BEFORE THE PUBLIC UTILITIES COMMISSION OF NEVADA

Transcription

1 BEFORE THE PUBLIC UTILITIES COMMISSION OF NEVADA Application of Nevada Power Company d/b/a NV Energy Seeking Acceptance of the First Amendment to its integrated resource plan and its Energy Supply Plan Update for 2015, which include an emissions reduction and capacity replacement plan filed pursuant to NRS et seq. Docket No 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 Supply Side Plan, Transmission Plan, Economic Analysis, And Financial Plan REDACTED 2 128

2 NARRATIVE LOAD FORECAST, MARKET FUNDAMENTALS, FUEL AND PURCHASE POWER PRICE FORECASTS & DEMAND SIDE PROGRAMS Page 2 of 327

3 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 A. B. C. D. E. F. G. FORECAST SUMMARY... 6 LOAD FORECAST METHODOLOGY COMPARISON WITH THE 2014 ESP UPDATE FORECAST FORECAST MODEL DEVELOPMENT Forecast Drivers Forecast Models Customer Class Sales and Customer Forecasts System Energy, Sales and Peak Forecasts...47 FORECAST SCENARIOS DSM/DR USED IN THE FORECASTS VS. FINAL DSM-DR EXTREME WEATHER TRANSMISSION PEAK FORECAST SECTION 2. MARKET FUNDAMENTALS...52 A. B. C. POWER FUNDAMENTALS WECC Capacity and Energy Future Generation Additions Renewable Energy in the Western States Challenges in Meeting RPS Targets Resource Adequacy Regional Power Price Trends Future Price Drivers...64 NATURAL GAS FUNDAMENTALS Demand Supply Transport and Storage Price Trends...95 COAL FUNDAMENTALS Page 3 of 327

4 1. Supply U.S. Coal and Consumption Coal Transportaion SECTION 3. FUEL AND PURCHASED POWER PRICE FORECASTS A. B. C. D. E. F. G. H. BASE GAS PRICE FORECAST BASE MARKET IMPLIED HEAT RATE FORECAST BASE POWER PRICE FORECAST HIGH AND LOW GAS PRICE FORECASTS HIGH AND LOW POWER PRICES CAPACITY PRICE FORECAST FOR MARKET PURCHASES COAL PRICE FORECAST FORECASTS & MODELING OF POTENTIAL CARBON COSTS SECTION 4. DEMAND SIDE PLAN A. ENERGY EFFICIENCY AND DEMAND RESPONSE Page 4 of 327

5 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 Figure LF-5: Nevada Power System Peak Forecasts Figure LF-6: Nevada Power System Energy Forecasts Figure LF-7: Population Growth Rate Forecast Comparison Figure LF-8: Real Household Income Growth Rate Forecast Comparison Figure LF-9: Real Gross Metro Product ( GMP ) Growth Rate Forecasts Figure LF-10: Non-Manufacturing Employment Growth Rate Forecasts Figure LF-11: Historical Class Sales Trends Figure LF-12: Residential Customers (2003 to 2013) Figure LF-13: Weather Normalized Residential Average Use (2003 to 2013) Figure LF-14: Small C&I Customers (2003 to 2013) Figure LF-15: Small C&I Average Use (2003 to 2013) Figure LF-16: Large C&I Sales (2003 to 2013) Figure LF-17: Average Annual Hotel/Motel Rooms (2003 to 2013) Figure LF-18: Clark County Population (2003 to 2023) Figure LF-19: Real per Household Income (2003 to 2023) Figure LF-20: Employment (2003 to 2023) Figure LF-21: Real Output (2003 to 2023) Figure LF-22: Prices (2003 to 2023) Figure LF-23: Total Residential Energy Intensity (2003 to 2013) Figure LF-24: Total Commercial Energy Intensity (2003 to 2023) Figure LF-25: Annual Calendar CDD (Las Vegas, 2003 to 2023) Figure LF-26: Annual Calendar HDD (Las Vegas, 2003 to 2023) Figure LF-27: Annual DSM Savings Estimates (2005 to 2023) Figure LF-28: Residential Average Use Model Results Figure LF-29: Residential Customer Forecast Model Figure LF-30: Small C&I Average Use Model Figure LF-31: Small C&I Customer Forecast Model Figure LF-32: Large C&I Sales Forecast Model Figure LF-33: Public Authority Sales History and Forecast (MWh) Figure LF-37: Indexed Monthly Large C&I Sales, Real GMP, Hotel/Motel Rooms Employment and the Large C&I Economic Variable Figure MF-1: NERC Regions Figure MF-2: WECC Sub-Regions Figure MF-3: WECC Capacity by Fuel Type (2013) Figure MF-4: 2013 Generation by Fuel Type (U.S. WECC) Page 5 of 327

