INL/EXT-18-51434

Light Water Reactor Sustainability Program

A Risk-Informed Analysis Approach for the U.S. Electricity Market

September 2018

U.S. Department of Energy

Office of Nuclear Energy

DISCLAIMER This information was prepared as an account of work sponsored by

an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness, of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.

INL/EXT-18-51434

A Risk-Informed Analysis Approach for the U.S. Electricity Market

Hongqiang Hu, Daniel Wendt, Hongbin Zhang, Ronaldo Szilard

September 2018

Prepared for the U.S. Department of Energy Office of Nuclear Energy

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EXECUTIVE SUMMARY

The United States commercial nuclear power industry has demonstrated a substantial history of safe operation and serves as a vital element that ensures the stability of the nation’s electricity grids. Nuclear energy is the nation’s largest contributor of non-greenhouse-gas-emitting electric power generation, comprising over 60% of the non-emitting sources. It provides reliable, base-load capacity at historically high availability rates while supporting national greenhouse gas emission goals. However, some plants in the existing U.S. nuclear fleet have experienced profitability challenges from wholesale power price suppression, which results in early retirement or planned-closure of many nuclear plants, frequently in advance of their license expiration dates. This situation is the result of a confluence of several factors, including the U.S. market and private ownership approach to the electricity sector, low electricity market prices, and no compensation to nuclear power plants for public benefits including zero-emission electricity, fuel diversity, and grid reliability. In order to address this issue, the Risk-Informed Systems Analysis (RISA) Pathway of the DOE Light Water Reactor Sustainability (LWRS) Program initiated research to develop analytical capabilities to explore potential revenue streams for nuclear fleets by collaborating with various industrial/academic partners. The focus of this report is to present a strategic plan for next three years to identify model and simulation methodology associated with industry on both technical and economic aspects, including benefits to risk, safety, operations, and business and cost.

The long term objectives of this project is to use RISA tools to assess, characterize, and quantify predicted market revenue gaps for the existing nuclear fleet (by site, region, and grid integration), analyze electricity market attributes, and analyze stress case conditions by developing attribute-based revenue models and assessing ways to monetize the attributes of nuclear (and other) energy systems. The potentially increased revenues could improve the status of NPPs and to provide secure and clean electricity for customers.

In addition, this project also aims to develop and apply a framework that enables DOE and electricity sector stakeholders to compare the system value of grid services provided by generation technologies necessary for reliable and resilient grid operations, on a more comprehensive and consistent “apples-to-apples” basis than previously possible using only the levelized cost of energy (LCOE) metric, and to reflect other potentially high impact grid services in the system value of specific technologies, that are not otherwise currently valued in organized markets (e.g., inertia, voltage support). This integrated evaluation approach will provide insights for the energy industry and policymakers about challenges, safety and economic improvements of nuclear power plants. Specifically, five tasks are proposed and scheduled as following:

Task 1: Study Defining: This task works closely with other four tasks over the next three years by setting up project regions, system characteristics, tools to be applied and industrial partnerships for each task. This task begins with a literature review for each of the other four tasks and will encompass related prior and ongoing efforts in DOE, the academic community, and industry. There is no

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Task 2: Resilience Study and Market Analysis: This task will quantify and define weather-based resilience for nuclear power plants; study the impacts of interdependence of fuel supply infrastructures, and evaluate fuel availability and price stability under high impact, low frequency weather events and how it changes the fuel mix with respect to percentage nuclear. The task will include defining baseline cases, and analyzing stress test cases using risk-informed tools and methods. Resilience describes a broad array of low-probability but high-impact risks at all stages of the production, transmission and distribution of electricity. One resilience risk that requires further study is fuel security. Fuel security focuses on the vulnerability of fuel supply and delivery to generators and the risks inherent in increased dependence on a single fuel-delivery system. Task 2 will perform targeted analyses to quantify the resilience of a regional grid in response to specific scenarios designed to explore identified fuel security risks, and quantify the extent to which the presence of light water reactors mitigates these fuel security risks. A milestone report is scheduled on September 2019. Specific objectives for Task 2 include: Establish metrics for quantifying grid resilience in relation to extreme weather events that

impact fuel delivery to critical generators. Quantify the impacts on grid resilience from varying the percentage of the generation

portfolio comprised of light water reactors. Analyze regional market and economic impacts of fuel diversity.

Task 3: Examining Social and Environmental Benefits of NPPs: In this task, a suite of models on air-quality and health impact and social cost of carbon will be applied to evaluate economic benefits of nuclear power plants on avoided emission and avoided damage through the following sub-tasks. A milestone report is scheduled on March 2020. A suite of reduced-form air-quality models will be applied to estimate the public health

benefits of reduced pollutant emissions. The range of estimates presented is driven both by uncertainty in the underlying processes and also by differences in model characteristics.

A statistical model will be used to find the SO2, NOx, PM2.5 and CO2 emissions that are avoided due to nuclear generation. This set of emissions contributes to an important portion of total external costs associated with electricity production.

A range of monetary climate benefits based on social cost of carbon (SCC) estimates will be presented.

Finally, reasons that benefits differ between regions and over time will be investigated. Task 4: System Value of Beyond LCOE: Levelized cost of electricity (LCOE) is a commonly used metric in the energy community. While easy to understand and a useful means of comparison within one type of generation or storage technology over time, this metric does not consider other grid services beyond energy that are necessary for the operation of a reliable and resilient power system. As such, there is a need for more comprehensive metrics, which consider energy as well as these additional grid services, which can be used to directly compare technologies.

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The objective of this task is to develop and apply a framework that enables DOE and electricity sector stakeholders to: Compare the system value of grid services provided by generation and storage

technologies necessary for reliable and resilient grid operations, on a more comprehensive and consistent “apples-to-apples” basis than previously possible using only the levelized cost of energy (LCOE) metric.

Reflect other potentially high impact grid services in the system value of specific technologies, which are not otherwise currently valued in organized markets (e.g., inertia, voltage support).

The work plan for task is composed of the following sub-tasks that will be conducted concurrently and a milestone report is scheduled on March 2021: Task 4.1 Conceptual Framework - Develop a conceptual framework that will establish a

taxonomy of technology characteristics and grid services for valuation, a technology-neutral economic framework and associated guiding principles that establish the generalized methodology for system value calculations, and an assessment frame that provides a consistent basis on which calculations can be made through definition of both a set of system value metrics and a range of future electricity scenarios/regions/reference grids.

Task 4.2 System Value Calculation – Initially implement the conceptual framework through its application to available wholesale market price data and existing planning/operation models to estimate initial system value metrics for a range of scenarios/regions/reference grids, focusing on grid services valued today in markets (e.g., energy, capacity, transmission, some ancillary services).

Task 4.3 Missing/ Supplemental Components - Leverage ongoing DOE and industry work to identify approximation methods and bounded monetary estimates of the value of non-market grid services (those service not currently valued) and, for a high priority subset of these services, develop and apply new quantitative valuation methods to establish improved estimates of system value metrics.

Task 4.4 Technology Characterization for Grid Services Provision - Work with the DOE Technology Offices to ensure that the conceptual framework and its initial application in tools are informed by ongoing office-funded work and office perspectives, apply the taxonomy component of the conceptual framework to characterize the specific technologies of interest to the offices, including assessing the current and potential future costs of these technologies for providing specific grid services, and use the resulting characterizations and the initial estimates of system value metrics for these technologies to help inform RD&D planning.

Task 5: Study on Expanded Resilience and Inertia: This task consists of two sub-tasks. The fist sub-task will develop method to quantify impacts/risks from cyber-, physical-, and weather-based events. The framework developed to quantify impacts/risks from cyber-, physical-, and weather-based events will be benchmarked/calibrated using data from actual events, and subsequently used to quantify the value of the resilience provide by light water reactors. The analysis will be performed using either the regional or national large area electric grid models

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developed in the previous tasks, selected to be consistent with the characteristics of the cyber-, physical-, or weather-based events considered in the analysis.

The second sub-task is to develop a systematic method to quantify inertia from nuclear generators from a thermal and rotational perspective and represent these aspects using high-fidelity digital representation in the simulations. A reference test system based on actual system data is to be developed to accurately represent the physics of the thermal plant, and a real-time model that is representative of a large area electric grid will be used to understand the behavior of the integrated system to quantify the value of inertia provided under various realistic conditions of operation. The scope of this phase is to formulate a systematic approach and demonstrate through preliminary simulations to quantify inertia in a representative power system through some practical scenarios that can trigger the requirement of fast inertial response in the electric grid for sub-second and seconds to minute time-scales.

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CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... iii

1. Introduction and Project Objective ................................................................................. 1 1.1 Nuclear Power Plants and Electricity Wholesale Market .......................................................1 1.2 Factors Contributing to Economic Challenges of Nuclear Power Plants ..................................3 1.3 LCOE Market Approach .......................................................................................................6 1.4 Impacts of Inertia ................................................................................................................7 1.5 Project Objective .................................................................................................................8

2. Market Economics R&D .................................................................................................. 9 2.1 Technical approach .............................................................................................................9

2.1.1 Risk-Informed Systems Analysis (RISA) ............................................................................. 9 2.1.2 Resilience and Reliability Attributes .................................................................................. 10 2.1.3 System Value ...................................................................................................................... 13 2.1.4 Rotational and Thermal Inertia ........................................................................................... 14

2.2 Partnerships and Industrial Collaborations ......................................................................... 17 2.3 Description of Market Models ........................................................................................... 17

2.3.1 AVERT ............................................................................................................................... 17 2.3.2 EASIUR .............................................................................................................................. 18 2.3.3 RAVEN ............................................................................................................................... 18 2.3.4 ReEDS ................................................................................................................................. 18 2.3.5 Weather Based Resilience Optimization Tool .................................................................... 18 2.3.6 US-REGEN ............................................................................................................................ 19

3. Project Plan and Schedule ............................................................................................. 20 3.1 Task 1: Define study regions, characteristics, tools and industrial partnerships ................... 20 3.2 Task 2: Complete a regional-level weather-based resilience study and analyze market and economic impacts of fuel diversity ................................................................................................ 21

3.2.1 Sub-task 2.1: Development of metrics and extreme weather based scenarios for evaluating a regional grid resilience ..................................................................................................................... 22 3.2.2 Sub-task 2.2: Model and evaluate grid resilience as function of light water reactor deployment in defined extreme weather scenarios ............................................................................. 22 3.2.3 Sub-task 2.3: Analyze regional market and economic impacts .......................................... 23

3.3 Task 3: Evaluate the climate and air-quality benefits of nuclear power plants at an ISO/RTO with significant nuclear power presence ....................................................................................... 24

3.3.1 Sub-task 3.1: Evaluation of Avoided Emission .................................................................... 24 3.3.2 Sub-task 3.2: Evaluation of air-quality benefits .................................................................. 25 3.3.3 Sub-task 3.3: Evaluation of GHG emission reductions ........................................................ 25 3.3.4 Sub-task 3.4: Investigation of reasons that benefits differ between regions and time ..... 25

