energy Outlook (“AEO”). The current forecast is based on the EIA’s 2013 Annual Energy Outlook.
The 2014 ERCR Forecast starts with end-use saturation and efficiency projections for the Mountain Census Division. The census-level residential end-use saturations are calibrated to appliance ownership reported by Nevada Power customers. Nevada Power conducted residential
appliance saturation surveys in late 2008 and again in the spring of 2011.6 Commercial end-use intensities were adjusted to reflect Nevada Power’s commercial customer mix based on market surveys conducted in December 2008 and January 2009.
End-use efficiency projections include the impact of the American Recovery and Reinvestment Act passed in March 2009, the efficiency and building code standard changes enacted as part of the federal 2007 Energy Information and Security Act, and new end-use standards set by the Department of Energy (“DOE”) in 2011.
End-use saturation and average stock efficiency projections are combined to generate projected energy intensities. For the residential sector, energy intensities are measured on an annual kWh per household basis and for the commercial sector energy intensities are calculated on an annual kWh per square foot. Figure LF-23 shows total residential energy intensity (summed across the end-use intensities) and Figure LF-24 shows total commercial energy intensity. Intensity projections also reflect the expected impact of future DSM activity.
FIGURE LF-24: TOTAL COMMERCIAL ENERGY INTENSITY (2003 TO 2023)
Weatherization. Historical and normal monthly HDDs and CDDs are based on reported daily temperature data from the McCarran International Airport in Las Vegas. As the models are estimated using monthly billed sales, HDD and CDD must also be on a billing-cycle (vs. calendar-month) basis. Cycle-weighted HDD and CDD are derived by combining the historical meter-read schedule with daily calculated HDD and CDD.
Forecasted or normal HDD and CDD are based on twenty years of historical weather data. Normal degree-days are calculated using reported temperature data from September 1993 through August 2013. Figures LF-25 and LF-26 show actual and forecasted calendar CDD and HDD.
FIGURE LF-25: ANNUAL CALENDAR CDD (LAS VEGAS, 2003 TO 2023)
FIGURE LF-26: ANNUAL CALENDAR HDD (LAS VEGAS, 2003 TO 2023)
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023
Degr
ee Da
ys
0 500 1,000 1,500 2,000 2,500Degr
ee Da
ys
Demand Side Management. The impact of DSM on future load is captured through the end-use intensity projections. As programs impact end-use saturations (e.g., removing inefficient refrigerators), encourage the adoption of more efficient technology, tune-up existing technologies (e.g., commercial air conditioning programs), or affect behavior, end-use energy intensities improve. Improvements in end-use intensities in turn drive average usage down through the forecast model specification. Figure LF-27 shows historical and forecasted DSM savings projections.
FIGURE LF-27: ANNUAL DSM SAVINGS ESTIMATES (2005 TO 2023)
On a cumulative basis, DSM programs have reduced sales by nearly 1.7 million MWh since 2005. Future DSM program activity is expected to reduce sales another 1.3 million MWh over the next ten years.
The DSM planning group provided historical and projected annual DSM savings by customer class (residential and nonresidential) and use. For the residential sector, the targeted end-uses are water heating, refrigeration, and miscellaneous use. For the nonresidential sector, the DSM-targeted end-uses are cooling, lighting, and miscellaneous loads. The method used in adjusting the end-use intensities for DSM programs is explained in Technical Appendix LF-1. It is important to note that the forecast before adjustments (referred to as the baseline forecast), includes the impact of both historical and some future DSM savings – it is not a “NO DSM” forecast, as some future DSM savings are captured in the baseline forecast. Even before further
0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 Mw h Res NRes
adjusting for DSM-driven efficiency improvements, baseline residential energy intensity for the Mountain Census Division averages 0.4 percent annual decline between 2013 and 2023, and commercial intensity increases just 0.2 percent annually over this period. As the intensity projections and estimation process capture some future as well as historical DSM impacts, the impact of future DSM savings are adjusted down to avoid a “double-count” of DSM savings in the forecast.