6 Figure MF-5: U.S. Natural Gas Consuption for Electric Generation Figure MF-6: WECC Generation Under Construction (MW) Figure MF-7: WECC Natural Gas-Fired Under Construction (MW) Figure MF-8: Renewable Standards in Western States Figure MF-9: 2013 WECC Power Supply Assessment (Summer) Figure MF-10: Historic Power Prices at Mead Figure MF-11: Historic Natural Gas Prices Figure MF-12: Historical Monthly Market Implied Heat Rates at Mead Figure MF-13: Historical Monthly Spark Spreads at Mead Figure MF-14: Projection of Import Reliance of Major Economies Figure MF-15: U.S. Dry Gas Consumption (trillion cubic feet per year) Figure MF-16: U.S. Energy Demand by Fuel Figure MF-17: U.S. Energy Demand By Sector Figure MF-18: Proved Natural Gas Reserves Figure MF-19: North American Shale Gas Plays and Formations Figure MF-20: World Shale Gas Resources Figure MF-21: U.S. Natural Gas Supply Figure MF-22: U.S. Natural Gas Production (trillion cubic feet per year) Figure MF-23: U.S. and Canadian Gas and Oil Rig Count Figure MF-24: U.S. and Canadian Dry Gas Production Figure MF-25: Western Gas Production ( ) Figure MF-26: U.S. LNG Imports Figure MF-27: Global Natural Gas Prices Figure MF-28: U.S. LNG Exports Figure MF-29: Proposed/Potential LNG Terminals Figure MF-30: U.S. Pipeline Imports from Canada Figure MF-31: Weekly Gas Storage for US (Lower 48) Figure MF-32: Weekly Gas Storage for West Region Figure MF-33: Western Underground Natural Gas Storage Figure MF-34: Oil Prices vs Gas Figure MF-35: Historical Natural Gas Prices Figure MF-36: Operating Coal Mines in Central Utah Figure MF-37: Operating Coal Mines in Western Colorado Figure MF-38: Operating Coal Mines in the Powder River Basin Figure PF-1: Annual Average Gas Price Forecast [REDACTED] Figure PF-2: Average Market Implied Heat Rate Forecast [REDACTED] Figure PF-3: Average Annual Power Price Forecast Mead [REDACTED] Figure PF-4: Gas Price Volatility for the High and Low Forecasts Figure PF-5: Base, High and Low Gas Price Forecast Socal [REDACTED] Figure PF-6: Base, High and Low Power Price Forecast - Mead (On-peak) [REDACTED] Page 6 of 327

7 Figure PF-7: Nevada Power Projected Coal Prices [REDACTED] Figure PF-8: Carbon Allowance Price for mid-carbon Scenario Figure PF-9: Natural Gas Price Adjustments for mid-carbon Scenario (Henry Hub) Figure PF-10: Examples of Modeling Power Price Impacts from Carbon Figure PF-11: Example Carbon Costs to Power Prices Figure PF-12: Purchased Power Price Increases due to Carbon (mid-carbon Case) [REDACTED] Figure DSM-1: Nevada Power Approved 2015 Targets (dollars) Figure DSM-2: Nevada Power Approved 2015 DSM Targets (kwh) Page 7 of 327

8 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 The 2014 ERCR Forecast was completed in November 2013 and incorporates actual sales and load data through August Nevada Power seeks approval of the 2014 ERCR Forecast for purposes of performing long-term integrated resource planning. A. FORECAST SUMMARY 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 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 Unlike sales, customer counts continued to modestly increase even during the economic downturn that started in Residential customer counts were up 1.5 percent in 2012 and 1.2 percent in 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 longterm projections of customer usage (measured in 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 A full discussion of customer usage is contained in Technical Appendix LF-1. 1 The Nevada Test Site represented a sales loss of approximately 65 GWh from 2012 to Page 8 of 327

9 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 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 and are the average annual growth rate ( AAGR ) across those time periods. The beginning annual growth rate for is the rate for 2003 to 2004, and the ending annual growth rate is 2012 to For , the beginning is 2013 to 2014 and the end is 2022 to All AAGRs in this document are calculated using this method. For example, an AAGR from in a figure or table begins with the growth rate from 2003 to 2004 and ends with the growth rate for 2006 to 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 ,456 3,743 6, , , % 3, % 6, % % % 18, % , % 4, % 6, % % % 20, % , % 4, % 7, % % % 20, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 20, % , % 4, % 7, % % % 20, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 20, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 21, % , % 4, % 7, % % % 22, % , % 4, % 7, % % % 22, % , % 4, % 8, % % % 22, % , % 5, % 8, % % % 22, % , % 5, % 8, % % % 23, % % 1.6% 1.7% 0.1% -14.2% 1.6% % 1.6% 0.9% -0.4% -0.3% 1.0% 7 Page 9 of 327

