Report#:DOE/EIA-0554(99)

Introduction

Macroeconomic Activity

International Energy

Household Expenditure

Residential Demand

Commercial Demand

Industrial Demand

Transportation Demand

Electricity Market

Oil and Gas Supply

Natural Gas Transmission & Distribution

Petroleum Market

Coal Market

Renewable Fuels

The NEMS Renewable Fuels Module (RFM) consists of five distinct submodules that represent the major renewable energy technologies.  Although it is described here, conventional hydroelectric is included in the Electricity Market Module (EMM) and is not part of the RFM.  Similarly, ethanol modeling is included in the Petroleum Market Module (PMM).  Some renewables, such as municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not require the production or consumption of a fuel.  Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using wind, solar, and geothermal energy.  In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittence, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

The submodules of the RFM interact only with modules outside of the RFM and not with other RFM submodules.  These interactions occur through common elements of the model with the Electricity Market Module (EMM).  Because of the high level of integration with the EMM, the final outputs (levels of consumption and market penetration over time) for renewable energy technologies are largely dependent upon the EMM.  

The EMM represents learning effects for new technologies, which are implemented as a decrease in capital costs as a function of the level of market penetration.  For AEO99, learning effects in the EMM occur in three phases, with capital costs declining most rapidly (usually 10 percent) for every doubling of capacity from the 1st through the 5th unit, less rapidly from the 5th through the 40th unit (usually 5 percent per doubling), and at a much slower rate thereafter per each doubling (2.5 percent).  The RFM provides the 5th (nth) unit costs.  In addition, unit size is provided to the EMM for renewable technologies, so that the level of market penetration can be determined.  

For AEO99, two increasing costs  are superimposed onto the capital costs of renewable energy technologies to represent two phenomena:

• Short-term cost adjustment factors, which  increase technology capital costs as a result of rapid U.S.  buildup in a single year and reflecting limitations on the infrastructure to accommodate unexpected demand growth.   These short-term factors are invoked when demand for new capacity in any year exceeds 25 percent of the prior year’s total U.S. capacity.  For every 1 percent increase in total U.S. capacity over the previous year greater than 25 percent, capital costs rise 0.5 percent.  These factors apply to biomass, geothermal, municipal solid waste, solar, and wind technologies.

• For biomass and wind only, increased costs resulting from a large cumulative increase in use of a given resource, reflecting any or all of three general factors: (1)  resource degradation, (2) transmission network upgrades, and (3 ) market factors.  Presumably best land resources are used first.  Increasing resource use necessitates resort to less efficient land - less accessible, less productive, more difficult to use (e.g, land roughness, slope, terrain variability, or productivity,  wind turbulence or wind variability). Second, as capacity increases, especially for intermittent technologies like wind power, existing local and long-distance transmission networks require upgrading, increasing overall costs.  Third, market pressures from competing land uses increase costs as cumulative capacity increases, including for agricultural or other production alternatives, residential or recreational use, aesthetics, or from broader environmental preferences.  As a result, for AEO99 , each EMM region’s biomass and wind resource estimates are parceled into five cost levels. For biomass, the increases that are applied to initial capital cost are 0, 15, 50, 75 and 100 percent for successive increments of the resource. For wind, the increases are 0, 20, 50, 100 and 200 percent respectively. The size of the resource increments vary by technology and region.

For an in-depth discussion of the learning functions, see the EMM section and the background section of the model summary for the Geothermal Electric Submodule.  A detailed description of the RFM is provided in the EIA publication, Model Documentation: Renewable Fuels Module of the National Energy Modeling System, DOE/EIA-M069.

Key Assumptions

Nonelectric Renewable Energy Uses

In addition to projections for renewable energy used in electricity generation, the AEO99 contains projections of nonelectric renewable energy uses for industrial and residential wood consumption, solar residential and commercial hot water heating, and residential and commercial geothermal (ground-source) heat pumps. Additional renewable energy applications, such as direct solar thermal industrial applications or direct lighting, off-grid electricity generation, and heat from geothermal resources used directly (e.g., district heating and greenhouses), are not included in the projections.