3.4 Task 4: Develop a risk-informed framework for optimizing grid system values .................... 25 3.5 Task 5: Expand resilience study to include cyber-, physical-, and weather-based events, various stress scenarios, and thermal and rotational inertia. ......................................................... 32

3.5.1 Expanded Resilience ........................................................................................................... 32 3.5.2 Inertia .................................................................................................................................. 32

4. Outcomes and Deliverables .......................................................................................... 33

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5. REFERENCES ................................................................................................................. 35

FIGURES

Figure 1. National Overview of Electric Power Markets in U.S. by Regions ................................................. 2

Figure 2. U.S. Wholesale Power Prices (Source: Bloomberg 2018) .............................................................. 4

Figure 3. Electricity Market Attributes and Challenges (Courtesy: NEI 2018) ............................................ 10

Figure 4. Illustration of the primary components/phases of resilience (Flynn 2008) ................................ 10

Figure 5. Consolidated range of factors that affect reliability, resilience, and vulnerability (Mann et al., 2018) ................................................................................................................................................. 12

Figure 6. Relationship between technology characteristics, grid services, and system attributes ............ 13

Figure 7. Diagram describing an ideal thermodynamic cycle in a steam power plant ............................... 16

Figure 8. Conceptual framework development .......................................................................................... 27

Figure 9. Steps to system value metrics ..................................................................................................... 29

TABLES

Table 1. Schedule of R&D tasks and milestones ......................................................................................... 20

Table 2. Project activities for Task 4 ........................................................................................................... 31

Table 3. Milestone and deliverables for projects over the next three years .............................................. 34

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1. Introduction and Project Objective

1.1 Nuclear Power Plants and Electricity Wholesale Market

Electric power is a vital component of the nation’s economy and is essential to continuing improvements in the quality of life. Currently, almost 70% of domestic electricity generation relies on fossil fuels in the United States (DOE 2015). With a total of 98 operating commercial nuclear power plants (NPPs) as of September 2018 (NRC 2018), nuclear energy is the nation’s largest contributor of non-greenhouse-gas-emitting electric power generation, comprising over 60% of the non-emitting sources (INL 2018). Nuclear energy is an important contributor to meeting national electricity generation objectives. It provides reliable, base-load capacity at historically high availability rates while supporting national greenhouse gas emission goals. The United States commercial nuclear power industry has demonstrated a substantial history of safe operation and serves as a vital element that ensures the stability of the nation’s electricity grids. Nuclear energy is an essential part of the nation’s long-term future energy mix, beyond just its ability to reduce greenhouse-gas emissions, for the following reasons (Szilard et al., 2017; Davis and Hausman 2015): Fuel source diversity: an appropriate balance of more than one type of energy resource within

the electricity supply system is prudent to mitigate short-term scarcity and price volatility.

Electric supply reliability: an electrical power supply in the U.S. must be stable in time and space and have an adequate capacity margin. The very high capacity factor for nuclear power makes it the most reliable, non-carbon dioxide-emitting source of baseload power available.

Environmental sustainability: this includes minimal free release emissions, no carbon emissions, small environmental footprints, minimal solid waste, and sustainable water use.

National Security: in order to have a major role in setting international standards of safeguards, physical security, and safety, the U.S. must be a major player in domestic nuclear energy to influence the directions taken worldwide.

In addition, nuclear power plants have long asset lifetimes and require highly skilled workforces, which make nuclear plants drivers of economic growth and job creation in their local economies, both during construction and operation (EPRI 2018). As the U.S. power system has continued to experience significant weather events leading to interruptions of electricity supply, nuclear plants have played an important role in preserving power supply and providing a foundation for grid recovery (CRS 2016). However, some plants in the existing U.S. nuclear fleet have experienced profitability challenges from wholesale power price suppression, which results in early retirement or planned-closure of many nuclear plants, frequently in advance of their license expiration dates. For instance, the Oyster Creek Nuclear Generation Station in New Jersey, America’s oldest nuclear power plant, was shut down On Sept 17, 2018 after over 49 years of operation, which reduces the number of operating NPPs to 98. The U.S. Wholesale electricity market are mainly operated by entities know as regional transmission organizations (RTOs) and Independent System Operators (ISOs) (Figure 1) (FERC 2018) or ISO/RTO Council (IRC), including CAISO, ERCOT, ISONE, MISO, NYISO, PJM,

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SPP(APPA 2017). For regions that are not members of ISOs/RTOs such as Northwest, Southwest and Southeast, the sales are conducted bilaterally, through direct contact and negotiation, through a voice broker, or through an electronic brokerage platform, such as the Intercontinental Exchange (ICE) (APPA 2017). These ISO/RTO markets provide for the wholesale sale of electric energy (both day-ahead and real-time purchases), as well as ancillary services. Some ISO/RTOs also operate capacity markets. The ISOs and RTOs use bid-based markets to determine economic dispatch to engage in regional planning to make sure the needs of the system are met with the appropriate infrastructure. Each of the ISOs and RTOs have energy and ancillary services markets in which buyers and sellers could bid for or offer generation.

Figure 1. National Overview of Electric Power Markets in U.S. by Regions

The ISO/RTO markets are authorized to administer wholesale markets for the sale of electric energy (both day-ahead and real-time purchases), electric power capacity, and ancillary services. ISO/RTOs do not own the power plants that generate the power bought and sold in the market, but do exercise extraordinary controls over power generation (DOE 2015; Hibbard et al., 2017). They decide which generators will run and at what levels, grant (or deny) the transmission services needed for transactions to occur, and run the billing systems for payments for power. Prices paid in these markets are not set through bilateral exchanges, but are centrally determined by the ISO/RTO. Two specific TROs, ISO-NE and PJM, are also highlighted in Figure 1. ISO-NE is the RTO for New England and is responsible for operating wholesale power markets that trade electricity, capacity, transmission congestion contracts and related products, in addition to administering auctions for the sale of capacity. ISO-NE operates New England’s high-voltage transmission network and performs long-term planning for the New England system for serving six New

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England states: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont. ISO-NE relies primarily on natural gas-fired and nuclear generation, accounting for 49% and 31% of the systems supply in 2016, respectively. There are four nuclear power units in ISO-NE (Figure 1). The region is facing numerous challenges from growing reliance on natural gas without a corresponding increase in natural gas pipeline capacity, retirements of nuclear and coal plants, and rising energy and capacity prices. Since the end of 2013, the ISO has implemented a Winter Reliability Program involving direct payments to oil and dual-fuel generators to increase oil inventories, for natural gas-fired generators to contract for liquefied natural gas, and for new demand-response resources. ISO-NE operates a mandatory capacity market, called the forward capacity market (FCM), which procures capacity three years in advance. PJM became a fully functioning ISO in 1996 and, in 1997, it introduced markets with bid-based pricing and locational market pricing (LMP). PJM was designated an RTO in 2001. The Company currently coordinates a pooled generating capacity of more than 178,563 megawatts and operates wholesale electricity markets with approximately 1,032 members. PJM enables the delivery of electric power to more than 65 million people in all or parts of 13 states, including Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia and the District of Columbia. PJM’s markets include energy (day-ahead and real-time), capacity and ancillary services. PJM has a variety of different resource types in its diverse resource mix, which shifted throughout PJM’s history. In 2005, coal and nuclear resources generated 91 percent of the electricity on the PJM system. Over time, policy initiatives, technology improvements, and economics spurred a shift from coal to natural gas and renewable generation. From 2010 to 2016 in PJM, coal-fired units made up 79 percent of the megawatts retired, and natural gas and renewables made up 87 percent of new megawatts placed in service (PJM 2018a). PJM’s installed capacity in 2018 consists of 31 percent coal, 38 percent natural gas, and 19 percent nuclear and others (PJM 2018b). And this trend will continue to occur. Oyster Creek Nuclear Generation Station in New Jersey was shut down On Sept 17, 2018. And Ohio power company FirstEnergy said its competitive arm would close four uneconomic nuclear units in PJM with generation capacity of 4 GW by year-end 2021 (Proctor 2018). Currently there are 33 nuclear power units in PJM (PJM 2016).

1.2 Factors Contributing to Economic Challenges of Nuclear Power Plants

The nuclear power plants facing the threat of early retirement are mostly those operating in states with a restructured electricity industry and organized electricity markets, but some nuclear power plants that are regulated and or owned by public power utilities are also at risk.

This situation is the result of a confluence of several factors (Szilard et al, 2016, 2017): the U.S. market and private ownership approach to the electricity sector; low electricity market prices resulting from low natural gas prices, low demand growth, increased penetration of renewable generation, and negative electricity market prices; and no compensation to nuclear power plants for public benefits including zero-emission electricity, fuel diversity, and grid reliability.

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For some countries (e.g., France and China), the electricity industry is owned and controlled by a national government, who can consider the full set of costs and benefits in making investment and other decisions. However, the U.S. electricity industry is primarily owned by private companies with a small portion of government-owned local or regional public power companies. The U.S. market economy has separated a set of private costs and benefits (e.g., those included in corporate net earnings) from public costs and benefits and usually takes few positions in the market outcomes. This U.S. approach means that nuclear power is an activity with most costs and benefits assigned to private or public power owners. Thus, nuclear power plant owners typically do not have the perspective that a national owner might have and may decide that a nuclear power plant’s private financial outcomes are not sufficient to warrant continued operation, even if early retirement would result in a net loss public benefits. The U.S. electricity industry has a focus on efficient interstate markets as a matter of policy, with an explicit goal of achieving lower market prices. The implicit emphasis on electricity and capacity as short-term commodities in these markets makes it difficult or impossible for any generator participant to have a long-term view of plant value or to focus on externality benefits such as zero carbon emissions that are not reflected in market prices. The focus on efficient electricity markets results in low electricity market prices. These lower electricity market prices directly impact merchant nuclear power plant operating cash flow. These lower prices also depress the price of power purchased by regulated and public power utilities, lowering the relative value of regulated and public power nuclear plants. The market-focused approach to the electricity industry in the U.S. means that the economics of nuclear power plants are linked to commodity electricity and capacity prices. When these prices are low, nuclear power plant operating cash flows are low or negative. Wholesale electricity prices in the U.S., both in organized regional transmission organization (RTO) markets and in regions that remain regulated, are very low as a result of several factors (Figure 2), including low natural gas prices, low demand growth, increased penetration of renewable generation, and negative prices. As an example, the ISO-NE Market Monitor reported that the average load-weighted real-time locational marginal pricing (LMP) for the first six months of 2017 at ISO-NE was $30.24/MWh, compared to $53.03/MWh for first half year of 2015 (ISO-NE 2018).

