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Electricity Sector Module

This Stochastic Energy Deployment System (SEDS) module is a single-region representation of the U.S. electric power system. The module competes a collection of generating technologies based on their perceived costs to expand the stock of generating capacity, so the national electric load and electrical energy consumption is met.

Fuel prices and electricity consumption from other modules are fed into the electric sector, which then calculates sector-specific electricity prices.

Focus of Analyses

The effects of a national renewable portfolio standard (RPS), a carbon tax, pro-nuclear policies, investment tax credits, production tax credits, and generator cost and performance improvements due to government-sponsored R&D are the most significant policies that can be analyzed with the electricity sector module. These policies can be simulated individually or together to evaluate their effects on electricity price, new generator capacity additions, and carbon dioxide emissions and fuel consumption caused by electricity generation.

Limitations of Analyses

The regional effects of such policies cannot be evaluated because the electric sector is based on a national region. With only a single region, SEDS cannot model transmission and distribution. Marginal or time-of-day pricing cannot be evaluated because the module is typically simulated with one-year resolution. The effects of repeatedly extending a production tax credit for short periods of time rather than extending the credit once for a longer duration of time cannot be investigated largely because this module does not have foresight and cannot take into account the actions of generator manufacturers, independent power producers, utilities, regulators, etc.

Technologies of Interest to U.S. Department of Energy

The technologies of potential interest to the U.S. Department of Energy in the electric sector include:

  • Integrated gasification combined cycle (IGCC)
  • IGCC with sequestration
  • Combined cycle (CC) with sequestration
  • New nuclear
  • Biomass
  • Biomass with sequestration
  • Geothermal (hydrothermal)
  • Enhanced geothermal system (EGS)
  • Wind
  • Concentrating solar power (CSP)
  • Photovoltaic (PV) generators.

Overview of Methodology

The module tracks the capacity and characteristics of 20 different generator technologies throughout the simulation. Of the 20 technologies, six are not allowed to compete for new capacity, but their existing capacity is used in load dispatch. Table 1 lists the 20 generator technologies and groups the technologies by their ability to compete for new capacity.

Table 1: Technologies Tracked in the Electric Sector by Ability to Compete for New Capacity
Able to compete
for new capacity
Not able to compete
for new capacity
  • Coal – new, scrubbed
  • IGCC
  • IGCC with sequestration
  • CC (adv oil/gas)
  • CC with sequestration
  • Nuclear – new
  • Biomass
  • Biomass with sequestration
  • Geothermal (hydrothermal)
  • EGS
  • Wind
  • CSP
  • PV
  • Combustion turbines
  • Coal – old, unscrubbed
  • Coal – old, scrubbed
  • Other fossil steam
  • Nuclear – old
  • Hydro
  • Municipal solid waste – landfill gas

 

Capital costs, fixed and variable operations and maintenance (O&M) costs, emissions costs, fuel costs, construction costs, tax incentives, and the expected utilization rates are used to calculate the levelized cost of energy for each technology. Capital costs can be decreased by R&D and learning, or they can be increased by limits in resource availability.

The effects of R&D are treated with uncertainty and can be adjusted to try to capture the level of government investment in R&D. Improvements from learning are based on cumulative installed capacity, such that a specified percent improvement in capital costs is achieved for each doubling of capacity.

Wind and geothermal technologies are subject to resource availability, so supply curves are used to capture the increase in capital costs caused by development of these technologies. Sulfur dioxide emissions costs are only applied to coal technologies, but carbon dioxide (CO2) emissions costs are applied to all fossil fuel technologies when a carbon tax is imposed.

Production tax credits, investment tax credits, and accelerated depreciation can be applied to appropriate technologies and will lead to lower levelized costs of energy. Heat rates and capacity factors can also be improved by R&D, where improvements are specified by probability distributions. The combination of all these factors produces a levelized cost of energy that is used to determine how the market share of new capacity additions will be given to the competing technologies.

These technologies compete to meet the demand for electric power. Actual demand for power is determined by the end-use sectors within SEDS. The total demand is broken into four dispatch periods: base, intermediate, peak-intermediate, and peak. The dispatch periods dictate which technologies can compete for new capacity.