Electric Power Generation

The RFM specifically and EMM in general consider only grid-connected electricity generation.  Off-grid sources, such as off-grid applications of photovoltaic, dish-Stirling solar, and wind generation, are not included in the energy projections for the AEO99.  The renewable submodules that interact with the EMM are the grid-connected solar (thermal and photovoltaic), wind, geothermal, biomass, and MSW submodules.  Most provide specific data that characterize that resource in a useful manner.  In addition, a set of technology cost and performance values is provided directly to the EMM.  These data are central to the build and dispatch decisions of the EMM with the exception of MSW.  The values are presented in Table 37 of the EMM section.

Conventional Hydroelectricity

Background

The Hydroelectric Power Data File in the EMM represents reported plans for new conventional hydroelectric power capacity connected to the transmission grid reported on Form EIA-860, Annual Electric Generator Report, and Form EIA-867, Annual Nonutility Power Producer Report.  It does not estimate additional unplanned capacity, nor estimate pumped storage hydroelectric capacity, which is considered a storage medium for coal and nuclear power and not a renewable energy use.  Hydroelectric power is not competed against any other generating technologies for capacity expansion, and all the hydropower generated is assumed to be consumed.  Data maintained for hydropower include the available capacity, capacity factors, and costs (capital, and fixed and variable operating and maintenance).  The fossil-fuel heat rate equivalents for hydropower are provided to the report writer for energy consumption calculation purposes only.  

Assumption

• Because of hydroelectric power’s position in the merit order of generation, it is assumed that all available installed hydroelectric capacity will be used within the constraints of available water supply and general operating requirements (including environmental regulations).

Solar Electric Submodule

Background

The Solar Electric Submodule (SOLES) currently includes two solar technologies:  100 megawatt central receiver (power tower) solar thermal (ST) and 5 megawatt fixed-flat plate thin-film copper-indium-diselenide (CIS) photovoltaic (PV) technologies.  PV is assumed available in all thirteen EMM regions, while ST is available only in the six primarily Western regions where direct normal solar insolation is sufficient.  Capital costs for both technologies are determined by EIA using multiple sources, including technology characterizations by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and the Electric Power Research Institute (EPRI)84. Most other cost and performance characteristics for ST are obtained or derived from the August 6, 1993, California Energy Commission memorandum, Technology Characterization for ER 94; and, for PV, from the Electric Power Research Institute, Technical Assessment Guide (TAG) 1993.   In addition, capacity factors are obtained from information provided by the National Renewable Energy Laboratory (NREL); limits to learning are determined by EIA.

Assumptions

• Capacity factors for solar technologies are assumed to vary by time of day and season of year, such that nine separate capacity factors are provided for each modeled region, three for time of day, and for each of three broad seasonal groups (summer, winter, and spring/fall).  The current solar thermal annual capacity factor for the region including California, for example, is assumed to average 40 percent; California’s current PVcapacity factor is assumed to average 24.6 percent.

• In order to incorporate assumed improvements in photovoltaic technologies, all PV capacity factors are assumed to improve linearly a total of 10 percent from 2005 through 2015; for example, California’s annual average capacity factor for PV increases from 24.6 percent to almost 27.1 percent by 2015.

• Because solar technologies are more expensive than other utility grid-connected technologies, early penetration will be driven by broader economic decisions such as the desire to become familiar with a new technology or environmental considerations. Early year’s penetration for such reasons is included as supplemental additions.

• Solar resources are well in excess of conceivable demand for new capacity; therefore, energy supplies are considered unlimited within regions (at specified daily, seasonal, and regional capacity factors).  Accordingly, there is no reason to track solar resources in NEMS. In the seven regions where ST technology is not modeled, the level of direct, normal insolation (the kind needed for that technology) is insufficient to make that technology commercially viable through 2020.