2. FORECAST MODELS
Monthly forecast models are run for the following customer classifications: x Residential--RES
x Small Commercial and Industrial-- small C&I x Large Commercial and Industrial-- large C&I
The Public Authority sales forecast are based on recent history. Because the STLight model did not pick up the reduction in usage due to local government lamp change out programs for 2011 through 2013, the forecast for that class was assumed to decline 1.0 percent per year through 2017 and then flat growth was assumed through 2023.7
Residential
The residential forecast is generated using separate average use and customer forecast models. The average use model is estimated using an SAE specification where monthly average use is estimated as a function of a heating variable (XHeat), cooling variable (Xcool) and other use variable (Xother) as shown below:
m m m
m
m a b XHeat b XCool b XOther
AvgUse 1u 2u 3u H
The end-use variables incorporate end-use intensity trends as well as household income, price, HDD, and CDD. The specific construction of the model variables are discussed in Technical Appendix LF-1. The average use model is estimated using linear regression with billed sales and customer data from January 1998 through August 2013. Figure LF-28 shows actual and predicted results.
FIGURE LF-28: RESIDENTIAL AVERAGE USE MODEL RESULTS
The estimated model fits the historical data well both from a graphical view and statistical perspective. The model Adjusted R2 is 0.99 and the mean absolute percentage error (“MAPE”) is 3.5 percent.
The residential customer model relates monthly customer counts to population estimates. The model is estimated using reported monthly customer data from January 1998 through August 2013. Figure LF-29 shows the model results.
FIGURE LF-29: RESIDENTIAL CUSTOMER FORECAST MODEL
There is a strong correlation between population and residential customers. The model’s Adjusted R2 is close to 1.00 and the average percent error is less than 0.2 percent.
Estimated residential model coefficients and model statistics are provided in Technical Appendix LF-1.
Small C&I. Separate average use and customer forecast models are also estimated for the small C&I class. As there are relatively little industrial sales in this class, the small C&I average use model is estimated using a commercial SAE model specification. Commercial heating (Xheat), cooling (Xcool), and other end-uses (Xother) are constructed by combining commercial end-use intensity trends with Real Gross Area Product, Price, HDD, and CDD. The model is estimated using monthly sales and customer data from January 2000 through August 20113. Figure LF-30 shows actual and predicted commercial average use.
FIGURE LF-30: SMALL C&I AVERAGE USE MODEL
In addition to the SAE model variables, the models include binary shift variables to account for the drop in average usage in 2009 and 2010 that could not be explained by the end-use variables. As in the residential model, the SAE model specification explains historical commercial usage well with an Adjusted R2 of 0.98 and a MAPE of 2.1 percent.
Small C&I customers are specified as a function of employment and a 12 month lag of small C&I customer counts. Figure LF-31 depicts actual and forecasted small C&I customers. The model MAPE is 0.6 percent.
FIGURE LF-31: SMALL C&I CUSTOMER FORECAST MODEL
Estimated small C&I average use and customer model coefficients and statistics are provided in the Technical Appendix LF-1.
Large C&I. The SAE large C&I model specification explains historical large C&I sales quite well. The large C&I sales forecast is based on a total sales model. The model is estimated using billed sales data from January 2000 through August 2013. Model variables XCool and XOther include commercial end-use intensity trends that are combined with employment, price, and CDD. The model does not include XHeat as heating is statistically insignificant. Figure LF-32 shows actual and predicted sales.
FIGURE LF-32: LARGE C&I SALES FORECAST MODEL
Public Authority and STLight Classes. Public Authority sales are driven by seasonal demand. As historical sales have been greatly affected by customers taking DOS, the forecast is based on the estimate of sales for the current year. Figures LF-33 is a graphical representation of Public Authority sales showing the large drop in sales from 2003 through 2007 caused by customers exiting the system.
FIGURE LF-33: PUBLIC AUTHORITY SALES HISTORY AND FORECAST (MWH)
Street lighting sales have been declining since 2010 as efficiency gains offset customer growth. Regression modeling did not capture this trend, so the forecast was assumed to continue declining through 2017 and then flattened after 2017.