10 The growth rate periods are interpreted as follows: The historical period includes annual growth rates beginning with the growth from 2003 to 2004 through 2012 to The forecast period includes annual growth rates beginning with growth from 2013 to 2014 through 2022 to 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) ,187 12,299 78,794 47, , % 12, % 83, % 47, % , % 12, % 87, % 48, % , % 12, % 92, % 47, % , % 12, % 96, % 47, % , % 12, % 99, % 46, % , % 12, % 99, % 44, % , % 11, % 100, % 43, % , % 11, % 100, % 43, % , % 11, % 101, % 43, % , % 11, % 102, % 42, % , % 11, % 104, % 42, % , % 11, % 105, % 42, % , % 11, % 107, % 42, % , % 11, % 109, % 42, % , % 11, % 111, % 42, % , % 11, % 113, % 41, % , % 11, % 115, % 41, % , % 11, % 117, % 42, % , % 11, % 120, % 42, % , % 11, % 122, % 42, % % -0.5% 2.7% -1.1% % -0.2% 1.7% -0.1% 8 Page 10 of 327

11 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 FIGURE LF-3: HISTORICAL (WEATHER NORMALIZED) AND FORECASTED ENERGY AND DEMAND Demand (MW) Year Energy (GWh) chg After DR chg ,299 4, , % 4, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 5, % , % 6, % , % 6, % , % 6, % % 1.7% % 0.9% 9 Page 11 of 327

12 B. LOAD FORECAST METHODOLOGY 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. FIGURE LF-4: FORECAST PROCESS 10 Page 12 of 327

13 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 ). C. COMPARISON WITH THE 2014 ESP UPDATE FORECAST This section describes the changes in the forecast between this ERCR Forecast and the Nevada Power 2014 ESP Update forecast filed in 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 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 11 Page 13 of 327

14 from the Gaming Control Board regarding its application for a gaming license before commencing construction 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 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 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 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 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 The CBER June 2013 short-term forecast projected population growth of 1.5 percent for 2013 and 2.0 percent for 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 The 2014 ESP Update forecast assumed population growth rates of 1.5 percent for 2014 and 1.8 percent for 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, See Technical Appendix LF-3 for the publication from the June 2013 CBER economic outlook and Technical Appendix LF-2 for the July 2013 long term population forecast publication. Also included as Appendix LF-4 for reference purposes is the December 2013 CBER economic outlook released after the 2014 ESP Forecast was finalized. 12 Page 14 of 327

15 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 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 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, 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, The 2014 ERCR Forecast also includes the projected 13,706 MWh for the City of Henderson premises expected to take DOS service in 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 The entire use was embedded in the historical data by the end of Page 15 of 327

16 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 6,400 NVE South Peak (MW) 6,200 6,000 5,800 5,600 5,400 Recorded WN Nov 13 Fcst Jan 13 Fcst 5,200 5, FIGURE LF-6: NEVADA POWER SYSTEM ENERGY FORECASTS 25,000 24,500 24,000 23,500 23,000 22,500 22,000 21,500 21,000 20,500 20,000 NVE South System Energy (GWH) Recorded WN Nov 13 Fcst Jan 13 Fcst 14 Page 16 of 327

17 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 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 shortterm and 2012 CBER long-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 FIGURE LF-7: POPULATION GROWTH RATE FORECAST COMPARISON 3.0% Clark County Annual Population Growth 2.5% 2.0% 1.5% 1.0% Nov-13 Jan % 0.0% Page 17 of 327

18 FIGURE LF-8: REAL HOUSEHOLD INCOME GROWTH RATE FORECAST COMPARISON Clark County Real Household Income Growth 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% -0.5% -1.0% -1.5% Nov-13 Jan FIGURE LF-9: REAL GROSS METRO PRODUCT ( GMP ) GROWTH RATE FORECASTS Clark County Real GMP Growth 4.5% 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% Nov-13 Jan % 0.0% -0.5% Page 18 of 327

19 FIGURE LF-10: NON-MANUFACTURING EMPLOYMENT GROWTH RATE FORECASTS 3.5% Clark County Non-Manufacturing Employment Growth and Forecast % G r o w t h 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% Nov-13 Jan % Additional comparisons are contained in Technical Appendix LF-1. D. FORECAST MODEL DEVELOPMENT 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 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 Small C&I and large C&I class models are estimated with monthly data from January 2003 through August Page 19 of 327

