Direction and Rate of Technological Change
Dominant design
Because we are discussing generators in the abstract, rather than in a technology-specific manner, the dominant design, if any, is one of business model and gross attributes (see the Utility-Scale PV and Wind value chains for technology-specific discussions [links]). As such, the dominant design for generators in our value chain context appears to be large (10’s-1000’s of MWs) transmission-connected resources that interact primarily with the wholesale market (i.e., merchant generators) or which provide power directly to a vertically-integrated investor-owned or public utility. This model is in contrast to other potential approaches to generation, including large-scale customer-owned resources behind the meter (e.g., industrial cogeneration, utility-scale wind or solar on commercial campuses, etc.), and smaller, distributed generation (e.g., rooftop PV).
Rate of technological change
Many of the changes involved in electricity generation are specific to the generation technology; for example, see the Innovative Outcomes discussions for Utility-Scale Wind and PV, where we discuss ongoing innovations in technologies and processes related to these two example value chains [links]. We recognize that other generation technologies (e.g., combined cycle natural gas-fired power generation) are also continuously evolving, and we aim to address the value chains of additional electricity generation technologies in the future.
The basic practices of owning and operating generation equipment are rather well-established, particularly for traditional coal- and natural gas-fueled facilities. More than a century of work in operations research and efforts to reduce the cost of reliable operations underpin the current state of fossil fuel-based electricity generation. Also critical to this step of the value chain, techniques for managing price risk of both fuel inputs and electricity output are thoroughly studied and tested; significant work has gone into predicting prices, forward markets, hedging strategies, etc. Nevertheless, as the underlying generation technologies evolve and come into competition with new technologies and new generation, wholesale, transmission, and distribution system models, the operational strategies to maximize their value are expected to concurrently evolve. Even within a given technology, improvements in one quality factor might have negative consequences in another. For example, to achieve peak efficiency with the newest technology, it could become necessary to dispatch a combined cycle gas turbine plants more rigidly, diminishing its value for providing short-term capacity services.
The operating environment for generators is also changing. Solar plant operators have to deal with the reality that there may be excess generation during sunny periods of the day, resulting in low prices or curtailment, both of which result in loss of value, either for the owner, the offtaker, or both, depending on their contractual arrangements in place. This was less of a concern among early PV owners, before utility-scale solar began its rapid increase in deployment and utility-scale PV did not appreciable affect daytime prices or supply. In another example, local air quality regulations may restrict the number of annual operating hours, dictate the times of day that a gas plant can run, or specify days when it cannot run (e.g., “spare the air” days). As a result, strategies must be devised to maximize generator value. As the constellations of strengths and weaknesses of the available technologies change, generation operators must continue to adapt.
The art and science of knowing when a new generation asset could be profitable, where to build it, and what technology to build is also constantly evolving. The ability to synthesize many inputs (e.g., geography, governance, technology, fuel, demand, transmission, etc.) into a decision to build a particular generation resource is a key element of expertise for any potential generation owner (see the Project Development and EPC sections of the Utility-Scale PV and Wind value chains for additional information) [links].
Data on Quantity, Cost, and Quality
Generators can be measured by a large array of quality factors, which we group into three broad categories: economic, technical, and environmental.
Economic Quality Factors
Economically, the cost to produce electricity and the price at which electricity can be sold are of paramount importance. For fossil-fueled generators, price is largely, though not entirely, determined by the heat rate of the machine — its efficiency. Heat rates are usually expressed in MMBtu/MWh. The price is therefore dependent on the price of fuel times the heat rate, plus all other costs (e.g., variable O&M, fixed O&M, etc.) For renewable generators, the $/MWh cost is often expressed as a levelized cost of energy (LCOE) which is calculated as the total cost to own and operate a resource over its lifetime divided by the total energy produced over the resources lifetime, both time discounted.
Because generators also sell their capacity, the price for capacity ($/kW) over a specified period, such a year ($/kW-yr), is also important. For resources that sell both capacity and energy, those prices are related to each other, as extra revenue from selling capacity can reduce the revenue a generator owner must charge for energy, or vice versa.