Figure 2. U.S. Wholesale Power Prices (Source: Bloomberg 2018)

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Since natural gas fueled power plants have the capability for flexible operation, low capital cost and short/predictable construction time, gas-fired generators are a preferred option for capacity additions when fuel prices are low. With the advent of horizontal drilling and hydraulic fracturing, enormous quantities of natural gas have been produced in the U.S. and delivered to market. Natural gas prices peaked in December 2005 at over $14/MMBtu and then again in July 2008 at over $13/MMBtu. However, beginning in 2010 natural gas prices declined and have remained below $5/MMBtu. More recently (i.e., since 2015), natural gas prices have been below $3/MMBtu and have been below $2/MMBtu for 5 out of the last 8 months with prices hitting a 17-year low in late winter 2015/2016 at below $1.50/MMBtu. Natural gas-fired generation is often the marginal unit in electricity markets, so lower natural gas prices result in lower electricity market clearing prices. On the other hand, significant renewable generation capacity has been added, despite low electricity market prices, which is mostly driven by out-of-electricity-market subsidies that include federal production tax credits, federal investment tax credits, state renewable mandates, and other policies. A combination of intermittent operation and zero or negative bids into electricity markets mean that the increased amount of renewable generation further depresses electricity market prices. Negative prices happen when there are more inflexible generator offers than demand for electricity in a trading period. When this happens, the market price is lowered until generators withdraw and/or price sensitive users increase demand to match supply and demand. In some instances, the market price can be negative. Nuclear power plants are usually operated at maximum output during all hours between refueling outages, so that negative prices in the electricity market means that the owners of nuclear power plants must make payments to the market operator resulting in less net annual operating revenue. To date, nuclear projects have fundamentally been viewed in the contexts of investment options for electricity generation, differentiated mainly by the cost factors. The climate and air-quality benefits of wind and solar power was reported (Millstein et al., 2017). However, U.S. electricity markets and regulations have done little to monetize factors like environmental impact, or contributions to grid reliability and resiliency. Nuclear power plants have been limited to revenue from selling electricity and capacity, until the New York ZEC (zero emission credit) Order provided additional revenue for the value of the emission-free energy generated by nuclear power plants. Some limited federal policies have been implemented to support nuclear power development (e.g., federal loan guarantees, investment tax credits), but these policies have had little impact on the margin. The future economic viability of a nuclear power plant (or another generation technology) should be a function of costs, potential revenue streams, and policies. Select U.S. states have responded to the prospect of premature retirements of nuclear reactors by instituting policies to value unpriced attributes, suggesting a growing recognition that attributes beyond energy cost are not adequately reflected under current economic and policy conditions. Many U.S. states have either explicit goals to reduce greenhouse gas emissions from electric power generation, or renewable portfolio standards (RPS) requiring increased deployment of renewable energy technologies. Nuclear plants are generally excluded from policies designed to promote deployment of eligible non-emitting resources. However, the premature retirement of

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nuclear power plants would adversely impact emissions (Roth and Jaramillo, 2017) with negative impacts on the electricity system, energy security, the environment, and the economy. These negative impacts have not been included in early retirement decisions because there is no compensation to the nuclear power plant for providing electricity system benefits and public benefits. In other words, the public benefits are generally not “monetized” in a way that can provide a return on the investment of the private owners. These early retirements of operating nuclear power plants will mean the loss of a large amount of zero-emission electricity, inconsistent with the goal of reducing carbon emissions and other air pollutants in the electricity sector. In addition, significant negative effects of these closures are being, and will be experienced, on local economies, electricity grid resilience, fuel diversity and United States world leadership on nuclear issues, including nonproliferation and safety standards.

1.3 LCOE Market Approach

The levelized cost of electricity (LCOE) of a generation technology is defined as the cost that, if assigned to every unit of energy produced by the technology, will equal the total net present value life cycle cost of that technology (EIA 2018). LCOE is a commonly used metric in the energy community and reducing LCOE has historically been a core goal of the sector. A primary function of electric system planners and utilities is to minimize the total cost to reliably meet demand for electricity, subject to other policy, societal, and legal constraints. A primary function of wholesale electricity markets is to assist in meeting these goals by maximizing societal welfare in delivering electricity services, primarily through creating markets that enable efficient dispatch and investment decisions. Minimizing total system costs requires consideration of not just LCOE, but also the ‘system value’ of various generation technologies. ‘System value’ refers to the ability of an electricity generator or storage provider to contribute to meeting demand as it avoids the cost of using other bulk-power system or distribution system resources (Mills and Wiser 2014; Lamont 2008). In general, costs that might be avoided include those related to energy, capacity, balancing, and transmission. Of these, ‘energy value’ is often based on the ability of a resource to offset the production costs of other resources. In competitive markets, wholesale market locational-marginal prices (LMPs) are sometimes used to estimate this value. ‘Capacity value’ measures a generator’s contribution to overall long-term resource adequacy via an ability to offset the need (and cost) for capacity from other technologies. ‘Balancing’ needs originate from the required flexibility to maintain a continuous operational balance between supply and demand, with some resources increasing these needs and related costs and others offsetting them. ‘‘Transmission’ refers to the differential impact of resources on the need for additional transmission infrastructure. Similarly, ‘distribution’ refers to the differential impact of resources on the need for additional distribution infrastructure. ‘System value’ ideally would comprehensively capture all five of the values considered above. ‘Market value’, meanwhile, refers to the subset of these factors that are actually priced in markets in the form of specific revenue (Mills and Wiser 2014, 2015; Lamont 2008). An idealized perfectly competitive wholesale electricity market would mimic the decisions described above, with efficient investment decisions occurring when a generator is able to cover its total fixed and variable cost (in effect, total cost) through the revenue it earns in the market

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(Wiser et al., 2017). Modeled outcomes and wholesale electricity prices do not necessarily reflect the full ‘system value’ of individual resources. Both models and wholesale markets are limited in their ability to account for all impacts. Additionally, wholesale electricity markets as well as the models used to simulate them may not be designed to reveal the complete set of values for all energy resources, particularly broader societal values, such as differential environmental costs and benefits. As such, broader policy considerations also sometimes come into play, with revenue for generators affected by those decisions—whether through feed-in tariffs or premiums, tax incentives, markets for renewable energy certificates, or otherwise. As described in Wiser et al. (2017), if all generation technologies were homogenous, decision-making by planners, utilities, and power plant investors would be simple: purchase from or invest in the technology with the lowest LCOE. However, generation technologies have widely varying technical and economic characteristics and deliver different services (e.g., a natural gas combustion turbine may operate only in the 5% of peak hours in a year, whereas a nuclear plant may operate on a 24x7 basis for most of the year). Although LCOE can be a useful measure to industry and R&D managers for tracking technology progress or for seeking ways to reduce technology costs, comparing the LCOE of different technologies can be misleading for the aforementioned differences in characteristics, services, and applications between technologies (Joskow 2011; Borenstein 2012; Hirth 2013). While easy to understand and a useful means of comparison within one type of generation or storage technology over time, this metric does not consider other grid services beyond energy that are necessary for the operation of a reliable and resilient power system. Within the context of the broader electricity system, LCOE is an imperfect measure of economic competitiveness, and is not used as the sole decision-variable when comparing alternative sources of electricity supply. As such, there is a need for more comprehensive metrics, which consider energy as well as these additional grid services, which can be used to directly compare technologies.

1.4 Impacts of Inertia

The decommissioning of nuclear power plants and the anticipated replacement of conventional power generation, such as coal and oil-fired power plants, is leading to reduction of inertia in power grids. Nuclear and coal thermal power plants (synchronous generators) are being replaced by renewable energy sources that may not add equivalent amounts of synchronous inertia. This presents a concern regarding the stability, reliability, and resilience of electric grids because they result in a significant reduction of rotational and thermal inertia. This reduction of inertia can adversely affect grid stability under certain disturbances and results in frequent brownouts and even widespread blackouts. It is noted that there are research efforts and implementations to enable renewable energy sources to have capabilities to provide virtual inertia that emulates the response of synchronous generators that are operated conventionally using thermal sources such as nuclear (Tielens et al., 2016).

Inertia plays an important role in maintaining the stability of the electric grid (Ulbig et al., 2014). Sources of inertia in the interconnected power grid include the rotating mass in power plant turbo-machinery, i.e. turbine blades, turbines and generators shafts, and their mechanical couplings. Rotating shafts in motors and other rotating machines supplied by the power grid are also a source of rotational inertia. Another source of inertia is the thermal mass available in the

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conventional nuclear and thermal power plants. It consists of the mass of high-temperature equipment such as boilers, pressurizers, steam pipes, and steam turbines, as well as the fluid used to convert and transfer energy from the heat sources to the rotating generators’ shafts.

During all times in an electric grid, the generation and load consumption has to be balanced along with the expected transmission losses. Hence after such a sudden change in the grid, the generators have to respond immediately to an alternate operational level so as to seamlessly match the altered load levels. The immediate first response by the synchronous generators to provide a suitable correctional action is on account of its rotational and thermal inertia.

Inertia sources can absorb excess energy when a sudden load drop occurs, and can release stored energy when needed. A sudden load drop in this context implies loss of a section of the electric grid, due to natural or artificial events. This is a commonly observed event in both transmission and distribution networks and leads to lowered metrics associated with quality of service (Mohanpurkar et al., 2018).

Inertia in the grid can also control the supply of power to the grid during sudden and large step loads, preventing power surges that can damage sensitive equipment. Such changes in supply and demand, generally result in a change of grid frequency and grid voltage amplitude that can lead to power plant shutdown if preset limits are exceeded. The presence of inertia helps the power plant governor restore and maintain grid stability during these disturbances, thereby, reducing the occurrence of catastrophic grid failure. In the case of extreme events physical damage to grid equipment, protection systems, and even propagation of cascading failures may occur. Hence, local events may manifest into regional and even interconnection level impacts.

1.5 Project Objective

The long term objectives of this project are to use RISA tools to assess, characterize, and quantify predicted market revenue gaps for the existing nuclear fleet (by site, region, and grid integration), analyze electricity market attributes, and analyze stress case conditions by developing attribute-based revenue models and assessing ways to monetize the attributes of nuclear (and other) energy systems. The potentially increased revenues could improve the status of NPPs and to provide secure and clean electricity for customers.

In addition, this project also aims to develop and apply a framework that enables DOE and electricity sector stakeholders to compare the system value of grid services provided by generation technologies necessary for reliable and resilient grid operations, on a more comprehensive and consistent “apples-to-apples” basis than previously possible using only the levelized cost of energy (LCOE) metric, and to reflect other potentially high impact grid services in the system value of specific technologies, that are not otherwise currently valued in organized markets (e.g., inertia, voltage support).

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2. Market Economics R&D

2.1 Technical approach

2.1.1 Risk-Informed Systems Analysis (RISA)

The RISA pathway is to support decision-making related to economics, reliability, and safety, providing integrated plant systems analysis solutions through collaborative demonstrations to enhance economic competitiveness of the operating fleet. This pathway will (1) demonstrate a risk-assessment method coupled to safety margin quantification that can be used by decision makers as part of their margin recovery strategies; and (2) apply the RISA toolkit that enables more accurate representation (e.g., reduce conservatisms, increase realism) of margins for the long-term benefit of nuclear assets. The RISA methodology can be used to optimize plant economic performance and safety by incorporating plant impacts, physical aging, and degradation processes into an integrated systems analysis context. Developing a systematic approach to understand safety margins and economics, and by using that approach to develop margins management options, represent a vital capability for the nuclear power plant owner/operator. It enables plant owner-operators to recover costs spent managing unnecessary conservatism in operating practices and align safety management activities with safety outcomes. RISA R&D is focused in these two main contributors to the revenue gap: (a) plant generating costs; and (b) the electricity market marketplace. RISA plant generating costs research, development, and demonstration will be investigated by other projects. RISA market economics research addresses the remaining component of the revenue gap equation: the electricity market.