The amount of energy that will need to be met by this new capacity is the difference between projected load (i.e., not the actual new load) and the generation that can be produced by generators surviving (i.e., not retiring) from the previous time period. The projected load for the period is determined by extrapolating a growth trend from previous periods.

The first technologies to compete for the new generation needed are the renewable electric technologies with variable resources, i.e., wind and central photovoltaics. These two technologies compete against a quantity-weighted average levelized cost of energy (LCOE) of the dispatchable technologies using a logit algorithm. The contributions from wind and PV are subtracted from the generation required from new capacity, and the remaining amount is competed for by the dispatchable technologies.

For each dispatch period, two logits are used to calculate the market share of each of the competing dispatchable technologies. The first logit computes an unconstrained market share based purely on the technologies' levelized costs of energy. The resulting unconstrained capacity additions for each technology is then compared to the average of the additions from the past three periods.

If the unconstrained additions are greater than the average additions, then a damping factor is applied. The damping factor is a function of the ratio of unconstrained additions to average additions, so as the ratio increases so too does the damping. This damping factor is intended to prevent rapid and unrealistic growth of technologies, especially nascent technologies. The second logit computes a damped market share using a utility for each technology that has been multiplied by its corresponding damping factor. It is this second logit that determines the actual share of new capacity additions for each technology.

The damped market share is then multiplied by the projected amount of new energy demand to assign a share of new load to each dispatchable technology in each dispatch period. These values are converted to a capacity value using each technology's respective capacity factor. Finally, these new capacity additions are added to the existing stock of capacity minus that time period's capacity retirements. The resulting stock of capacity is compared to the predicted peak demand plus a 15% reserve margin. If the total stock capacity is less than this reserve peak, then combustion turbines are added to meet the difference.

Once the stock of capacity has been changed to reflect additions, retirements, and reserve margin, market shares for actual dispatch of the stock are calculated for each dispatch period using the variable cost of generation (i.e. variable operating and fuel costs) in a capacity-constrained logit. Fuel use is then calculated based on the amount of electricity generated from each technology and the corresponding heat rate. CO2 emissions are then calculated based on the amount of fuel combusted and the carbon content of each fuel.

Electricity prices are calculated from a generator rate base and operation costs. The cost of new additions is added to the rate base, and the entire rate base is depreciated as the model moves forward in time. The rate base multiplied by the rate of return, O&M costs, and fuel costs are summed together and divided by the total demand to give a cost of production per kilowatt-hour. The average electricity price is then calculated as the sum of this production cost and transmission, distribution, and general administration costs. Multipliers derived from historic data are used to produce sector-specific prices from the average electricity price.

Major Assumptions

Capital costs (before learning, R&D, financing, construction, and resource quality factors are considered) and performance characteristics for new generating technologies are based on the Annual Energy Outlook's (AEO's) assumptions. The capacity and generation from the existing stock of generators is based on AEO, Energy Information Administration (EIA), Annual Energy Review (AER), and U.S. Environmental Protection Agency data.

Electricity pricing is based on the methods of regulated markets, where investments in new generators are added to a rate base and divided by the total load. Variable, transmission, and distribution costs are added to this rate per kilowatt-hour to calculate the average price of electricity. Residential, commercial, and industrial electrical prices are determined by multiplying the average electricity price by 1.18, 1.12, and .72, respectively. These price multipliers were derived from historical data reported in the AER.

Transmission and distribution costs are based on the AEO's results. The scaling (alpha) and damping (beta) parameters for the market share logit use generic values, which have not been calculated using regression methods. The percentage of a technology's capacity that is assigned to the three dispatch periods is drawn from reasonable estimates, as is the utilization rate of the capacity in each period. A reserve margin of 15% of the peak demand is met by combustion turbines alone. The peak demand is calculated by multiplying the average hourly demand by 1.67, which is derived from historical data reported in the AER. The wind supply curve was generated by NREL's WinDS model. The geothermal supply curves are from the EIA.

Portfolio Decision Support Inputs

Portfolio decision support (PDS) inputs have been received for the capital cost and performance of wind, CSP, PV, and geothermal generators.