Figure IO.1 On-Shore Wind LCOE, Summary of Published Estimates
Source: https://openei.org/apps/TCDB/
Figure IO.2 Combined-Cycle Natural Gas LCOE, Summary of Published Estimates
Source: https://openei.org/apps/TCDB/
Technical Quality Factors
Generators that operate reliably are more valuable than those that do not. Various measures of availability and reliability have been established to provide measures of comparison Some renewable resources, such as PV and wind, are subject to the weather, and as a result, though the facility itself may function reliably, its output will be intermittent. In such cases, output of the machine is a partially random variable, but there are still statistically meaningful (and measurable) differences between facilities, primarily driven by the quality of the wind or solar resource in their location.
Informally, fbecause such resources can be used to manage constantly varying and unpredictable load. There is no formal generally accepted definition for flexibility, some ISO/RTOs have begun to define it for their own system’s purposes To the degree that certain resources such as wind and solar may add variability to the overall power system, generators that can offset that variability become more valuable. Highly inflexible generators, such as nuclear power plants, on the other hand, though providing relatively cheap base-load power, can be operationally inconvenient in scenarios with high penetration of variable loads and generators.
Some generators can provide “black start” services; they can be started even if the power grid to which they are connected has gone down, while others require system power to start.
In addition to real power, as measured in MW, the power system also needs reactive power, measured in megavolt amperes reactive (VARS), to operate correctly and maintain voltage. The ability to provide VARS is a notable technical consideration. Most traditional fossil-fueled generation technology with real spinning synchronous generators can provide this service, but utility-scale PV generally typically does not. Wind turbines typically usually do not, but wind farms often have special ancillary equipment such as synchronous condensers, static synchronous compensators (STATCOMS) and static VAR compensators (SVCs) to provide reactive power.
Environmental Quality Factors
There are many important environmental factors by which to judge generators. All generators that burn fuel produce emissions and waste streams. These include greenhouse gases (GHG), nitrous oxides (NOx), sulphur oxides (SOx), particulates, and other pollutants. Coal plants also have ash which must be stored and disposed of without contaminating ground water. Nuclear plants will have high-level nuclear waste, which in the US to date is stored entire on-site, either in pools or in dry casks.