0 50,000 100,000 150,000 200,000 250,000 300,000 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23
Figure LF-34 is a summary of the historical and forecast Public Authority and Street Lighting sales.
FIGURE LF-34: STREET LIGHT AND PUBLIC AUTHORITY SALES (MWH)
Year Street chg Public chg
Lighting Authority 2003 153,224 257,671 2004 158,063 3.2% 249,403 -3.2% 2005 166,890 5.6% 183,713 -26.3% 2006 170,478 2.2% 110,240 -40.0% 2007 171,302 0.5% 63,835 -42.1% 2008 173,095 1.0% 57,797 -9.5% 2009 184,711 6.7% 55,303 -4.3% 2010 177,006 -4.2% 55,523 0.4% 2011 169,576 -4.2% 54,998 -0.9% 2012 166,378 -1.9% 52,565 -4.4% 2013 155,006 -6.8% 55,827 6.2% 2014 153,943 -0.7% 54,000 -3.3% 2015 152,404 -1.0% 54,000 0.0% 2016 150,880 -1.0% 54,000 0.0% 2017 149,371 -1.0% 54,000 0.0% 2018 149,371 0.0% 54,000 0.0% 2019 149,371 0.0% 54,000 0.0% 2020 149,371 0.0% 54,000 0.0% 2021 149,371 0.0% 54,000 0.0% 2022 149,371 0.0% 54,000 0.0% 2023 149,371 0.0% 54,000 0.0% 2003 - 13 0.2% -12.4% 2013 - 23 -0.4% -0.3%
3. CUSTOMER CLASS SALES AND CUSTOMER FORECASTS Residential
Residential sales are projected to increase on average 1.0 percent annually over the next ten years. With average use expected to decline 0.2 percent per year, sales growth will come from adding new customers. Figure LF-35 shows actual and forecasted residential sales, customers, and average use.
FIGURE LF-35: RESIDENTIAL SALES, CUSTOMERS, AND AVERAGE USE
Year Sales chg Custs chg Avg Use chg
(MWh) (kWh) 2003 7,455,525 606,187 12,299 2004 7,741,793 3.8% 633,907 4.6% 12,213 -0.7% 2005 8,511,648 9.9% 667,742 5.3% 12,747 4.4% 2006 8,906,535 4.6% 700,309 4.9% 12,718 -0.2% 2007 8,979,352 0.8% 720,116 2.8% 12,469 -2.0% 2008 8,883,852 -1.1% 724,663 0.6% 12,259 -1.7% 2009 8,717,717 -1.9% 725,557 0.1% 12,015 -2.0% 2010 8,639,177 -0.9% 729,565 0.6% 11,842 -1.4% 2011 8,603,574 -0.4% 737,500 1.1% 11,666 -1.5% 2012 8,832,994 2.7% 748,226 1.5% 11,805 1.2% 2013 8,871,170 0.4% 757,052 1.2% 11,718 -0.7% 2014 8,968,977 1.1% 768,297 1.5% 11,674 -0.4% 2015 8,985,334 0.2% 777,535 1.2% 11,556 -1.0% 2016 9,009,791 0.3% 786,458 1.1% 11,456 -0.9% 2017 9,083,472 0.8% 795,438 1.1% 11,419 -0.3% 2018 9,206,822 1.4% 804,507 1.1% 11,444 0.2% 2019 9,334,829 1.4% 813,309 1.1% 11,478 0.3% 2020 9,449,564 1.2% 821,678 1.0% 11,500 0.2% 2021 9,565,359 1.2% 829,994 1.0% 11,525 0.2% 2022 9,654,000 0.9% 837,917 1.0% 11,521 0.0% 2023 9,756,113 1.1% 845,641 0.9% 11,537 0.1% 2003 - 13 1.8% 2.2% -0.5% 2013 - 23 1.0% 1.1% -0.2%
Small Commercial and Industrial
Figure LF-36 shows historical and forecasted small C&I sales and customers.