• NEMS models the Energy Policy Act of 1992 (EPACT) 10-percent investment tax credit for solar electric power generation by tax-paying entities. Because it does not distinguish publicly-owned from privately-owned facilities, and  EIA assumes that most new capacity will be privately-owned, the model does not include EPACT’s 1.5 cent renewable energy production incentive for publicly owned new solar capacity.

Wind-Electric Power Submodule

Background

Because of limits to windy land area, wind is considered a finite resource, so the submodule calculates maximum available capacity by Electricity Market Module Supply Regions.  The minimum economically viable wind speed is about 13 mph, and wind speeds are categorized into three wind classes according to annual average wind speed.  The RFM keeps track of wind capacity (megawatts) within a region and moves to the next best wind class when one category is exhausted.  Wind resource data on the amount and quality of wind per EMM region come from a Pacific Northwest Laboratory study and a subsequent update.85  The technological performance, cost, and other wind data used in NEMS are derived by EIA from consultation with industry experts.86

Maximum wind capacity, capacity factors, and incentives are provided to the EMM for capacity planning and dispatch decisions.  These form the basis on which the EMM decides how much power generation capacity is available from wind energy.  The fossil-fuel heat rate equivalents for wind are provided to the report writer for energy consumption calculation purposes only.  

Assumptions

• Only grid-connected (utility and nonutility) generation is included.  The forecasts do not include off-grid electric generation.

• In the wind submodule, wind supply is constrained by three modeling measures, addressing (1) average wind speed, (2) distance from existing transmission lines, and (3) resource degradation, transmission network upgrade costs, and market factors.

• First availability of wind power (among three wind classes) is based on the Pacific Northwest Laboratory Environmental and Moderate Land-Use Exclusions Scenario, in which some of the windy land area is not available for siting of wind turbines.  The percent of total windy land unavailable under this scenario consists of all environmentally protected lands (such as parks and wilderness areas), all urban lands, all wetlands, 50 percent of forest lands, 30 percent of agricultural lands, and 10 percent of range and barren lands.

• Wind resources are mapped by distance from existing transmission capacity among three distance categories, accepting wind resources within (1) 0-5, (2) 5-10, and (3) 10-20 miles on either side of the transmission lines.  Transmission cost factors are added to the resources further from the transmission lines.

• For AEO99, capital costs for wind technologies are also assumed to increase in response to (1) declining quality of land or wind resources other than average annual wind speed, such as terrain slope, terrain roughness, terrain accessibility, wind turbulence,  wind variability, or other natural resource factors, (2) increasing cost of upgrading existing local and network distribution and transmission lines to accommodate growing quantities of intermittent wind power, and (3) market conditions, the  increasing costs of alternative land uses, including for aesthetic or environmental reasons.  Capital costs are left  unchanged for some initial share, then increased 20, 50, 100 percent, and finally 200 percent, to represent the aggregation of these factors.  Proportions in each category vary by EMM region.

• Depending on the EMM region, the cost of competing fuels and other factors, wind plants can be built to meet system capacity requirements or as “fuel savers” to displace generation from existing  capacity. For wind to penetrate as a fuel saver, its total capital and fixed operations and maintenance costs minus applicable subsidies from the EPACT, must be less than the variable operating and fuel costs for existing (non-wind) capacity.

• Because of downwind turbulence and other aerodynamic effects, the model assumes an average spacing between turbine rows of 5 rotor diameters and a lateral spacing between turbines of 10 rotor diameters. This spacing requirement determines the amount of power that can be generated from windy land area and is factored into requests for generating capacity by the EMM.

• It is expected that wind turbine technology will improve in performance and that blade lengths will increase, as the cubic relationship between the area swept by the rotor and power generation provides a large incentive for increasing blade length.  Capacity factors are assumed to increase to a national average of about 34 percent in the best wind class.  However, as better wind resources are depleted, capacity factors go down.