Generators that use direct water cooling from lakes, rivers, or the sea will also have environmental impacts. Their intakes may draw in and kill aquatic life, and the hot wastewater may cause thermal pollution. These factors may be measured in terms of volume of water withdrawn for cooling, volume of release, average release temperature, or number of animals killed. Changes in salinity may also occur. See, e.g., Raptis et al. (2016) for a discussion of thermal pollution from the electricity sector globally.
Other environmental quality factors, discussed in greater detail in the Utility-Scale PV and Wind value chains, include:
- Avian impacts: wind turbines and solar projects may kill, injure, or impact the habitat of birds
- Noise: wind turbines generate low frequency sound that may be an environmental hazard
- Glare: large solar projects cause glare, potentially hazardous to wildlife and a hazard to air navigation
Tech Trend Table that would let someone else build a learning curve/experience curve
| Only exists if we have year and one or more of quantity cost quality
Table Properties:
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Table IO.1 LCOE by Generation Source ($/kWh)
Source: OpenEI, http://en.openei.org/apps/TCDB/
| Coal | CCNG | Nuclear | Geothermal | Off-shore Wind | |
| 2009 | 0.0956 | 0.047056 | 0.0872 | 0.0522 | 0.143667 |
| 2010 | 0.0784 | 0.03 | 0.087 | 0.072667 | 0.142667 |
| 2011 | — | — | 0.063 | 0.069167 | 0.1392 |
| 2012 | — | — | — | — | — |
| 2013 | — | 0.066 | — | 0.087125 | 0.174333 |
| 2014 | 0.122 | 0.062 | 0.1095 | — | 0.165 |
Table IO.2 Emissions from Energy Consumption at Conventional Power Plants and Combined-Heat-and-Power Plants 2006 through 2016 (Thousand Metric Tons)
Source: Energy Information Administration, https://www.eia.gov/electricity/annual/
| Year | Carbon Dioxide (CO2) | Sulfur Dioxide (SO2) | Nitrogen Oxides (NOx) |
| 2006 | 2,488,918 | 9,524 | 3,799 |
| 2007 | 2,547,032 | 9,042 | 3,650 |
| 2008 | 2,484,012 | 7,830 | 3,330 |
| 2009 | 2,269,508 | 5,970 | 2,395 |
| 2010 | 2,388,596 | 5,400 | 2,491 |
| 2011 | 2,287,071 | 4,845 | 2,406 |
| 2012 | 2,156,875 | 3,704 | 2,148 |
| 2013 | 2,173,806 | 3,609 | 2,163 |
| 2014 | 2,168,284 | 3,454 | 2,100 |
| 2015 | 2,031,452 | 2,548 | 1,824 |
| 2016 | 1,928,401 | 1,807 | 1,630 |
Table IO.3 Emissions from Conventional Power Plants and CHP Plants by State, 2015 and 2016 (1000 tons)
Source: Energy Information Administration, https://www.eia.gov/electricity/annual/
| Census Division and State |
Carbon Dioxide (CO2) | Sulfur Dioxide (SO2) | Nitrogen Oxides (NOx) | |||
| Year 2016 | Year 2015 | Year 2016 | Year 2015 | Year 2016 | Year 2015 | |
| New England | 29,066 | 31,965 | 12 | 19 | 26 | 31 |
| Connecticut | 8,579 | 9,049 | 1 | 1 | 6 | 7 |
| Maine | 2,557 | 2,956 | 7 | 11 | 6 | 8 |
| Massachusetts | 12,722 | 13,422 | 3 | 5 | 10 | 11 |
| New Hampshire | 2,526 | 3,653 | 1 | 2 | 2 | 3 |
| Rhode Island | 2,670 | 2,874 | 0 | 0 | 1 | 1 |
| Vermont | 12 | 11 | 0 | 0 | 1 | 1 |
| Middle Atlantic | 137,445 | 143,131 | 121 | 226 | 131 | 151 |
| New Jersey | 21,108 | 19,427 | 3 | 3 | 12 | 12 |
| New York | 31,295 | 32,731 | 18 | 22 | 32 | 35 |
| Pennsylvania | 85,041 | 90,973 | 100 | 201 | 87 | 104 |
| East North Central | 338,794 | 369,356 | 432 | 700 | 271 | 310 |
| Illinois | 72,226 | 84,275 | 98 | 139 | 36 | 42 |