FIGURE LF-36: SMALL C&I SALES, CUSTOMERS, AND AVERAGE USE
In 2009 sales fell 3.4 percent and another 2 percent in 2010 largely as a result of the recession. Sales grew in 2012 but fell back in 2013 due to the sluggish tourism industry. Sales are expected to continue to increase over the next ten years averaging 1.6 percent annual growth between 2013 and 2023. Like residential, given flat to declining average usage growth, sales will largely be driven by the addition of new small C&I customers. While we expect to see moderate small C&I sales growth, sales do not reach the 2008 level until 2018.
Year Sales chg Custs chg Avg Use chg
(MWh) (kWh) 2003 3,742,569 78,794 47,498 2004 3,922,916 4.8% 83,149 5.5% 47,179 -0.7% 2005 4,243,282 8.2% 87,819 5.6% 48,318 2.4% 2006 4,409,738 3.9% 92,367 5.2% 47,741 -1.2% 2007 4,572,507 3.7% 96,579 4.6% 47,345 -0.8% 2008 4,604,318 0.7% 99,089 2.6% 46,466 -1.9% 2009 4,446,838 -3.4% 99,446 0.4% 44,716 -3.8% 2010 4,358,366 -2.0% 100,270 0.8% 43,466 -2.8% 2011 4,352,029 -0.1% 100,712 0.4% 43,213 -0.6% 2012 4,455,630 2.4% 101,410 0.7% 43,937 1.7% 2013 4,390,633 -1.5% 102,981 1.5% 42,635 -3.0% 2014 4,462,378 1.6% 104,166 1.2% 42,839 0.5% 2015 4,477,072 0.3% 105,522 1.3% 42,428 -1.0% 2016 4,524,629 1.1% 107,187 1.6% 42,213 -0.5% 2017 4,595,393 1.6% 109,138 1.8% 42,106 -0.3% 2018 4,678,464 1.8% 111,269 2.0% 42,046 -0.1% 2019 4,767,113 1.9% 113,514 2.0% 41,996 -0.1% 2020 4,861,241 2.0% 115,772 2.0% 41,990 0.0% 2021 4,956,184 2.0% 117,962 1.9% 42,015 0.1% 2022 5,048,891 1.9% 120,076 1.8% 42,047 0.1% 2023 5,144,551 1.9% 122,075 1.7% 42,142 0.2% 2003 - 13 1.6% 2.7% -1.1% 2013 - 23 1.6% 1.7% -0.1%
Large C&I. The large C&I class includes Nevada Power’s larger customers. Large C&I sales were flat from 2008 through 2012 and then dropped 2.4 percent in 2013 due to the Nevada Test Site leaving the system and flat growth in tourism. Over the longer term we expect large C&I sales to average 0.9 percent growth.
The large C&I forecast is derived using a total sales model instead of average use models used in forecasting residential and small C&I sales. In estimating the sales model, we found that employment, output and hotel/motel room count were strongly linked to large C&I sales growth. Figure LF-37 compares large C&I sales with historical Real GMP, hotel/motel rooms and employment. For analysis purposes, each series is indexed to 1.0.
FIGURE LF-37: INDEXED MONTHLY LARGE C&I SALES, REAL GMP, HOTEL/MOTEL ROOMS EMPLOYMENT AND THE LARGE C&I ECONOMIC
Figure LF-38 shows forecasted Large Commercial and Industrial sales, customer count and average usage.