The primary issues and challenges of the marketplace today (INL rev.6, 2018), as illustrated in Figure 3, include the following:

Markets are not structured to recognize all of the valuable attributes of different energy sources, like emission avoidance, reliability, and on-site fuel.

A debate of how to value resiliency and other relevant energy attributes continues to allow for the recovery of costs of fuel-secure generation units frequently relied upon to make our grid reliable and resilient.

Energy companies price their electricity based on short-term costs, thereby making long-term investments difficult to afford.

Electricity demand is projected to be flat for the foreseeable future. RISA tools and methods can be used to address these challenges by: Quantifying and defining resilience for the nuclear power plants. Defining baseline cases, and analyze stress test cases. Developing plans in partnership with vertical-integrated utilities and grid operators to address

revenue gaps by region and by grid integration. Use modeling and simulation tools to analyze stress case conditions.

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Figure 3. Electricity Market Attributes and Challenges (Courtesy: NEI 2018)

2.1.2 Resilience and Reliability Attributes

While there is no official standard definition of grid resilience, key reports summarize responses and practices from hundreds of electricity operators (DOE 2017; NIAC 2009, Mann et al., 2018). Resilience is widely understood to be the ability to anticipate, adapt to, and recover from extreme, dynamic conditions (DOE 2017). Error! Reference source not found.4 chronologically arranges the four main components of resilience coined by specialist Stephen Flynn (Flynn 2008). The measures are summarized as (1) robustness, which includes preventative measures and infrastructure put in place before an event; (2) resourcefulness, which emphasizes workers’ abilities to make critical decisions as an event progresses; (3) rapid recovery, which includes steps taken immediately after an event to expedite restoration; and (4) adaptability, including the long-term, post-event measures to learn from incidents and adjust accordingly.

Figure 4. Illustration of the primary components/phases of resilience (Flynn 2008)

Improving the grid’s resilience requires a fundamental understanding of possible courses of action during each phase of a large-scale disturbance, taking into account the particulars of any given form of grid failure. The recently rejected Notice of Proposed Rulemaking (NOPR), submitted to the Federal Energy Regulatory Commission (FERC) in October 2017, sparked nationwide conversations about the contributions from different types of power plants to grid resilience (FERC 2017). The proposed rule specifically emphasized the role of traditional “baseload” generators—coal and nuclear—as sources of fuel security during extreme weather that cuts off supply channels to natural gas. FERC concluded that the NOPR would significantly raise consumer costs without appreciably contributing to grid reliability and resilience. The

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disparity between the NOPR and FERC analyses highlights the need for more comprehensive understanding of resilience determinants. Resilience, in the context of the bulk electric system, relates to preparing for, operating through and recovering from a high-impact, low-frequency event (PJM 2017). Resilience is remaining reliable even during these high impact, low frequency events. A resilient energy system can anticipate, minimize the impact of and quickly recover from shocks. It also can provide alternative means of satisfying energy service needs in the event of changed external circumstances. In addition to delivering energy services reliably during strained system conditions to which probabilities can be attached (e.g., plant outages, weather variability), a resilient energy system also must be resistant at larger scale. For a specific region, resilience means the ability of the system to withstand or timely recover from high risk events beyond the control of individual generators and ensuring sufficient system flexibility in light of a changing generation mix.

To develop a resilient bulk electric system that will operate reliably in response to and be resilient in the face of a wide range of probable contingencies, North American Electric Reliability Corporation (NERC) standards require Planning Coordinators and Transmission Planners to analyze extreme events, such as loss of a large gas pipeline into a region or multiple regions that have significant gas-fired generation. Currently, if such an analysis concludes there is a probability of cascading outages caused by the occurrence of extreme events, an evaluation is conducted of possible actions to reduce the likelihood or mitigate the consequences and adverse impacts of the event(s). The NERC ERO Reliability Risk Priorities report identifies additional risk scenarios that should be assessed but go beyond current NERC criteria (ERO 2016). While some of these additional risk scenarios may result in developing standards, many are recommended as assessments only. These risks include changing the resource mix, bulk-power system planning, resource adequacy and performance, extreme natural events, physical security and cybersecurity vulnerabilities. Some themes and takeaways from the Reliability Risk Priorities report focus on enhancing resilience and recovery and a focus on natural gas deliverability (ERO 2016).

The electricity sector has historically used reliability as the principal metric for grid risk. NERC defines reliability in terms of two key attributes: (1) adequacy—the ability to meet energy and electricity demand at all times, and (2) security—the ability to withstand any disturbances in the grid (Berkeley and Wallace 2010). An awareness of the existing level of generator reliability attributes is required to understand how retirements of conventional generation (e.g., coal and nuclear) and replacement of capacity with natural gas generators and variable energy resources will impact reliable operation of the bulk electric system. At the same time, resources on the distribution system are increasing because of demand response programs and growth of distributed energy resources. To better quantify the generator reliability attributes that contribute to grid reliability, a matrix of the generator reliability attributes is defined, including frequency response, voltage control, ramp, fuel assurance, flexibility, black start capable, etc (Figure 5) (Mann et al., 2018; Borders and Szilard 2018).

The capability to provide various generator reliability attributes may change in the future as a result of changes in technology or resource mix driven by regulation. The matrix is intended primarily to group units by fuel type, and as such, may not exclusively represent every

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combination of fuel, prime mover and technology type. Should the actual, future fuel mix evolve such that the potential exists for the quantity of generator reliability attributes to fall below that which is necessary to maintain reliable grid operations, then operations, market incentives and regulatory structures may need to shift to provide incentives to ensure adequate levels of these attributes are maintained (PJM 2017).

Legend

Much better than NGCC Better than NGCC Worse than NGCC Much worse than NGCC

Notes * This chart considers NGCC generators to be the benchmark technology for power generation in

the U.S. The chart rates other generators by comparing them against the NGCC benchmark. ** i.e., "Availability factor"

*** Each generator type can use a variety of cooling technologies and water sources of varying robustness. For simplicity, this column considers cooling water consumption magnitude.

**** Typical plant size has more to do with financing and economies of scale, but it does also influence grid vulnerability.

Figure 5. Consolidated range of factors that affect reliability, resilience, and vulnerability (Mann et al., 2018)

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2.1.3 System Value

The ways in which a technology interacts with the system depends on the technology’s characteristics and the characteristics of the overall electric system. This interaction will reflect the extent to which a technology’s characteristics enable its energy output to be delivered to the end user and contribute to the overall system’s ability to deliver energy reliably to the end user (i.e., how it enables other technologies on the system to deliver their energy output). A primary objective in the design and operation of an electric power system is to minimize costs while maintaining certain system attributes, such as specified levels of reliability and, depending on the region, additional desired attributes including resiliency and sustainability. These system attributes are achieved through the aggregate provision of grid services by all technologies in the system. The ability and cost of a technology to provide a specific grid service depends on the technology’s characteristics.

Figure 6. Relationship between technology characteristics, grid services, and system attributes A technology’s value is determined by its interaction with the system and is affected by whether and how much it contributes to meeting system attributes via the provision of grid services (see Figure 6). If the addition of the technology contributes to meeting a system attribute (e.g., it provides a grid service), then that displaces the need for some other technology in the system to contribute to the system attribute. Generally, as the objective is to meet system attributes through supply grid services while minimizing overall system costs, the other resource that is displaced is whichever resource that would have otherwise been the most expensive way to meet the system attribute, known as the ‘marginal resource’. The marginal cost of meeting the system attribute with this grid service is known by a variety of names, including the ‘dual value’ or ‘shadow price’ in optimization literature, or the ‘market-clearing price’ in power markets. Estimating the value of a technology can be distilled to the question of what grid services the technology provides, and what the shadow price (marginal cost/shadow price/ market price) of that grid service is. The value of a given grid service provided by technology is the product of the shadow price of the grid service and the quantity of the grid service provided. The aggregate value of a technology is the sum of the values from all grid services provided by the technology. In this project, we will document and apply a variety of methods, based largely on the approaches commonly used in existing grid modeling tools and organized wholesale power markets, to quantitatively estimate the value of a variety of technologies based on the grid services that they currently provide or may provide in the future. We also propose to identify

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those grid services, for which valuation methods are not clearly recognized or available, to bound the potential range of value for these services and to conduct research on a high priority subset of these “missing” components to develop quantification methods. There are many challenges encountered in attempting to quantify the value of a technology within a power system. A few particularly salient challenges are the multitude of desired system attributes associated with the operation of modern power systems, and the magnitude of the provision and associated shadow prices of the grid services necessary to achieve these system attributes vary considerably over time, space, and system state (e.g., generation mix, transmission and distribution system network topology). 2.1.4 Rotational and Thermal Inertia

Physics-based models are used for a quantitative evaluation of rotational and thermal inertia. The definition of rotational inertia and the derivation of its expression is given in subsection 2.1.4.1. The definition of thermal inertia and model equations used to represent its effects are given in subsection 2.1.4.2. 2.1.4.1 Rotational Inertia Representation

Rotational inertia is defined through Newton’s second law of motion for a rotating body which relates the net torque acting on a rotating body to its acceleration as follows:

∑ 𝑇𝑇𝑖𝑖 = 𝐽𝐽𝐽𝐽𝑖𝑖 (1) where Ti represents the torques acting on the body, J its moment of inertia, and α its angular acceleration. For a synchronous generator driven by a turbine, the torques acting on the rotor shaft are the mechanical torque Tm from the turbine, the retarding electromagnetic torque Te that develops in the machine, and a friction torque Tf that accounts for various friction losses such bearing losses and windage loss. Expanding equation (1):

𝑇𝑇𝑚𝑚 − 𝑇𝑇𝑒𝑒 − 𝑇𝑇𝑓𝑓 = 𝐽𝐽𝐽𝐽 (2) Using the angular velocity ω and taking the friction torque proportional to velocity with friction factor µf, equation (2) gives:

𝑇𝑇𝑚𝑚 − 𝑇𝑇𝑒𝑒 − 𝜇𝜇𝑓𝑓𝜔𝜔 = 𝐽𝐽 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

(3) A more general equation is obtained using the per-unit basis with the generator rated power S (VA) and rated angular velocity ω0 (rad/s). Multiplying equation (3) by 𝑑𝑑