Geothermal-Electric Power Submodule

Background

In developing geothermal capacity growth projections, the focus is on hydrothermal resources; extraction of energy from hot dry rock resources is not included.  This is because the technology probably will at best be available after 2010, and reliable cost and resource data are not available. The Geothermal-Electric Power Submodule (GES) utilizes a process of resource accounting based on Sandia National Laboratory’s 1991 geothermal resource assessment.87  Site-specific costs, including those for drilling, steam collection, and electricity transmission to the grid, as well as site characteristics, are used in identifying available resources and capacities by EMM region.  The cost and performance values are based on dual flash and binary cycle technologies.  The costs from 51 sites are aggregated into a set of regional supply curves for each year.  For each iteration of a model run, a value for avoided cost is obtained from the Electricity Capacity Planning Submodule to establish the levelized cost at which to truncate the curves, thereby excluding the higher cost resources.  Capital cost learning on the generating units which emulates what is done in the EMM is incorporated in the GES88. For AEO99 , the capacity factor has been set at 87 percent, based on historical data.

Assumptions

• Existing and planned capacity data are accessed directly by the EMM.  The data are obtained from Forms EIA-860 and EIA-867.

• An investment tax credit of 10 percent is assumed to be available in all forecast years based on the EPACT.

• Plant retirements are generally assumed to occur 30 years after startup.  An exception is made for wells affected by a project to bring water to parts of The Geysers site which is expected to halt the enthalpy decline occurring there.  Of these (six) wells, half are assumed to be retired after 35 years, the others in 40 years.

• Capital and operating costs vary by site and year; values shown in Table 37 are indicative of those used by EMM for geothermal build and dispatch decisions.

Biomass Electric Power Submodule

Background

Biomass consumed for electricity generation is modeled in two parts in NEMS.  Capacity in the wood products and paper industries, the so-called captive capacity, is included in the industrial sector module as cogeneration.  Generation by the electricity sector is represented in the EMM, with capital and operating costs and capacity factors as shown in Table 37, as well as fuel costs, being passed to the EMM where it competes with other sources.  Fuel costs are provided in sets of regional supply schedules. Projections for ethanol are produced by the Petroleum Market Module (PMM), with the quantities of biomass consumed for ethanol decremented from, and prices obtained from, these same supply schedules.

Assumptions

• Existing and planned capacity data are accessed directly by the EMM.  The data are obtained from Forms EIA-860 and EIA-867.

• The conversion technology represented, upon which the costs in Table 37 are based, is an advanced gasification-combined cycle plant that is similar to a coal-fired gasifier.  Costs in the reference case were developed by EIA to be consistent with coal gasifier costs. Co-firing with coal is a distinct possibility, but it would not add capacity and is not included in the reference case but is allowed in the renewable portfolio standard case.

• In place of the previously used capacity constraints, short-term and long-term cost adjustment factors have been installed.  These values are described in the RFM introduction.  

• Fuel supply schedules are a composite of four fuel types; forestry materials, wood residues, agricultural residues and energy crops. The first three are combined into a single supply schedule for each region which does not change for the full forecast period. Energy crops data are presented in yearly schedules from 2010 to 2020 in combination with the other material types for each region. The forestry materials component is made up of logging residues, rough rotten salvable dead wood and excess small pole trees.89 The wood residue component consists of primary mill residues, silvicultural trimmings and urban wood such as pallets, construction waste and demolition debris that are not otherwise used.  90 Agricultural residues are wheat straw and corn stover only, which make up the great majority of crop residues.91 Energy crops data is for hybrid poplar, willow and switchgrass grown on three land types. Costs range from zero to over five dollars per million Btu. 92 The maximum amount of resources in each supply category is shown in Table 64.

Table 64. U.S. biomass Resources, by Region and Type, 2020 (Trillion Btu)

Municipal Solid Waste-Electric Power Submodule

Background

Municipal solid waste (MSW) combustion is treated within NEMS as a separate technology whose electricity production is determined exogenous to the EMM.  The cost of producing electricity is passed to the EMM only as an input to the calculation that derives the average cost of producing electricity.  Energy from MSW is a byproduct of waste disposal activity and, therefore, does not compete against other technologies in model decisions regarding new capacity additions.93

Assumptions

• MSW is assumed to displace other energy forms lower in the merit order.