FIGURE LF-38: LARGE C&I LOAD
Year Sales chg Custs chg Avg Use chg
(MWh) (kWh) (MWh) (MWh) 2003 6,291,615 1,315 4,783 2004 6,547,817 4.1% 1,374 4.5% 4,764 -0.4% 2005 6,985,667 6.7% 1,423 3.6% 4,908 3.0% 2006 7,269,934 4.1% 1,483 4.2% 4,901 -0.1% 2007 7,471,931 2.8% 1,553 4.7% 4,811 -1.8% 2008 7,645,112 2.3% 1,611 3.8% 4,745 -1.4% 2009 7,596,371 -0.6% 1,643 2.0% 4,623 -2.6% 2010 7,653,131 0.7% 1,630 -0.8% 4,695 1.6% 2011 7,627,037 -0.3% 1,613 -1.1% 4,730 0.7% 2012 7,645,339 0.2% 1,587 -1.6% 4,819 1.9% 2013 7,458,211 -2.4% 1,600 0.8% 4,661 -3.3% 2014 7,562,728 1.4% 1,610 0.6% 4,697 0.8% 2015 7,587,539 0.3% 1,625 0.9% 4,669 -0.6% 2016 7,632,848 0.6% 1,645 1.2% 4,640 -0.6% 2017 7,702,799 0.9% 1,665 1.2% 4,626 -0.3% 2018 7,789,414 1.1% 1,685 1.2% 4,623 -0.1% 2019 7,875,032 1.1% 1,705 1.2% 4,619 -0.1% 2020 7,956,944 1.0% 1,725 1.2% 4,613 -0.1% 2021 8,015,013 0.7% 1,745 1.2% 4,593 -0.4% 2022 8,061,588 0.6% 1,765 1.1% 4,567 -0.6% 2023 8,122,478 0.8% 1,785 1.1% 4,550 -0.4% 2003 - 13 1.7% 2.0% -0.3% 2013 - 23 0.9% 1.1% -0.2%
Electric Vehicle Sales
EV sales were estimated using the same methodology used in the 2014 ESP Update Forecast, with the stipulated limitation that 1.0 percent of new vehicles on the road beginning in 2016 would be EV. Global Insight’s total new car and truck registrations were extracted from the 2013 Nevada forecast and multiplied by the forecasted percentage of new EV vehicle registrations to derive the EV forecast. Based on prior research, Nevada Power assumed that an EV was driven for 10,000 annual miles and that for each 3.3 miles, one kWh was necessary for charging, for annual use per EV of 3,300 kWh. The total EV sales for Nevada were then split between Sierra and Nevada Power by relative customer forecasts shares. Figure LF-39 is a summary of the EV sales forecast for Nevada Power.
FIGURE LF-39: ELECTRIC VEHICLE SALES (MWH) AT THE METER
Year Annual MWH MWh as a % of Base Retail Sales 2014 1,942 0.01% 2015 4,876 0.02% 2016 8,051 0.04% 2017 11,243 0.05% 2018 14,401 0.07% 2019 17,510 0.08% 2020 20,543 0.10% 2021 23,687 0.11% 2022 26,963 0.13% 2023 30,303 0.14%
Net Metering Impacts. The impact of the Company’s small net metering projects includes sales reductions for the Solar, and Wind Generations forecasts. In addition 15 megawatts of capacity is expected to be installed outside the program from 2014 through 2016 and two megawatts of capacity is assumed to be installed outside the program annually beginning in 2017. Figure LF-40 is a summary of the sales reductions by program.8
FIGURE LF-40: NET METERING SALES REDUCTIONS (MWH) AT THE METER
8 The wind calculation incorrectly divided one term by 1000. The total MWh from 2014 through 2017 are 339, 679,
1,018, 1,357 and 1,969 MWh. After 2017 the wind generations is assumed to be flat. This error is immaterial with respect to the total sales forecast.
Year Solar PV Wind Total
2013 31,395 0.34 31,395 2014 67,908 0.68 67,909 2015 101,145 1.02 101,146 2016 124,426 1.36 124,428 2017 154,817 1.70 154,818 2018 186,714 1.70 186,716 2019 218,351 1.70 218,353 2020 233,034 1.70 233,036 2021 236,756 1.70 236,758 2022 240,478 1.70 240,480 2023 242,101 1.70 242,103
Energy Efficiency Program Impacts. As noted in Section D above in the Demand Side Management discussion, the incremental DSM from 2014 and beyond has been integrated into the energy intensities (“EIs”) for residential, small C&I and large C&I classes. Figure LF-41 shows the aggregated incremental DSM MWH integrated into the EIs.
FIGURE LF-41: AGGREGATED INCREMENTAL DSM PROGRAM IMPACTS (MWH) AT THE METER
The Demand Response (“DR”) load shape is subtracted from the system hourly loads including losses. The aggregated sales reductions by class are shown in Figure LF-42. These reductions are total impacts from operating all prior DR as well as new DR.