| Indiana | 85,393 | 89,045 | 83 | 157 | 89 | 97 |
| Michigan | 58,644 | 67,119 | 92 | 137 | 52 | 61 |
| Ohio | 81,618 | 83,722 | 131 | 214 | 65 | 76 |
| Wisconsin | 40,914 | 45,195 | 28 | 54 | 28 | 34 |
| West North Central | 203,950 | 219,199 | 246 | 304 | 179 | 189 |
| Iowa | 30,216 | 35,043 | 31 | 44 | 26 | 31 |
| Kansas | 25,762 | 27,341 | 6 | 13 | 17 | 19 |
| Minnesota | 29,644 | 30,307 | 24 | 27 | 25 | 28 |
| Missouri | 62,731 | 67,995 | 93 | 114 | 54 | 44 |
| Nebraska | 23,014 | 25,326 | 47 | 59 | 20 | 23 |
| North Dakota | 29,908 | 31,246 | 43 | 43 | 36 | 42 |
| South Dakota | 2,676 | 1,941 | 1 | 4 | 1 | 3 |
| South Atlantic | 379,065 | 378,419 | 275 | 345 | 271 | 301 |
| Delaware | 4,363 | 4,091 | 0 | 1 | 2 | 2 |
| District of Columbia | 48 | 36 | 0 | 0 | 0 | 0 |
| Florida | 110,388 | 111,863 | 59 | 77 | 70 | 76 |
| Georgia | 60,156 | 59,274 | 53 | 67 | 43 | 48 |
| Maryland | 18,578 | 18,314 | 25 | 31 | 13 | 15 |
| North Carolina | 52,492 | 53,824 | 47 | 52 | 48 | 51 |
| South Carolina | 28,001 | 29,849 | 23 | 26 | 15 | 18 |
| Virginia | 36,566 | 34,898 | 27 | 31 | 32 | 34 |
| West Virginia | 68,473 | 66,270 | 42 | 61 | 48 | 57 |
| East South Central | 196,408 | 204,017 | 182 | 351 | 126 | 149 |
| Alabama | 57,776 | 64,442 | 49 | 117 | 35 | 51 |
| Kentucky | 72,433 | 76,427 | 72 | 122 | 55 | 62 |
| Mississippi | 26,272 | 25,171 | 12 | 33 | 15 | 15 |
| Tennessee | 39,927 | 37,977 | 48 | 79 | 22 | 21 |
| West South Central | 358,451 | 369,898 | 395 | 432 | 290 | 301 |
| Arkansas | 31,726 | 28,587 | 54 | 54 | 31 | 29 |
| Louisiana | 53,162 | 56,299 | 58 | 69 | 66 | 71 |
| Oklahoma | 37,106 | 41,626 | 50 | 61 | 27 | 29 |
| Texas | 236,457 | 243,386 | 233 | 247 | 166 | 172 |
| Mountain | 209,056 | 228,381 | 101 | 126 | 204 | 254 |
| Arizona | 44,531 | 50,201 | 12 | 16 | 36 | 43 |
| Colorado | 36,075 | 37,413 | 18 | 22 | 29 | 35 |
| Idaho | 1,829 | 1,866 | 4 | 4 | 5 | 12 |
| Montana | 16,470 | 18,136 | 11 | 13 | 16 | 19 |
| Nevada | 14,542 | 14,752 | 2 | 5 | 10 | 10 |
| New Mexico | 23,193 | 24,850 | 7 | 11 | 35 | 42 |
| Utah | 28,245 | 33,688 | 11 | 16 | 33 | 47 |
| Wyoming | 44,172 | 47,476 | 35 | 40 | 39 | 45 |
| Pacific Contiguous | 65,443 | 76,054 | 21 | 22 | 95 | 102 |
| California | 47,008 | 55,481 | 2 | 1 | 69 | 73 |
| Oregon | 8,207 | 8,987 | 8 | 9 | 12 | 15 |
| Washington | 10,229 | 11,586 | 11 | 12 | 13 | 14 |
| Pacific Noncontiguous | 10,723 | 11,033 | 21 | 23 | 37 | 37 |
| Alaska | 3,466 | 3,676 | 3 | 4 | 21 | 19 |
| Hawaii | 7,257 | 7,356 | 18 | 20 | 16 | 17 |
| U.S. Total | 1,928,401 | 2,031,452 | 1,807 | 2,548 | 1,630 | 1,824 |
Table IO.4 Use of Emission Abatement Technologies over Time (number of systems and associated electric generation capacity)
Source: Energy Information Administration, https://www.eia.gov/electricity/annual/
| Flue Gas Desulfurization Systems | Electrostatic Precipitators | Baghouses | Select Catalytic and Non-Catalytic Reduction Systems | Activated Carbon Injection Systems | Direct Sorbent Injection Systems | |||||||
