The Use of System Dynamics in
Assessing Nuclear Energy System Futures
Luc Van Den Durpel
Abdellatif Yacout
Argonne National Laboratory
Nuclear Engineering Division
9700 S. Cass Avenue
60439 Argonne, Illinois
USA
daness@anl.gov
www.daness.anl.gov
Abstract
The role of nuclear energy in future sustainable energy systems is subject of many debates
worldwide. The assessment of nuclear energy systems asks for a multi-disciplinary look
into the development of nuclear energy according to the sustainability dimensions, i.e.
economics, environmental and socio-political.
Modeling the worldwide nuclear reactor park including all supply chain details, i.e. the
nuclear fuel cycle, demands for an integrated nuclear energy system model which also
includes feedback loops representing physical feedbacks within the system as well as, and
most prominently, socio-political feedbacks in the decision-making on the various available
deployment pathways for nuclear energy. Despite the availability since the early 1960s of
detailed model-codes for nuclear reactors covering physic, supply chain and economic
aspects of nuclear energy, development of a truly system dynamics view on nuclear energy
development only recently gained worldwide interest.
Argonne National Laboratory (ANL) started in 2000 with the development of such
integrated nuclear energy system models, i.e. DY MOND and more recently DANESS. These
models are based on system dynamics modeling used in various industry sectors and
allowing modeling the full mass-flow chain of time-varying mixes of nuclear reactor plants
and associated fuel cycle options. Several other sub-models are coupled to the mass-flow
kernel calculating heat loads, economics, life cycle inventory, and several other parameters
and feedback decision-making loops important in the assessment of nuclear energy futures.
This paper will bring an overview on the need for a system dynamics view on nuclear
energy system development, the role that system dynamics modeling may play in drafting
nuclear energy system scenarios and the modeling of such system dynamics view of nuclear
energy systems in the DANESS-code developed using IThink.
Introduction
Various technical and economic studies have been undertaken in the past few years drafting
possible pathways for the development of nuclear energy systems in a sustainable energy future
context [1-7]. Most of these studies were technology roadmap studies focusing on the technical
capabilities that are or will become available to society in order to advance the use of nuclear
energy as a safe, environmental friendly and economic source of energy.
While these technical and economic studies are of highest importance to advance nuclear energy
development through international collaboration, only a few studies have looked into the
deployment of nuclear energy systems as a whole, i.e. covering the synergistic effects that may
be exploited in designing future nuclear reactor parks as well as incorporating the environmental
and socio-political dimensions in the decision-making process on sustainable energy futures.
Indeed, addressing the market potential for nuclear energy in various market environments and
taking into account all decision criteria or key performance indicators (KPIs) for nuclear energy
demands a system dynamics view on nuclear energy as an integrated system. Figure 1
schematically shows the different layers of modeling needs to describe in varying detail
(nuclear) energy systems.
Figure 1. Schematic view of three layers in energy assessment models development.
The technical analysis layer focuses on the detailed technical description of the various
components of nuclear energy systems, i.e. nuclear reactors and fuel cycle facilities, according to
reactor physics, fuel composition changes, safety analysis, radiation protection, economics, and
other aspects of these components. A large set of such detailed technical analysis and simulation
codes have been developed in the past and some are made available to the international nuclear
community through international organizations [8-9].
The second layer consists of integrated process models allowing performing scenario analysis of
nuclear energy systems with a main focus on the technical dimension. These codes cover the full
supply chain and mass-flow transfer function of one or of a limited set of nuclear reactors in
order to give a detailed assessment of important physical parameters or a limited set of economic
parameters (e.g. levelized energy generation cost). These codes are also essentially based on
dedicated simulation software tools.
The third layer involves the description of nuclear energy systems in terms of sustainability
metrics used in policy-support and decision-making on energy policies. While many energy
market-penetration models, e.g. ENPEP [10] and MARKAL [11], have been developed in this
third layer, a new set of system dynamics and agent-based models are being developed to cover
the whole spectrum of sustainability dimensions which is not always the case in these energy
market-penetration models.