FIGURE LF-42: DR PROGRAM IMPACTS (MWH) AT THE METER
Year Res NonRes Total
2014 45,094 83,840 128,934 2015 92,409 176,680 269,089 2016 136,692 271,320 408,012 2017 177,580 362,110 539,690 2018 218,468 452,900 671,368 2019 259,356 543,690 803,046 2020 300,244 634,480 934,724 2021 338,342 725,270 1,063,612 2022 373,650 816,060 1,189,710 2023 404,998 906,850 1,311,848
Year Res Small C&I Large C&I Total
2014 7,801 772 1,383 9,955 2015 10,016 991 2,917 13,924 2016 12,858 1,272 3,745 17,874 2017 14,206 1,405 4,137 19,748 2018 14,206 1,405 4,137 19,748 2019 14,206 1,405 4,137 19,748 2020 14,206 1,405 4,137 19,748 2021 14,206 1,405 4,137 19,748 2022 14,206 1,405 4,137 19,748 2023 14,206 1,405 4,137 19,748
4. SYSTEM ENERGY, SALES AND PEAK FORECASTS
The system hourly load forecast is derived using a “bottom-up” approach. In the first step forecasted class sales are combined with the class hourly load profiles to generate customer class hourly load forecasts through 2034. The hourly class profiles are derived from hourly regression models that relate the hourly class load to weather conditions, day of the week, holidays, and seasonal factors. The models are estimated from load research data through September 2012. This was the most recent data at the time the profiles were developed. Class forecasted sales are then applied to the class hourly load profiles to produce the forecasted class loads. The class hourly loads are not adjusted for solar net metering, EVs, line losses or DR.
In the next step, class hourly load forecasts are aggregated and adjusted for net metering from solar systems,9 EVs and line losses to produce system hourly loads prior to DR through 2034. The system hourly load forecast is then adjusted for DR program impacts.
The final system hourly load and peak demand forecasts are the basis for resource planning. Annual energy is calculated by summing the hourly loads across the year. Then the summer and winter peak demand forecasts are derived by finding the maximum hourly load in each season. As the underlying hourly class load forecasts are based on daily normal temperatures, the resulting peak represents demand for the 50 percent probability case. Figure LF-43 is a summary of the summer and winter peak MW, system energy (GWh), load factor and Company Use/Losses forecast. Note that all peak demand historical values are weather normalized and historical system energy is not weather normalized.
9 The wind net metering reduction is subtracted from the class sales as the impacts are too small for load shape
FIGURE LF-43: ENERGY (GWH), SUMMER AND WINTER PEAKS (MW), LOAD FACTOR AND COMPANY USE/LOSSES (GWH)
Year Energy GWh chg Summer Peak MW chg Winter Peak MW (1) (2) chg Load Factor Co. Use & Losses GWh (3) 2003 19,299 4,781 2,290 46.1% 920 2004 3.5%19,968 4,944 3.4% 2,408 5.2% 46.0% 832 2005 3.9%20,752 5,226 5.7% 2,571 6.8% 45.3% 886 2006 7.7%22,354 5,673 8.6% 2,673 4.0% 45.0% 1,152 2007 2.2%22,843 5,650 -0.4% 2,742 2.6% 46.2% 1,031 2008 0.0%22,846 5,640 -0.2% 2,800 2.1% 46.1% 1,233 2009 -3.4%22,061 5,490 -2.7% 2,755 -1.6% 45.9% 807 2010 -1.6%21,718 5,495 0.1% 2,708 -1.7% 45.1% 845 2011 -0.3%21,659 5,556 1.1% 2,716 0.3% 44.5% 843 2012 3.2%22,363 5,571 0.3% 2,666 -1.8% 45.7% 773 2013 -1.1%22,123 5,630 1.1% 2,835 6.3% 44.9% 938 2014 -0.2%22,089 5,656 0.5% 2,654 -6.4% 44.6% 887 2015 0.3%22,144 5,629 -0.5% 2,696 1.6% 44.9% 888 2016 0.8%22,313 5,654 0.4% 2,715 0.7% 44.9% 941 2017 0.8%22,492 5,713 1.0% 2,742 1.0% 44.9% 907 2018 1.4%22,803 5,795 1.4% 2,817 2.7% 44.9% 925 2019 1.4%23,129 5,883 1.5% 2,825 0.3% 44.9% 948 2020 1.5%23,477 5,963 1.4% 2,894 2.4% 44.8% 1,006 2021 1.0%23,714 6,043 1.3% 2,914 0.7% 44.8% 974 2022 1.0%23,954 6,108 1.1% 2,938 0.8% 44.8% 986 2023 1.1%24,227 6,180 1.2% 2,993 1.9% 44.8% 1,001
Gross and Net Summer Peak Loads
As explained above, the solar net metering and DR are subtracted from the system hourly loads to produce the final hourly load. Integration of the DSM into the EUIs driving the sales models does not mean that the DSM has any less impact than forecasted by the Nevada Power’s DSM Planning department. Nevada Power assumes the incremental DSM impacts equal the forecast. Figure LF-44 is a summary of the gross and net summer peaks, along with the DSM, solar net metering and DR. These are the same values shown in Figure EA-30 of the Supply Plan Narrative, Section 3.G.