20 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 Nevada Power saw the largest decline in sales right after the start of the recession in late Weather normalized sales fell 1.7 percent in 2009, 0.4 percent in 2010 and 0.3 percent in Small C&I sales fell 3.4 percent in 2008 and 2.0 percent in Residential sales growth slowed as well, falling 0.7 percent in 2009 and 0.2 percent in 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 10,000 9,000 8,000 7,000 6,000 Res GWh 5,000 4,000 3,000 2,000 1,000 0 AAGR 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% Small C&I Large C&I Other 5 However, if we use 2008 as the base year for the analysis, large C&I sales have declined an average of 0.5% per year. 18 Page 20 of 327

21 Residential. The recession had a significant impact on customer growth. Residential customer growth leveled off significantly starting in Customer annual growth averaged just 0.6 percent between 2007 and 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 Figure LF-12 illustrates the customer trend. FIGURE LF-12: RESIDENTIAL CUSTOMERS (2003 TO 2013) 800, , ,000 AAGR % % 650, , , , , , Page 21 of 327

22 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) 14,000 13,000 12,000 kwh per customer 11,000 10,000 9,000 8,000 7,000 6,000 5,000 AAGR % % Page 22 of 327

23 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 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) 110, ,000 AAGR % % 90,000 80,000 70,000 60,000 50,000 40, 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 Page 23 of 327

24 FIGURE LF-15: SMALL C&I AVERAGE USE (2003 TO 2013) kwh per customer 49,000 48,000 47,000 46,000 45,000 44,000 43,000 42,000 41,000 40,000 39,000 AAGR % % 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 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) Thousands 8,000 7,500 7,000 M W h 6,500 6,000 5,500 5,000 4,500 4,000 AAGR % % Page 24 of 327

25 Hotel/Motel Rooms. Hotel/motel room growth averaged 1.2 percent growth from 2003 through 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 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) 155,000 Number 150, , , , ,000 AAGR % % 125, , , Page 25 of 327

26 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 ( ). 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 Between 2007 and 2013, population growth averaged just 0.6 percent. Population growth does appear to be improving with long-term population forecast of 1.3 percent annual average growth; while positive, this is significantly slower than population growth experienced before As 24 Page 26 of 327

27 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 While steady improvement in household income is expected, forecasted Real Household Income does not reach the 2007 level until 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 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. 25 Page 27 of 327

28 FIGURE LF-20: EMPLOYMENT (2003 TO 2023) FIGURE LF-21: REAL OUTPUT (2003 TO 2023) 26 Page 28 of 327

29 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 27 Page 29 of 327

30 appliance saturation surveys in late 2008 and again in the spring of Commercial end-use intensities were adjusted to reflect Nevada Power s commercial customer mix based on market surveys conducted in December 2008 and January 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 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-23: TOTAL RESIDENTIAL ENERGY INTENSITY (2003 TO 2013) 6 See Technical Appendix Item LF-3 for the 2011 Residential Appliance Saturation Survey Report. 28 Page 30 of 327

31 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 Figures LF-25 and LF-26 show actual and forecasted calendar CDD and HDD. 29 Page 31 of 327

32 FIGURE LF-25: ANNUAL CALENDAR CDD (LAS VEGAS, 2003 TO 2023) Degree Days 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, FIGURE LF-26: ANNUAL CALENDAR HDD (LAS VEGAS, 2003 TO 2023) 2,500 2,000 Degree Days 1,500 1, Page 32 of 327

33 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) Mwh 350, , , , , ,000 50,000 Res NRes On a cumulative basis, DSM programs have reduced sales by nearly 1.7 million MWh since 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 end-use. For the residential sector, the targeted enduses 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 31 Page 33 of 327

34 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: Residential--RES Small Commercial and Industrial-- small C&I 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 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: AvgUse a b XHeat b XCool b XOther m 1 m 2 m 3 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 Figure LF-28 shows actual and predicted results. m m 7 As 2013 street lighting sales finished slightly higher than expected at the time of the forecast, the actual sales decline is 0.7% from 2013 through Page 34 of 327

35 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 R 2 is 0.99 and the mean absolute percentage error ( MAPE ) is 3.5 percent. 33 Page 35 of 327

36 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 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 R 2 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 Page 36 of 327

37 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 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 R 2 of 0.98 and a MAPE of 2.1 percent. 35 Page 37 of 327

38 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 Page 38 of 327

39 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 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 The large C&I model Adjusted R 2 is 0.94 and the average absolute monthly error is 2.8 percent. 37 Page 39 of 327

40 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) 300, , , , ,000 50, 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 Page 40 of 327