𝑆𝑆 and the right hand side

of (3) by 2𝑑𝑑02

2𝑑𝑑02 we obtain an equation relating the generator per-unit power and per-unit angular

velocity: 𝑇𝑇𝑚𝑚𝑑𝑑𝑆𝑆− 𝑇𝑇𝑒𝑒𝑑𝑑

𝑆𝑆−

𝑑𝑑𝜇𝜇𝑓𝑓𝑑𝑑

𝑆𝑆= 𝑃𝑃𝑚𝑚

𝑆𝑆− 𝑃𝑃𝑒𝑒

𝑆𝑆− 𝑃𝑃𝑓𝑓

𝑆𝑆= 𝐽𝐽

𝑆𝑆2𝑑𝑑0

2

2𝑑𝑑02 𝜔𝜔

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= 2𝑆𝑆

(12𝐽𝐽𝜔𝜔0

2) 𝑑𝑑𝑑𝑑0

𝑑𝑑 𝜔𝜔𝜔𝜔0𝑑𝑑𝑑𝑑

(4) Recognizing the kinetic energy stored in the rotor mass when the generator is operating at nominal speed ω0, E0 = ½ Jω0

2, and using per-unit notation, we have: 𝑃𝑃𝑚𝑚���� − 𝑃𝑃𝑒𝑒� − 𝑃𝑃𝑓𝑓 ���� = 2𝐸𝐸0

𝑆𝑆𝜔𝜔� 𝑑𝑑𝑑𝑑�

𝑑𝑑𝑑𝑑 (5)

An inertia factor H is defined as: 𝐻𝐻 = 𝐸𝐸0

𝑆𝑆= 𝐾𝐾𝑖𝑖𝐾𝐾𝑒𝑒𝑑𝑑𝑖𝑖𝐾𝐾 𝑒𝑒𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑠𝑠𝑑𝑑𝑠𝑠𝑒𝑒𝑒𝑒𝑑𝑑 𝑖𝑖𝐾𝐾 𝑒𝑒𝑠𝑠𝑑𝑑𝑠𝑠𝑒𝑒 𝑚𝑚𝑚𝑚𝑠𝑠𝑠𝑠 (𝐽𝐽𝑠𝑠𝐽𝐽𝐽𝐽𝑒𝑒𝑠𝑠)

𝐺𝐺𝑒𝑒𝐾𝐾𝑒𝑒𝑒𝑒𝑚𝑚𝑑𝑑𝑠𝑠𝑒𝑒 𝑒𝑒𝑚𝑚𝑑𝑑𝑒𝑒𝑑𝑑 𝑝𝑝𝑠𝑠𝑝𝑝𝑒𝑒𝑒𝑒 (𝑉𝑉𝑉𝑉) (6)

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Inserting the expression of H (6) in equation (5) and noting that, in per-units, the electrical frequency is equal to the mechanical frequency, we obtain the general dynamic equation relating frequency changes to power changes for generators connected to power grids:

𝑃𝑃𝑚𝑚���� − 𝑃𝑃𝑒𝑒� − 𝑃𝑃𝑓𝑓 ���� = 2𝐻𝐻𝜔𝜔𝑒𝑒����𝑑𝑑𝑑𝑑𝑒𝑒����𝑑𝑑𝑑𝑑

(7) Equation (7) shows that a change in the electrical frequency requires a change in generator power with the inertia factor H playing a moderating or enhancing role in frequency response to disturbances. H has unit of time and its typical values are in the range 1 s to 10 s.

The location of the disturbance in the power grid with respect to the locations of multiple generators and the action of breakers and fault isolation procedures triggered by the disturbances cause various generators to contribute their inertia in different proportion. Consequently, using a single average value does necessarily give a correct result if a single equation such as (7) is used to represent multiple grid-connected generators with an average inertia factor H. It is therefore necessary to represent each generator by its own dynamic equation even though this may require large computational resources when studying power grids with a large number of connected generators.

2.1.4.2 Thermal Inertia Representation:

Thermal inertia cannot be expressed in terms of a single factor as is the case for rotational inertia. Thermal inertia represents the physical mass that interacts with the heat energy from the nuclear reactor, or other heat sources, and affects the energy transferred to and from the power grid. This thermal mass has the ability to absorb heat and can support changes in pressure of the fluid, in the boilers and heat channels, used to transfer and convert heat energy from the heat source to the generators’ shafts.

In order to quantify the effect of thermal inertia, so that its value can be assessed, a full thermodynamic analysis of all pertinent components is necessary. These include water in liquid and steam states, as it transfers heat from the source to the steam turbine, the boilers where heat exchange occurs, the steam channels, the steam turbine, and the condensers. Water pumps and valves are also important to include in the model since they control the flow of feed water and output steam. Figure 8 shows a diagram describing the thermodynamic an ideal cycle in a steam power plant, without reference to a specific heat source for simplicity. Using the notations on Figure 7 for reference with h referring to enthalpy and �̇�𝑚 to the fluid (steam or water) mass flow rate, the first order model equations describing the thermodynamic processes using an ideal cycle, assuming adiabatic processes, and no losses are as follows: Power added to the feed-water pump:

𝑊𝑊𝑃𝑃̇ = �̇�𝑚(ℎ1𝑎𝑎 − ℎ1) (8) Heat transfer in the boiler:

�̇�𝑄𝐼𝐼𝐼𝐼 = �̇�𝑚(ℎ3 − ℎ1𝑎𝑎) (9) Power delivered by the turbine to the generator:

�̇�𝑊𝑇𝑇 = �̇�𝑚(ℎ3 − ℎ4) (10) Heat rejected by the condenser:

�̇�𝑄𝑂𝑂𝑂𝑂𝑇𝑇 = �̇�𝑚(ℎ4 − ℎ1) (11)

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Figure 7. Diagram describing an ideal thermodynamic cycle in a steam power plant

As changes occur in the power grid, i.e. at the output of the steam turbine, the thermodynamic response of the steam power plant is represented, at first order, by the equations just described. Another factor that plays an important role in the dynamics of steam power plants is the steam volume effect phenomenon, which reflects the fact that changes in output steam flow rate lag the changes in input steam flow rate. This phenomenon exists in all volumes, i.e. steam turbine, boiler, condenser, and steam channels. In a vessel such as a steam turbine, with volume V, containing steam with weight W and density ρ, the change of steam weight can be expressed as:

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= 𝑉𝑉 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= �̇�𝑚𝐼𝐼𝐼𝐼 − �̇�𝑚𝑂𝑂𝑂𝑂𝑇𝑇 (12) If we assume the output steam flow rate is proportional to steam pressure P in the vessel, we have:

�̇�𝑚𝑂𝑂𝑂𝑂𝑇𝑇 = �̇�𝑚𝑂𝑂𝑃𝑃𝑃𝑃𝑂𝑂

(13) where �̇�𝑚𝑂𝑂 𝑎𝑎𝑎𝑎𝑎𝑎 𝑃𝑃𝑂𝑂 are the rated steam mass flow rate and the rated steam pressure respectively. If the steam temperature is constant in the vessel then:

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= 𝑑𝑑𝑃𝑃𝑑𝑑𝑑𝑑

𝜕𝜕𝑑𝑑𝜕𝜕𝑃𝑃

(14)

The term 𝜕𝜕𝑑𝑑𝜕𝜕𝑃𝑃

is a steam parameter at given temperature and is a tabulated value, so it is a known value. Inserting (14) and (13) into (12) we have:

�𝑉𝑉 𝑃𝑃𝑂𝑂�̇�𝑚𝑂𝑂

𝜕𝜕𝑑𝑑𝜕𝜕𝑃𝑃� 𝑑𝑑�̇�𝑚𝑂𝑂𝑂𝑂𝑂𝑂

𝑑𝑑𝑑𝑑= �̇�𝑚𝐼𝐼𝐼𝐼 − �̇�𝑚𝑂𝑂𝑂𝑂𝑇𝑇 (15)

Defining the time constant TV:

𝑇𝑇𝑉𝑉 = 𝑉𝑉 𝑃𝑃𝑂𝑂�̇�𝑚𝑂𝑂

𝜕𝜕𝑑𝑑𝜕𝜕𝑃𝑃

(16) Laplace transform of (15) provides:

�̇�𝑚𝑂𝑂𝑂𝑂𝑇𝑇 = 11+𝑇𝑇𝑉𝑉𝑆𝑆

�̇�𝑚𝐼𝐼𝐼𝐼 (17)

Equation (17) shows that the output steam mass flow lags the input steam mass flow in all vessels of a thermal power plant, as was mentioned earlier. This phenomenon, referred to as the

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“steam volume effect” plays an important role in the response of the thermal power plant to power system disturbances. It reflects the effect of thermal inertia in the system as the responses of various power plant components are slowed or delayed preventing the power plant control system from acting on the power source to keep the power demand and supply balanced.

2.2 Partnerships and Industrial Collaborations

The research proposed in this plan is a collaboration between the LWRS Program, INL and other collaborators, potentially including Jensen Hughes (JH), Lawrence Berkeley National Laboratory (LBNL), and the University of Texas at Austin. Each project will be conducted by different parties and collaborations. Even though the project will be conducted collaboratively, each organization will have different focuses.

The following provides a breakdown of each of the collaborator’s specific activities.

INL will firstly coordinate among collaborators, manage all the projects to be aligned with time schedule and arrange deliverables. Secondly, INL will develop and validate some methods and tools that will be used in analysis of market economics. Specifically, INL will apply existing model framework such as RAVEN and other tools to provide integrated analyses of all technical and economic aspects of electricity market.

Jensen Hughes will conduct technical analyses on fuel mix and grid resilience study on a regional market. Jensen Hughes will provide integrated analysis capabilities in all technical and economic assessments of using current industry tools. Additionally, Jensen Hughes will support industry assessments of the economic benefits. Finally, Jensen Hughes will contribute to the development of the RISA methods and tools with a focus on identifying characteristics and capabilities required for industry acceptance and adoption.

LBNL will mainly analyze social and environmental benefits of nuclear power fleets potentially resulting from avoid emission and damages.

University of Texas at Austin will mainly conduct technical and economic analysis of grid reliability and resilience.

Other collaborators may be involved in future along with project progress. Each of the partners will work collaboratively to prioritize the research tasks necessary to move the projects forward.

2.3 Description of Market Models

Both existing and advanced analysis tools will be utilized in the application of RISA to the key transient and accident sequences identified previously.

2.3.1 AVERT

EPA's Avoided Emissions and geneRation Tool (AVERT) model is used to estimate the historical impacts of nuclear power generation on US air emissions. The AVERT model was developed to determine which electricity generators would be most likely to respond to either the addition or removal of non-dispatchable resources such as nuclear, wind or solar power. In other

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words, the AVERT model finds the generators on the margin at each hour of the year and returns those generators along with their emission characteristics, allowing us to calculate the emissions impact of removing the existing nuclear resources from the power sector. 2.3.2 EASIUR

EASIUR, the impact factor model developed in Penn et al. and Levy et al., Air Pollution Emission Experiments and Policy analysis model (AP2, formerly APEEP: Muller et al.), EPA RIA benefits per-tonne estimates, and COBRA.

2.3.3 RAVEN

RAVEN is a software framework that is designed to perform parametric and stochastic analyses based on the response of complex systems codes (Mandelli 2016). It is capable of communicating directly with the system codes described above that currently used to perform plant safety analyses. The provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and exploring input spaces using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN’s strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. 2.3.4 ReEDS

NREL’s Regional Energy Deployment System (ReEDS) is a capacity planning and dispatch model for the North American electricity system and is a national-scale capacity expansion model (and, as appropriate, the higher-fidelity PLEXOS and PRAS models).