• Build decisions are based on a stepwise process involving waste disposal parameters.

• Gross domestic product (GDP) and population are used as the drivers in an econometric equation that establishes the supply of MSW.

• The values are extrapolated from historical Environmental Protection Agency (EPA) values for MSW and multiplied by 1.47 to reflect a broader definition of materials known to be combusted.  The factor 1.47 is derived from information in the Biocycle State Survey.94

• The heat content of the MSW is set at 5,190 Btu per pound, and is assumed to remain at that level throughout the projection.

• The percentage of waste combusted is assumed to remain constant at 11 percent of a growing waste stream.  Using the Biocycle-based value for generation of the MSW waste stream, the percentage currently combusted is reduced from the EPA value of 17 percent95 to 12 percent.

• The total energy from MSW projected for the United States is limited to the portion currently used for electricity generation (about 92 percent) and is disaggregated into regions.  This regional breakdown is performed by maintaining the projected 1996 distribution of these factors as represented in the EIA database of MSW plants.  Steam from MSW is represented in industrial cogeneration.

• An estimate is made of energy produced from landfill gas.  Data values are entered in a spreadsheet that considers existing and additional landfills and a profile of gas generation from the waste. It is assumed that the percent of the gas emitted that will be captured for energy conversion will increase from 13 percent in 1995 to 40 percent in 2020, based on an EPA estimate of a tripling of landfills that will capture the gas. The resulting generating capacity is added to the capacity for MSW combustion, is disaggregated by region and passed to the EMM.

Legislation

Energy Policy Act of 1992 (EPACT)

The RFM includes the investment tax and energy production credits called for in the EPACT for the appropriate energy types.  EPACT provides a renewable electricity production credit of 1.5 cents per kilowatt-hour for electricity produced by wind, applied to plants that become operational between January 1, 1994, and December 31, 1999.  The credit extends for 10 years after the date of initial operation.  EPACT also includes provisions that allow an investment tax credit of 10 percent for solar and geothermal technologies that generate electric power.  This credit is represented as a 10-percent reduction in the capital costs in the RFM.

Supplemental Capacity Additions

In addition to the reported generating capacity plans from the EIA-860 and EIA-867 and capacity projected through the use of the EMM/RFM, the AEO99 also includes 2,897 megawatts additional generating capacity powered by renewable resources.  Summarized in Table 65, some of the capacity represents mandated new capacity required by state laws, EIA estimates for expected new capacity under recent state-enacted renewable portfolio standards (RPS), estimates of winning bids in California’s renewables funding program (Assembly Bill 1890), expected new capacity under known voluntary programs, such as “green marketing” efforts, and other reported plans; finally, EIA includes minimal “floor” estimates for solar photovoltaic and solar thermal capacity assumed to be built for reasons not represented in the RFM, such as for testing, investment, learning, or for use in niche markets.  Table 65 details the planned additions.

Table 65. Post-1996 Supplemental Capacity Additions (Megawatts, Net Summer Capability)

International Learning

For AEO99, capital costs for all new electricity generating technologies decrease in response to foreign as well as domestic experience (Table 66) .  For estimate of international learning in the EMM (see Table 41).  In the EMM, international learning effects are limited to the equivalent of one unit of a new technology per year; as a result, both wind and solar photovoltaic effects are limited to 50 megawatts and 5 megawatts per year, respectively, despite much greater actual additions observed and expected over the forecast period.

Table 66. Current and Planned U.S. Generating Capacity, New Technologies, as of August 21, 1998

The electricity generating capacity effects on AEO99 of Action Item 26 are incorporated in EIA’s projections for renewable technologies. The supplemental capacity additions include additions that will be cost-shared by DOE and industry.  While the stated goal of this action item is “increased utility and investor experience and confidence” in renewable technologies, in general, no additional cost declines beyond those discussed above are assumed.