FIGURE LF-44: NEVADA POWER SYSTEM GROSS AND NET SUMMER PEAKS (MW), DSM, DR AND SOLAR NET METERING (MW)
FORECAST SCENARIOS E.
Consistent with Commission regulations and prior orders, high and low load forecast scenarios were developed for the 2014 ERCR Forecast. The high and low load forecast scenarios are based on high and low economic growth scenarios developed by Global Insight. High and low hotel/motel load forecasts were produced based on LVCVA and MAE input. Unlike the 2014 ESP Update scenario forecasts, the 2014 ERCR Forecast scenarios do not include high and low market penetration assumptions for DSM, DR, and net metering. The Commission order in Docket No. 13-07002 does not require high and low DSM in non-IRP years. Therefore, Nevada Power did not use the high DSM and net metering reductions in the low sales case and vice-versa. EV was left at the base level for all three forecasts as the base MW and MWh are low enough that scenarios are not warranted. See Technical Appendix Item LF-1 for further details. Figures LF-45 and LF-46 are summaries of the system peaks and energy totals for the low, base and high scenarios. Further discussion of the scenario development is included in Technical Appendix Item LF-1. Year Gross, w/o incremental DSM, DR and Solar Less: incremental DSM & Solar Less: Demand Response Net of incremental DSM, Solar & DR etc. 2014 5,885 32 197 5,656 2015 5,933 66 238 5,629 2016 5,998 101 243 5,654 2017 6,088 132 243 5,713 2018 6,204 166 243 5,795 2019 6,325 199 243 5,883 2020 6,440 234 243 5,963 2021 6,549 263 243 6,043 2022 6,640 289 243 6,108 2023 6,738 315 243 6,180
FIGURE LF-45: SCENARIO COMPARISONS: PEAK DEMAND (MW)
FIGURE LF-46: SCENARIO COMPARISONS: SYSTEM ENERGY (GWH)
DSM/DR USED IN THE FORECASTS VS. FINAL DSM-DR F.