| Year | Quantity | Associated Net Summer Capacity (MW) | Quantity | Associated Net Summer Capacity (MW) | Quantity | Associated Net Summer Capacity (MW) | Quantity | Associated Net Summer Capacity (MW) | Quantity | Associated Net Summer Capacity (MW) | Quantity | Associated Net Summer Capacity (MW) |
| 2006 | 559 | 117,481 | 1,496 | 317,863 | 539 | 60,641 | 1,160 | 257,367 | 139 | 6,859 | 56 | 7,433 |
| 2007 | 587 | 131,397 | 1,496 | 317,751 | 556 | 65,672 | 1,195 | 266,295 | 141 | 7,735 | 57 | 7,507 |
| 2008 | 634 | 151,417 | 1,471 | 316,810 | 576 | 68,442 | 1,248 | 277,474 | 169 | 17,391 | 60 | 7,606 |
| 2009 | 674 | 174,672 | 1,456 | 314,356 | 597 | 73,863 | 1,321 | 299,905 | 227 | 39,546 | 63 | 8,147 |
| 2010 | 713 | 200,950 | 1,410 | 310,486 | 610 | 83,407 | 1,358 | 315,120 | 262 | 54,183 | 64 | 8,627 |
| 2011 | 727 | 211,652 | 1,367 | 307,035 | 633 | 98,507 | 1,406 | 331,140 | 274 | 59,057 | 73 | 8,883 |
| 2012 | 723 | 219,214 | 1,290 | 298,416 | 629 | 101,593 | 1,449 | 344,709 | 287 | 63,709 | 81 | 10,524 |
| 2013 | 698 | 219,050 | 1,217 | 289,174 | 636 | 104,292 | 1,457 | 351,217 | 260 | 61,160 | 95 | 12,890 |
| 2014 | 696 | 223,556 | 1,171 | 283,932 | 620 | 105,951 | 1,471 | 358,410 | 277 | 68,697 | 102 | 16,683 |
| 2015 | 687 | 223,848 | 1,036 | 264,897 | 622 | 110,780 | 1,478 | 359,767 | 361 | 105,860 | 120 | 23,213 |
| 2016 | 688 | 228,047 | 942 | 252,896 | 605 | 109,956 | 1,477 | 360,785 | 477 | 152,254 | 120 | 26,030 |
Table IO.5 Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities ($mil)
Source: Energy Information Administration, https://www.eia.gov/electricity/generatorcosts/
| Year | Op Rev Electric Utility | Op Exp Electric Utility | Production (fuel, purchased power, etc) | Maintenance |
| 2006 | 246736 | 218445 | 127494 | 12838 |
| 2007 | 240864 | 213076 | 121700 | 13181 |
| 2008 | 266124 | 236572 | 140974 | 14192 |
| 2009 | 249303 | 219544 | 118816 | 14092 |
| 2010 | 260119 | 234173 | 128831 | 14957 |
| 2011 | 255573 | 228873 | 122520 | 15772 |
| 2012 | 249166 | 220722 | 111714 | 15489 |
| 2013 | 257718 | 227483 | 115046 | 15505 |
| 2014 | 271832 | 240643 | 123366 | 16801 |
| 2015 | 260121 | 228366 | 107201 | 16392 |
| 2016 | 261047 | 226457 | 100852 | 16982 |
Table IO.6 Construction Cost of Generators by Census Region
Source: Energy Information Administration, https://www.eia.gov/electricity/generatorcosts/
| Average Construction Cost ($/kilowatt of installed nameplate capacity) by year | |||
| Census Region / Year | 2015 |
2014 |
2013 |
| South | 1,393 | 1,546 | 1,952 |
| West | 2,256 | 2,901 | 1,303 |
| Midwest | 1,350 | 1,671 | 1,878 |
| Northeast | 923 | 1,410 | 3,400 |
Table IO.7 Construction Cost of Generators by State Region
Source: Energy Information Administration, https://www.eia.gov/electricity/generatorcosts/
| State | Average Construction Cost ($/kilowatt of installed nameplate capacity) | |
| TX | 1,299 | |
| NJ | 695 | |
| CA | 3,087 | |
| OK | 1,611 | |
| CO | 1,286 |
Figure IO.3 Unsubsidized LCOE Comparison
Source: Lazard’s Levelized Cost of Energy Report Version 11.0, https://www.lazard.com/perspective/levelized-cost-of-energy-2017/