Stochastic Inputs

The electricity sector has numerous stochastic inputs that relate to one of three categories:

  • Policy
  • Generator capital costs
  • Generator performance.

Policy inputs relate to production tax credits, renewable portfolio standards, and pro-nuclear action. Capital cost inputs effect the timing and magnitude of improvements in capital costs due to government-sponsored R&D. Performance inputs effect the timing and magnitude of improvements in heat rates and capacity factors due to government-sponsored R&D.

Table 2 lists all of the stochastic inputs by category. Stochastic fuel prices are received by the electricity sector module from the fuel-supply modules of SEDS.

Table 2. Stochastic Inputs by Category
Policy Inputs Capital Cost Inputs Performance Inputs
  • Nuclear power?
  • Wind production tax credit (PTC) expires
  • Geothermal PTC expires
  • Yucca mountain happens
  • Yucca mountain year
  • RPS percentage
  • RPS start year
  • RPS phase in period years
  • RPS happens
  • Wind learning rate
  • IGCC learning rate
  • Adv CC learning rate
  • Adv CC Seq learning rate
  • EGS learning rate
  • Coal cap cost time to achieve reduction
  • Coal cap cost % reduction
  • IGCC cap cost time to achieve reduction
  • IGCC cap cost % reduction
  • CC cap cost time to achieve reduction
  • CC cap cost % reduction
  • Nuclear cap cost time to achieve reduction
  • Nuclear cap cost % reduction
  • Biomass cap cost time to achieve reduction
  • Biomass cap cost % reduction
  • Geothermal cap cost time to achieve reduction
  • Geothermal cap cost % reduction
  • Wind cap cost time to achieve reduction
  • Wind cap cost % reduction
  • CSP cap cost time to achieve reduction
  • CSP cap cost % reduction
  • PV cap cost time to achieve reduction
  • PV cap cost % reduction
  • EGS cap cost time to achieve reduction
  • EGS cap cost % reduction
  • IGCC w/seq cap cost time to achieve reduction
  • IGCC w/seq cap cost % reduction
  • CC w/Seq cap cost time to achieve reduction
  • CC w/Seq cap cost % reduction
  • Biomass w/Seq cap cost time to achieve reduction
  • Biomass w/Seq cap cost % reduction
  • Coal heat rate time to achieve reduction
  • Coal heat rate % reduction
  • IGCC heat rate time to achieve reduction
  • IGCC heat rate % reduction
  • CC heat rate time to achieve reduction
  • CC heat rate % reduction
  • Nuclear heatrate time to achieve reduction
  • Nuclear heatrate % reduction
  • Biomass heatrate time to achieve reduction
  • Biomass heatrate % reduction
  • Geothermal heatrate time to achieve reduction
  • Geothermal heatrate % reduction
  • Wind capacity factor time to achieve improvement
  • Wind capacity factor % improvement
  • CSP capacity factor time to achieve improvement
  • CSP capacity factor % improvement
  • PV capacity factor time to achieve improvement
  • PV capacity factor % improvement
  • EGS heat rate time to achieve reduction
  • EGS heat rate % reduction
  • IGCC w/seq heat rate time to achieve reduction
  • IGCC w/seq heat rate % reduction
  • CC w/Seq heatrate time to achieve reduction
  • CC w/Seq heatrate % reduction
  • Biomass w/Seq heatrate time to achieve reduction
  • Biomass w/Seq heatrate % reduction

 

Key Inputs from Other Modules

  • The price of biomass fuel, coal, natural gas, and heavy fuel oil
  • The total demand for electricity
  • Interest rates
  • Carbon tax

Key Outputs to Other Modules

  • Sector-specific electricity prices (residential, commercial, industrial, wholesale)
  • Demand for biomass, coal, natural gas, heavy fuel oil, and nuclear fuel
  • Cost of investments in new plants
  • CO2 emissions and sequestration
  • CO2 content of electricity (g / MJ)

Attachments

SEDS Electric Sector - May 2009

Electric Sector Market Share Logit Description - May 2009

Authors

James Milford, National Renewable Energy Laboratory

Walter Short, National Renewable Energy Laboratory