The base load forecast was finalized in November 2013 and the final DSM budget was approved by the Commission in December 2013. The Commission’s final order in Docket No. 13-07002 approved the Solar Thermal program for Nevada Power at the minimum funding level and the Energy Education Program at the maximum funding level (adding a small Home Energy Reports
Year Low Base High Low High Low High
2014 5,563 5,656 5,703 (-) 93 47 (-) 1.6% 0.8% 2015 5,480 5,629 5,727 (-) 149 98 (-) 2.6% 1.7% 2016 5,460 5,654 5,795 (-) 194 141 (-) 3.4% 2.5% 2017 5,470 5,713 5,899 (-) 243 186 (-) 4.3% 3.3% 2018 5,505 5,795 6,021 (-) 290 226 (-) 5.0% 3.9% 2019 5,548 5,883 6,142 (-) 335 259 (-) 5.7% 4.4% 2020 5,592 5,963 6,255 (-) 371 292 (-) 6.2% 4.9% 2021 5,636 6,043 6,363 (-) 407 320 (-) 6.7% 5.3% 2022 5,670 6,108 6,460 (-) 438 352 (-) 7.2% 5.8% 2023 5,710 6,180 6,564 (-) 470 384 (-) 7.6% 6.2%
Difference from Base Peak Megawatts
Year Low Base High Low High Low High
2014 21,717 22,089 22,238 (-) 372 150 (-) 1.7% 0.7% 2015 21,566 22,144 22,468 (-) 579 324 (-) 2.6% 1.5% 2016 21,566 22,313 22,790 (-) 747 477 (-) 3.3% 2.1% 2017 21,574 22,492 23,150 (-) 917 659 (-) 4.1% 2.9% 2018 21,711 22,803 23,607 (-) 1,092 804 (-) 4.8% 3.5% 2019 21,869 23,129 24,061 (-) 1,259 932 (-) 5.4% 4.0% 2020 22,066 23,477 24,535 (-) 1,411 1,058 (-) 6.0% 4.5% 2021 22,167 23,714 24,875 (-) 1,546 1,162 (-) 6.5% 4.9% 2022 22,286 23,954 25,227 (-) 1,668 1,274 (-) 7.0% 5.3% 2023 22,443 24,227 25,620 (-) 1,785 1,393 (-) 7.4% 5.7%
Difference from Base Gigawatthours
program) for a net increase of 9,682 megawatt hours of annual savings in 2014.10 These changes are not material with respect to the forecast.
EXTREME WEATHER TRANSMISSION PEAK FORECAST G.
The extreme weather transmission peak models were not updated for the 2014 ERCR Forecast. The 4.23 percent adder to the base load forecast is identical to the 2012 IRP Forecast. Figure LF-47 is a summary of the base and extreme weather forecasts. Additional discussion on the methodology is contained in Technical Appendix LF-1.
FIGURE LF-47: BASE AND EXTREME TEMPERATURE FORECASTS
10 No estimates of the additional reductions were calculated past 2014 but are expected to be immaterial to the
forecast as both programs are small in terms of savings.
Increase base by: 4.23%
Year Base Extreme Temper- ature 2014 5,656 5,896 2015 5,629 5,867 2016 5,654 5,893 2017 5,713 5,955 2018 5,795 6,040 2019 5,883 6,132 2020 5,963 6,216 2021 6,043 6,299 2022 6,108 6,367 2023 6,180 6,442 Megawatts
SECTION 2. MARKET FUNDAMENTALS
POWER FUNDAMENTALSA.
1. WECC CAPACITY AND ENERGY
Regional Profile. Nevada Power is a member of the Western Electricity Coordinating Council
(“WECC”). “The WECC is the largest and most diverse of the eight Regional Entities that have Delegation Agreements with the North American Electric Reliability Corporation (NERC).”11 At the beginning of 2014, the previous WECC functions of Reliability Coordinator and Interchange Authority in the Western Interconnection became the responsibility of Peak Reliability, a new company providing core and other associated reliability coordination services. The WECC covers most of the Western U.S. including Nevada, the Canadian provinces of Alberta and British Columbia, and the northern portion of Baja California in Mexico. Peak Reliability is headquartered in Vancouver, Washington with an additional office in Loveland, Colorado. Figure MF-1 depicts the various NERC regions, including the WECC.
The WECC is divided into the following four sub-regions which are shown in Figure MF-2, with Nevada Power residing in the Arizona/New Mexico/Southern Nevada (“AZ-NM-SNV”) sub-region:
Arizona-New Mexico-Southern Nevada Area (“AZ-NM-SNV”) California-Mexico Power Area (“CA-MX”)
Northwest Power Pool (“NWPP”) Rocky Mountain Power Area (“RMPA”)
FIGURE MF-2: WECC SUB-REGIONS
The AZ-NM-SNV and the CA-MX sub-regions peak in the summer and the majority of their resources are gas-fired. The NWPP is typically a winter peaking sub-region with a large amount of hydroelectric resources. The peak in the RMPA sub-region can occur in either the summer or the winter, and that sub-region has a significant amount of coal generation.