ReEDS uses high spatial resolution and high-fidelity modeling. Though it covers a broad geographic and technological scope, ReEDS is designed to reflect the regional attributes of energy production and consumption. Unique among long-term capacity expansion models, ReEDS possesses advanced algorithms and data to represent the cost and value of variable renewable energy; the full suite of other major generation technologies, including fossil and nuclear; and transmission and storage expansion options. Used in combination with other NREL tools, data, and expertise, ReEDS can provide objective and comprehensive electricity system futures.

2.3.5 Weather Based Resilience Optimization Tool

The weather based resilience optimization tool is a multivariate optimization algorithm that models the fuel mix and impact on resilience. The tool allows the end user to understand the impacts to reliability and weather-based resilience as the fuel mix changes. The tool uses quantitative factors provided in the report on grid reliability and resilience to evaluate how changes in the fuel mix impact reliability, resilience, and vulnerability for a large set of energy sources.

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The tool provides the user the ability to change the fuel mix across all energy sources, change the input weighting factors per attribute, select specific attributes for optimization (this gives the user the ability to interactively explore various definitions of resilience), and understand the impact to each attribute under weather-based conditions as the fuel mix changes. 2.3.6 US-REGEN

The US Regional Economy, Greenhouse Gas, and Energy Model (US-REGEN) is an energy economy model developed and maintained by the Electric Power Research Institute (EPRI). It combines a detailed dispatch and capacity expansion model of the United States electric sector with a high-level macroeconomic model of the United States economy. The electric sector component of US-REGEN is a detailed generation planning model that makes time-based decisions about capacity (e.g. new investment, retrofit, or retire) and dispatch to meet energy demand for both generation and inter-region transmission. The macroeconomic component of US-REGEN is a computable general equilibrium (CGE) model that is designed to show how changes in policy impact economic activities relative to a baseline case. The ability to couple solutions from the electric sector and macroeconomic component models makes US-REGEN capable of exploring sensitivities and the interdependence of technology, economics, and policy factors.

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3. Project Plan and Schedule

This strategic plan on modelling and quantifying prioritized energy attributes mainly consists of five tasks over the next three years. The tasks and milestones are summarized in Table 1 and discussed in details as followed.

Table 1. Schedule of R&D tasks and milestones

Task Timeline / Milestone Report (M)

Q1-Q2 FY19

Q3-Q4 FY19

Q1-Q2 FY20

Q3-Q4 FY20

Q1-Q2 FY21

Q3-Q4 FY21

1 Study Defining

2 Resilience Study and Market Analysis at regionals

Milestone report on 9/30/2019

3 Examining Social and Environmental Benefits of NPPs

Milestone report on 3/31/2020

4 System value of beyond LCOE

Milestone report on 3/31/2021

5 Study on Expanded Resilience and Inertia

Milestone report on 9/30/2021

Progress report Annual milestone report

3.1 Task 1: Define study regions, characteristics, tools and industrial partnerships

This task works closely with other four tasks over the next three years by setting up the project regions, system characteristics, tools to be applied and industrial partnerships for each task. This task begins with a literature review for each of the other four tasks and will encompass related prior and ongoing efforts in DOE, the academic community, and industry. The Task 2 review effort will define resilience and related attributes such as fuel delivery infrastructure robustness, severe weather durability, ramp rate per capacity. Task 3 will focus on identification of valuation methodologies for avoid emission and damage. Task 4 effort will focus on identification methodologies used to examine system value of beyond LCOE. Task 5 will focus on value the impacts of interdependence of fuel supply and identification of values of thermal and rotational inertia.

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3.2 Task 2: Complete a regional-level weather-based resilience study and analyze market and economic impacts of fuel

diversity

Summary: Study the impacts of interdependence of fuel supply infrastructures. Evaluate fuel availability and price stability under high impact, low frequency weather events and how it changes the fuel mix with respect to percentage nuclear. The DOE Grid Study highlighted the need to take action to ensure that our Nation’s electric grid remains reliable, resilient, and affordable (Mann et al., 2018). A debate of how to value resiliency and other relevant energy attributes continues to allow for the recovery of costs of fuel-secure generation units frequently relied upon to make our grid reliable and resilient. This task will quantify and define weather-based resilience for nuclear power plants. The tasks include defining baseline cases, and analyzing stress test cases using risk-informed tools and methods. Approach Resilience describes a broad array of low-probability but high-impact risks at all stages of the production, transmission and distribution of electricity. One resilience risk that requires further study is fuel security. Fuel security focuses on the vulnerability of fuel supply and delivery to generators and the risks inherent in increased dependence on a single fuel-delivery system. An ISO/RTO with significant nuclear power presence and other fuel mixes will be selected to study. In March 2017, PJM published an analysis of the reliability attributes associated with various potential future resource mixes. The analysis concluded that the bulk electric system could be operated reliably under an array of future supply portfolios. However, the scope of the analysis did not include the resilience of the system with various potential portfolios nor the risks associated with significant disruptive events. As the paper noted: “Heavy reliance on one resource type, such as a resource portfolio composed of 86 percent natural gas-fired resources, however, raises questions about electric system resilience, which are beyond the reliability questions this paper sought to address.” To define potential fuel-security criteria, the fuel-supply risks in an environment trending towards greater reliance on natural gas supply and delivery should be understood. Task 2 will perform targeted analyses to quantify the resilience of a regional grid in response to specific scenarios designed to explore identified fuel security risks, and quantify the extent to which the presence of light water reactors mitigates these fuel security risks. Specific objectives for Task 2 include: • Establish metrics for quantifying grid resilience in relation to extreme weather events that

impact fuel delivery to critical generators

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• Quantify the impacts on grid resilience from varying the percentage of the generation portfolio comprised of light water reactors

• Analyze regional market and economic impacts of fuel diversity. 3.2.1 Sub-task 2.1: Development of metrics and extreme weather based scenarios for

evaluating a regional grid resilience

Description: An ISO/RTO with significant nuclear power presence and other fuel mixes will be selected to study. Develop metrics for quantifying the grid resilience and fuel security. Collect information and develop models necessary to apply these metrics to a resilience study of the selected regional grid in various extreme weather event scenarios. Establish scenarios for evaluating grid resilience in defined weather events with varying generator asset portfolios. Although current fuel portfolios may be reliable, diverse, and well supplied with the required generator reliability attributes, an increased reliance on any one resource type introduces potential fuel security risks not recognized under existing reliability standards. Such risks could include the deliverability logistics of fuel supplies during stressed conditions over time as opposed to more momentary interruptions that otherwise are considered through the procurement of reserves. Analysis Scenarios Preliminary capacity portfolio scenarios to be investigated in this study include: • Base Portfolio: This scenario includes the current ISONE resource portfolio with scheduled

retirements. • Stressed Portfolio: This scenario includes the base portfolio scenario along with additional

coal and nuclear retirements. • High-Stressed Portfolio: This scenario includes the base scenario along with an assumption

that an increased percent of coal and nuclear are retired and replaced with natural gas within the same zone as the retired resources.

3.2.2 Sub-task 2.2: Model and evaluate grid resilience as function of light water reactor

deployment in defined extreme weather scenarios

Description: Model and evaluate grid resilience in defined extreme weather scenarios, and determine impact on grid resilience associated with varying light water reactor deployment levels. Determine the generator portfolio characteristics that present the greatest fuel security risks during extreme weather events, and quantify the extent to which inclusion of light water reactors in the generation profile mitigates these fuel security risks. Task 2.2 would allow for the proper valuation of grid services provided by light water reactors as well as competition among resources that today or in the future can provide those attributes to ensure a resilient grid. Task 2.2 will help identify and value fuel security attributes associated with light water reactors.

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The Weather-Based Resilience Optimization Tool will be used in Task 3.2 to provide insights on weather-based resilience as the fuel mix changes. The Weather-Based Resilience Optimization Tool has previously been used to evaluate weather-based resilience in the ERCOT region. Under Task 2.2 the tool will be adapted to analyze the selected region with significant nuclear power presence. Assumptions The following are a few high-level indicative assumptions that could be utilized for the analysis in Task 2.2: • Generator forced, planned and maintenance outage rates (other than outages related to fuel

supply) will be consistent with recent winters • Several different capacity supply portfolios under multiple different gas-availability

scenarios will be studied. • The study will be simulated under 2017-18 Cold Snap extended cold weather conditions and

under 2014 Polar Vortex loads and wind chill levels. • The study will be conducted for a region with significant nuclear power presence. Disruptions Task 2.2 will simulate disruptions to fuel delivery systems that could be the result of credible extreme events such as coordinated physical or cyber-attacks, extreme weather, etc. The following is a description of the disruption scenarios: • No disruptions; generators have access to supply throughout the winter, subject to current

pipeline capacity. • Reduction of a realistic percentage of delivery capability on particular constrained portions of

pipelines in the selected region. This would address the potential for a significant disruptive event to degrade the pipelines’ ability to deliver to a set of generating units.

• In addition to reduced supply availability over longer periods, study a few realistic but extreme contingencies such as a force majeure event on key delivery facilities.

• In addition to reduced gas availability, analyze other fuel supply disruptions. • Each of these disruptions will be applied to the three capacity portfolio scenarios described

above. 3.2.3 Sub-task 2.3: Analyze regional market and economic impacts

Description: Analyze market and economic impacts of fuel diversity at the selected region. Where possible, compile data and results that support system value determination to be performed in subsequent tasks. In Task 2.3, results from the grid resilience evaluation performed in Task 2.2 will be utilized to perform an analysis of market and economic impacts of fuel diversity for the selected region. Task 2.2 will provide information that specifies the generator portfolios necessary to achieve the specified resilience attributes. Task 2.3 will evaluate the economic impacts associated with implementing the specified generator portfolios. The clearing price required to achieve the

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required generator portfolio will provide an important baseline metric for subsequent studies of system value to be investigated in Task 4. In addition to the economic metrics collected, data related to non-market grid services associated with each of the scenario optimized generator portfolios will be compiled under this task to support the system value analysis to be performed in Task 4. These non-market grid services may include attributes such as grid stability, price stability, 24/7 electricity production, prevention of carbon emissions, clean air compliance, fuel diversity, etc.

3.3 Task 3: Evaluate the climate and air-quality benefits of nuclear power plants at an ISO/RTO with significant nuclear power

presence

The nuclear power fleets provide air-quality, public health and greenhouse gas (GHG) emission benefits as it reduces the reliance on combustion-based electricity generation. In the past years, regulatory changes and fossil fuel price changes have led to steep cuts in overall power-sector emissions of criteria air pollutants and CO2. In some cases the monetary value of these benefits varies widely across these studies. However, to the best of our knowledge, no study has fully quantified US nuclear power benefits over the past decade. In addition, wholesale electricity markets as well as the models used to simulate them may not be designed to reveal the complete set of values for all energy resources, particularly broader societal values, such as differential environmental costs and benefits. As such, broader policy considerations also sometimes come into play, with revenue for generators affected by those decisions—whether through feed-in tariffs or premiums, tax incentives, markets for renewable energy certificates, or otherwise. In Task 3, for an ISO/RTO with significant nuclear power presence, a suite of models on air-quality and health impact and social cost of carbon will be applied to evaluate economic benefits of nuclear power plants on avoided emission and avoided damage through the following sub-tasks: • A suite of reduced-form air-quality models will be applied to estimate the public health

benefits of reduced pollutant emissions. The range of estimates presented is driven both by uncertainty in the underlying processes and also by differences in model characteristics.

• A statistical model will be used to find the SO2, NOx, PM2.5 and CO2 emissions that are avoided due to nuclear generation. This set of emissions contributes to an important portion of total external costs associated with electricity production.

• A range of monetary climate benefits based on social cost of carbon (SCC) estimates will be presented.

• Finally, reasons that benefits differ between regions and over time will be investigated. 3.3.1 Sub-task 3.1: Evaluation of Avoided Emission

A suite of reduced-form air-quality models is to be used to estimate the public health benefits of reduced pollutant emissions. The range of estimates presented is driven both by uncertainty in

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the underlying processes and also by differences in model characteristics. EPA's Avoided Emissions and geneRation Tool (AVERT) model will be used to estimate the historical impacts of nuclear power generation on US air emissions. The AVERT model was developed to determine which electricity generators would be most likely to respond to either the addition or removal of non-dispatchable resources such as nuclear, wind or solar power. In other words, the AVERT model finds the generators on the margin at each hour of the year and returns those generators along with their emission characteristics, allowing us to calculate the emissions impact of removing the existing nuclear resources from the power sector. This approach produces a more detailed estimate of emission displacement than could be done from using simple regional average emission rates while allowing us to provide continental coverage over a certain period. 3.3.2 Sub-task 3.2: Evaluation of air-quality benefits

To estimate the value of reductions to the pollutants SO2, NOx and PM2.5, we use a suite of models: EASIUR, the impact factor model developed in Penn et al. and Levy et al., Air Pollution Emission Experiments and Policy analysis model (AP2, formerly APEEP: Muller et al.), EPA RIA benefits per-tonne estimates, and COBRA. 3.3.3 Sub-task 3.3: Evaluation of GHG emission reductions

The social cost of carbon (SCC) is an estimate of the present value of the societal cost of releasing an additional tonne of carbon. As there is wide uncertainty about the social costs of climate change, there is also a wide range of SCC estimates. A range of monetary climate benefits are also present based on social cost of carbon (SCC) estimates spanning most of the range found in the literature. The valuation of GHG emissions reductions based on a range of SCC values that is consistent with the current literature will be reported. 3.3.4 Sub-task 3.4: Investigation of reasons that benefits differ between regions and

time

Additionally, the current and future emphasis on capacity provision to meet peak residual load means that flexibility is essential to navigate future power systems, though the timescales and degree of flexibility required will depend on system configurations and technological characteristics. Additional revenue streams of about $15/MWh (e.g., production tax credits, primary heat sales) provide different varieties of investment uncertainty in comparison to the various forms of regulatory support. Carbon pricing through a CO2 tax or cap offer relatively predictable ways to value the emissions reduction benefits of nuclear, but their technology-neutral structures also help other low carbon substitutes as well (e.g., wind, solar, CCS), which makes evaluations of competitiveness difficult a decade or more in advance.

3.4 Task 4: Develop a risk-informed framework for optimizing grid system values

Levelized cost of electricity (LCOE) is a commonly used metric in the energy community. While easy to understand and a useful means of comparison within one type of generation or storage technology over time, this metric does not consider other grid services beyond energy that are

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necessary for the operation of a reliable and resilient power system. As such, there is a need for more comprehensive metrics, which consider energy as well as these additional grid services, which can be used to directly compare technologies. The objective of this task is to develop and apply a framework that enables DOE and electricity sector stakeholders to: Compare the system value of grid services provided by generation and storage technologies

necessary for reliable and resilient grid operations, on a more comprehensive and consistent “apples-to-apples” basis than previously possible using only the levelized cost of energy (LCOE) metric.

Reflect other potentially high impact grid services in the system value of specific technologies, which are not otherwise currently valued in organized markets (e.g., inertia, voltage support).

The work plan for this task is composed of the following sub-tasks that will be conducted concurrently: Task 4.1 Conceptual Framework - Develop a conceptual framework that will establish a

taxonomy of technology characteristics and grid services for valuation, a technology-neutral economic framework and associated guiding principles that establish the generalized methodology for system value calculations, and an assessment frame that provides a consistent basis on which calculations can be made through definition of both a set of system value metrics and a range of future electricity scenarios/regions/reference grids.

Task 4.2 System Value Calculation – Initially implement the conceptual framework through its application to available wholesale market price data and existing planning/operation models to estimate initial system value metrics for a range of scenarios/regions/reference grids, focusing on grid services valued today in markets (e.g., energy, capacity, transmission, some ancillary services).

Task 4.3 Missing/ Supplemental Components - Leverage ongoing DOE and industry work to identify approximation methods and bounded monetary estimates of the value of non-market grid services (those service not currently valued) and, for a high priority subset of these services, develop and apply new quantitative valuation methods to establish improved estimates of system value metrics.

Task 4.4 Technology Characterization for Grid Services Provision - Work with the DOE Technology Offices to ensure that the conceptual framework and its initial application in tools are informed by ongoing office-funded work and office perspectives, apply the taxonomy component of the conceptual framework to characterize the specific technologies of interest to the offices, including assessing the current and potential future costs of these technologies for providing specific grid services, and use the resulting characterizations and the initial estimates of system value metrics for these technologies to help inform RD&D planning.

As shown in Figure 8, the Conceptual Framework plays a central role in the project. An extensive literature review of prior work, including by the DOE Technology Offices as well as academia and industry, will inform the development of a generalized technology-neutral

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framework for determining the system value of technologies in providing grid services to support system reliability and resilience. This conceptual framework will have three distinct components: A system value taxonomy that defines and relates system attributes, grid services, and

technology characteristics. This taxonomy will identify and define the specific grid services that support reliability and resilience and their sources of cost and value, as well as the specific characteristics that enable a technology to provide a grid service. The taxonomy will also relate each technology characteristic to the specific grid services it enables, as well as to the resulting supported system attributes.

A generalized economic framework to quantify the system value of technology characteristics and grid services identified in the taxonomy. This framework will provide an overall economic approach for determining system value, identify existing best-in-class, state-of-the-art quantification methodologies to include in it, and identify an economic approach to ranking the importance of missing (non-market) grid services to reliability and resilience. The framework will also identify guiding principles that will ensure consistent and effective application of the generalized system value quantification methodologies.

An assessment frame for reporting system value metrics based on application of the economic framework that recognizes system and regional variation for current and potential future systems. This frame will include an aggregated set of system value metrics that are applicable across the widest range of technologies, grid services, and potential values, and identify specific future electricity scenarios/regions/reference grids, addressing potential regional variations in systems, technology mixes, and service requirements, for which to estimate system value metrics. The set of system value metrics that are defined are expected to be supplemental to the existing LCOE metrics, consistent with Figure 1 above, and focus on the net value that a technology provides to the system as a whole. This set is also expected to include quantification in native units amount of each grid service that is necessary for the reliable and resilient operation of the region/reference grid and future scenario considered (i.e., market depth), as well as the value contribution that each technology makes toward providing that necessary amount of the service.

Figure 8. Conceptual framework development The conceptual framework will be constructed to help practitioners avoid the traps associated with valuation of generating and storage technologies and to avoid confusion in the application

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of best-in-class, state-of-the-art methodologies. The intent of the conceptual framework (Task 4.1) is to develop external consensus on how system values can be appropriately estimated. While the conceptual framework will focus on valuing technology characteristics that support the provision of grid services, it will be designed to be extensible to incorporate the following additional services and impacts in the future: Non-electricity co-products (e.g., thermal energy) Value of subsidies and regulations Environmental and other social externalities Cybersecurity Economic development and jobs Technology-specific LCOE refinement

Figure 9 depicts how the framework developed in Task 4.1 will be used as the basis of for the development of an initial set of system value metrics in five discrete steps: The first step in implementing the conceptual framework is to apply the economic framework

and supporting technology to the specific problem at hand (e.g., the contribution of DERs to system value in the distribution system), including identification of the specific grid services to assess, the specific technology characteristics that enable the provision of those grid services, the specific technologies that possess those characteristics, and the generalized methodologies from the framework that could be used as a basis for calculating system value.

The next step is to determine any additional quantification methodologies that are needed, beyond the generalized methods from the conceptual framework, to calculate system values for the specific problem. While the conceptual framework is expected to provide general guidance and methods for calculating system value, this second step is expected to provide more detailed problem-specific guidance (e.g., quantification of the distribution system value of distributed storage).

The third step is to implement, or operationalize, the detailed methodologies within a set of modeling tools. This application could take the form of documentation of the general work flow that must be followed, both within and among the specific tools, and associated inputs, assumptions, and scenarios to be modeled to ensure the appropriate conduct of the detailed methodologies and generation of model outputs according to the assessment frame component specified in the conceptual framework. In some cases, extensions of existing tools may be needed (e.g., to input data associated with specific scenarios, regions, or reference grids, or to output specific system value metrics).

The fourth step is to conduct the set of model runs needed to produce outputs that address the set of technologies, grid services, and future electricity scenarios/regions/reference grids to be assessed.

The final step is to analyze, synthesize, and report these outputs to generate the desired system value metrics.

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Figure 9. Steps to system value metrics

Task 4.1 (Conceptual Framework) in the work plan will develop the initial version of the conceptual framework that is applied in the first step in Figure 10. Task 4.2 (System Value Calculation) will carry out all five of these steps with three different sets of modeling tools available in the Lab system, as well as apply the conceptual framework to report system value metrics from historical market data. One component of Task 4.3 (Missing/Supplemental Components) will apply the first step to non-market services, along with a robust literature review, to help inform the development of system value bounds. Finally, the first step will also be applied in Task 4.4 (Technology Characterization for Grid Services Provision), specifically the taxonomy component of the conceptual framework, to characterize the set of technologies of interest to the DOE Technology Offices. Implementation of the five steps throughout the project will generally focus on quantifying the value of the physical provision of a grid service (how much is it worth), and not consider new market designs or regulations that could be implemented to ensure that the estimated value is compensated for (how much it is paid), nor will it attempt to address revenue sufficiency for individual technologies. In particular, the valuation of grid services using recent market data in Task 4.2 may reflect distorting effects of out-of-market policies and subsidies; this work is a starting point for research and not the end in mind. The initial model-based estimates derived in Task 4.2 will be agnostic to these effects. The intent of Task 4.1 is to develop external consensus on how system values can be appropriately estimated. It is hoped that such consensus will contribute to acceptance of system value metrics estimates developed through the five steps. Further, implementation of the five steps will progress from initial estimates of the system value for grid services generally compensated for in current markets (bulk power system) to more refined bulk power system value estimates that incorporate more robust technology representations, a broader consideration of regional and system variation, and quantification of high priority non-market services. The initial set of system value metrics estimated in Task 4.2 is meant to be a draft implementation of the conceptual framework; these results and their limitations will be carefully presented in this context.

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A draft version of the conceptual framework, including all three of its components, will be developed within the first 6 months. The various components will then be used to inform work on the other three technical tasks. More specifically, Task 4.2 will apply the economic framework and assessment frame components to develop

initial estimates of system value metrics for market services. Task 4.3 will apply the taxonomy and the economic framework to develop system value

bounds for non-market services as well as identify detailed quantification methodologies for distributed PV and storage.

Task 4.4 will apply the taxonomy to characterize the set of technologies of interest to the DOE Technology Offices.

In turn, each of the technical tasks will provide feedback for these corresponding framework components that will be incorporated, along with stakeholder feedback, into the final version of the conceptual framework completed by the end of the first year. Literature review is expected to be a significant activity in Tasks 4.1, 4.3, and 4.4 and will encompass related prior and ongoing efforts in DOE, the academic community, and industry. The Task 4.1 review effort will focus on identification of valuation methodologies for market services. Task 4.3 effort will focus on identification methodologies used to value non-market services, as well as existing estimates of those values that can inform bounding estimates. Task 4.4 will focus on identification of values for specific characteristics of the technologies of interest to DOE. Given the potential overlap in literature reviewed for these efforts, these literature review activities will be closely coordinated across these tasks to avoid duplication of effort. In addition to informing specific task efforts, the literature review will identify significant points of leverage for this project, as well as remaining knowledge gaps, so that project efforts can explicitly add to knowledge rather than duplicate previous efforts. The complete set of major project activities is summarized for each major task in the Table 2. In addition, activities that expected to involve significant interactions with the Technology Offices (TO) and external stakeholders (SE) are noted in the table. In particular, close coordination with Technology Offices (TO) is expected concerning all conceptual framework components in Task 4.1, the determination of the representative technologies that will initially be used in Task 4.2 for estimation of system value metrics, as well as all activities in Task 4.4 (characterization of technologies of interest to DOE).

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Table 2. Project activities for Task 4

Task Activity Task 4.1: Conceptual Framework

• 4.1.1 Develop a system value taxonomy that defines and relates system attributes, grid services, and technology characteristics o Identify and define the specific grid services that support reliability and resilience and their

sources of cost and value (TO) o Identify and define the characteristics that enable a technology to provide a grid service, along

with approaches for quantifying the characteristics (TO) o Relate the technology characteristic to the specific grid services each enables, as well as to the

resulting supported system attributes (TO) • 4.1.2 Develop an economic framework to quantify the system value of technology characteristics

and grid services identified in the taxonomy o Develop a system valuation economic framework, identify existing best-in-class, state-of-the-art

generalized quantification methodologies to include in it, and identify an economic approach to ranking the importance of missing (non-market) grid services to reliability and resilience (TO)

o Identify guiding principles that ensure the effective application of system value quantification methods (TO)

• 4.1.3 Develop an assessment frame for reporting system value metrics that recognizes system and regional variation for current and potential future systems o Identify an aggregated set of system value metrics that are applicable across the widest range of

technologies, grid services, and potential values (TO) o Identify specific future electricity scenarios/reference grids, addressing potential regional

variations in systems, technology mixes, and service requirements, for which to estimate system value metrics (TO)

Task 4.2: System Value Calculation

• 4.2.1 Develop quantitative estimates of the value of a representative set of technologies in multiple regions of the U.S. based on 2017 wholesale market prices augmented to consider market depth and associated transmission infrastructure

• 4.2.2 Document, implement, and apply system value quantification methodologies specific to bulk power system grid services and technologies to estimate system value metrics o Document system value quantification methodologies that can be assessed in available system

planning and dispatch models o Apply these methodologies in one set of models to develop an initial set of system value metrics

for an initial set of future electricity scenarios/ reference grids (TO) o Develop Phase 2 work plan options to update methodologies, tool applications, and system value

metrics, as well as further develop tools (TO)

Task 4.3 Missing/ Supplemental Components

• 4.3.1 Develop and apply simple methods and literature to estimate system value bounds for non-market grid services based on the taxonomy developed In Task 2 o Identify and define missing/supplemental components o Develop and apply simple methods and literature to estimate system value bounds for these

components for specific technologies (TO) o Develop Phase 2 work plan options to research quantification methodologies for a prioritized

subset of missing components informed by the economic framework developed in Task 2 (TO) • 4.3.2 Develop distribution system value quantification methodologies for distributed PV and storage

consistent with the economic framework developed in Task 2 (TO)

Task 4.4: Technology Characterization for Grid Services Provision

• 4.4.1 Work with DOE Technology Offices to develop estimates of current and projected values of the characteristics of technologies of interest, including updating traditional cost information (e.g., capital, O&M, fuel), by applying the taxonomy developed in Task 2 o Develop a current characterization of each technology of interest for its existing state (TO) o Develop projected characterizations of each technology of interest for its potential future states,

through the additional hardware or improvement from RD&D (TO)

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3.5 Task 5: Expand resilience study to include cyber-, physical-, and weather-based events, various stress scenarios, and thermal

and rotational inertia.

3.5.1 Expanded Resilience

This sub-task will develop method to quantify impacts/risks from cyber-, physical-, and weather-based events. The framework developed to quantify impacts/risks from cyber-, physical-, and weather-based events will be benchmarked/calibrated using data from actual events, and subsequently used to quantify the value of the resilience provide by light water reactors. The analysis will be performed using either the regional or national large area electric grid models developed in the previous tasks, selected to be consistent with the characteristics of the cyber-, physical-, or weather-based events considered in the analysis.

3.5.2 Inertia

This sub-task is to develop a systematic method to quantify inertia from nuclear generators from a thermal and rotational perspective and represent these aspects using high-fidelity digital representation in the simulations. A reference test system based on actual system data is to be developed to accurately represent the physics of the thermal plant, and a real-time model that is representative of a large area electric grid will be used to understand the behavior of the integrated system to quantify the value of inertia provided under various realistic conditions of operation. The scope of this phase is to formulate a systematic approach and demonstrate through preliminary simulations to quantify inertia in a representative power system through some practical scenarios that can trigger the requirement of fast inertial response in the electric grid for sub-second and seconds to minute time-scales.

A representative electric power system is modeled and studied using high fidelity digital real-time simulations for accurate assessment of rotational and thermal inertia. The need for a systematic assessment and theoretical quantification of both rotational and thermal inertia imparted by nuclear and thermal power plants is identified as a key area of future research. Electromagnetic transient models will be used to represent power systems and its components, and mechanical mass representations for rotating mass from turbines and generators are interfaced in digital real-time simulations. The effect of inertia is examined through real-time simulations of typical disturbances in a wide area electric grid with a representative power plant. Comparison of inertial response from a representative nuclear generator is compared at varying inertia levels for quantifying the effect of rotational inertia on grid stability. Inertia from rotational masses and corresponding effect in electric grid is observed for practical events that occur in the grids. Varying degree of total inertia are modeled in the system to provide insights into the transient stability aspects.

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4. Outcomes and Deliverables

The following high-level outcomes from this project are expected by its conclusion, assuming funding is available to conduct all tasks. An evaluation of the economic viability of nuclear across a range of scenarios

representing different combinations of technology, economic, and policy conditions. The scenarios evaluated will allow an assessment of the relative importance of different cost, revenue, and policy factors to future deployment of advanced nuclear reactors.

Analysis of various combinations of reduced capital costs, favorable policy conditions

(e.g., climate policy), and additional revenue streams for other services and products that are more likely to create conditions under which significant new deployment of nuclear reactor technology will occur.

Evaluation of the quantities of additional revenue beyond that received from bulk energy

sales, for example from process heat, energy storage and fuel synthesis (e.g., hydrogen production), district heating, or other opportunities necessary to maintain economically viable operation.

Discussion of the importance of policy and market environments in driving new nuclear

deployment. The proposed deliverables are listed in the following Table 3.

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Table 3. Milestone and deliverables for projects over the next three years

Milestone Name/Description End Date Type

Task 1: Study Defining

The deliverables for this task are incorporated into other four tasks as progress reports.

Task 2: Resilience Study and Market Analysis at an selected region

Documentation of defining the attributes and characteristics of resilience, the study scope and boundary, the historical data needed for modeling, and the scenarios for simulation.

3/31/2019 Progress Measure

Report the market and economic impacts of fuel diversity at an ISO/RTO with significant nuclear power presence at different weather scenarios.

9/30/2019 Annual Milestone

Task 3: Examining Social and Environmental Benefits of NPPs

Documentation of defining benefits of NPPs on society, environment, security, and defining methodology for avoiding emission and damage.

9/30/2019 Progress Measure

Report the evaluation of climate, air quality, and security benefits of NPPs at an ISO/RTO with significant nuclear power presence. 3/31/2020 Annually milestone

Task 4: System Value of Beyond LCOE

Documentation of conceptual framework 3/31/2020 Progress Measure

Documentation of calculation of system value 9/30/2020 Progress Measure

Report on Development and application of a framework capable comparing the system value of grid services 3/31/2021 Annual Milestone

Task 5: Study on Expanded Resilience and Inertia

Research roadmap identifying modeling and analysis needs for expanding study from reginal to national for an expanded study. 3/31/2021 Quarterly Progress

Measure

Report on expand resilience study including cyber-, physical-, and weather-based events, and thermal and rotational inertia

9/30/2021 Annual Milestone

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5. REFERENCES

1. American Public Power Association (APPA) 2017. Wholesale electricity markets and regional transmission organizations. https://www.publicpower.org/system/files/documents/wholesale_electricity_markets_and_regional_transmission_organizations_1.pdf

2. Berkeley, A.R. and M. Wallace, 2010. A Framework For Establishing Critical Infrastructure Resilience Goals: Final Report and Recommendations by the Council, 2010

3. Bloomberg New Energy Finance. The 2018 Sustainable Energy in America Factbook. https://data.bloomberglp.com/bnef/sites/14/2018/02/Sustainable-Energy-in-America-2018-Factbook.pdf

4. Borders T. and R. Szilard. 2018. Position paper evaluating and prioritizing nuclear energy market attributes. INL/EXT-18-45063.

5. Borenstein, S. 2012. The Private and Public Economics of Renewable Electricity Generation. Journal of Economic Perspectives 26 (1):67–92. https://doi.org/10.1257/jep.26.1.67.

6. CRS 2016. Financial challenges of operating nuclear power plants in the United States. https://www.everycrsreport.com/files/20161214_R44715_e13f9da7116c0368451dd56ac6f1c729b593d21c.pdf

7. Davis, L. and C. Hausman. 2015. Market impacts of a nuclear power plant closure. https://ei.haas.berkeley.edu/research/papers/WP248.pdf

8. Department of Energy (DOE) 2015. United States electricity industry primer. https://www.energy.gov/sites/prod/files/2015/12/f28/united-states-electricity-industry-primer.pdf

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