The use of system dynamics in the modeling of nuclear energy system futures covering the three
layers as given in figure | is motivated by following considerations:
e Synergistic effects between nuclear reactors and their fuel cycle allow to improve the
overall performance of nuclear energy systems which may not be analyzed using
existing codes detailing one or only a limited set of nuclear reactors and fuel cycle
options, i.e. a systems thinking perspective is therefore appropriate;
e Decision-making on nuclear energy system development has to take into account all
criteria used by the stakeholders and modeling these decision-making processes, through
feed-back loops, is crucial in understanding the dynamics of such nuclear energy system
development in a broader energy policy context;
¢ Communicating the potential of nuclear energy in sustainable energy development asks
for a transparent model environment which is supported and trusted by all stakeholders,
i.e. from researchers until policy-makers;
e The dynamics of nuclear energy deployment is of highest importance due to the inherent
long time-delays in developing the necessary technology as well as all (in)direct effects
of intermediate stocks or processing of nuclear material that may have a decisive impact
on the decision-making on nuclear deployment;
e The time-dimension in economic and environmental aspects of nuclear energy is very
important due to the long technical and economic life-times of the facilities involved and
the variety of environmental impacts to be assessed. This time-dimension needs
explicitly treated by any integrated process model of nuclear energy systems and this is
not always the case in existing codes;
e Allowing scenario analysis, i.e. what-if analysis, in a transparent and quick way is very
important to create a deep understanding of nuclear energy system development
dynamics by all stakeholders. This demands a nuclear energy system model allowing
calculating 100-year time-span simulations on modern PCs in less than a few minutes in
order to become an interactive learning tool for all stakeholders involved;
e Finally, uncertainty and sensitivity analysis is very important to investigate the main
dependencies of results on input parameters and to calculate the propagation of
uncertainty distributions throughout the model.
Argonne National Laboratory has recognized early in 2000 the need to develop such new
simulation systems covering the whole spectrum of facets in mapping the development
pathways for nuclear energy. The DYMOND [12] and DANESS [13] codes are the result of this
effort and the DANESS-code will be described in some more detail in what follows.
DANESS, i.e. Dynamic Analysis of Nuclear Energy System Strategies [13]
DANESS is composed of different interconnected sub-models each of those intended to perform
a specific part of the system dynamics simulation (i.e. lower and second layer in figure 1).
Figure 2 shows the overall architecture! of the DANESS-model where the following sections
will detail the sub-models.
Figure 2. DANESS-architecture
Exogenous defined nuclear energy demand scenarios are given as input to the current DANESS-
model. Starting from an existing reactor park, the model aims to match this demand by
generating energy with a varying mix of reactors. New reactors will be ordered depending on
their technological readiness level, their economic performance, pre-set user preferences for
reactor park fractions, and fissile material availability. The DANESS-model allows using
economic decision making where the type of reactor to be ordered at each moment will be based
' The figure only shows the main interconnections between the sub-models.
on the bus-bar cost of generating electricity. The user may also set a preferred distribution of
new reactor types upon which the DANESS-model aims to follow this preferred park
composition as long as fissile material availability allows. The economic decision making sub-
model will prefer investment in those reactor and fuel cycle combinations that render the full
bus-bar cost of energy generation minimal. The full bus-bar cost accounts for capital costs,
O&M costs, fuel cycle costs, externalities, taxes and any other cost considered by the user. Each
reactor type follows a technological development path (according to 9 technological readiness
levels) and each reactor is traced from ordering until shutdown and decommissioning. Up to 10
different reactor types may be simulated in parallel. Each reactor type may have different
characteristics such as different fuel types, thermal characteristics, licensing and construction
times, costs, and these may also vary over time.
The fuel cycle mass-flow model incorporates 21 fuel cycle steps, ranging from mining until
disposal and calculates other quantities such as fresh fuel needs, interim stored spent fuel,
separated actinides inventories and their isotopic composition. Up to 10 different fuel types may
be simulated in parallel. Each fuel type may have different characteristics and different paths to
follow through the fuel cycle where these may vary over time. Cross-flow of fissile material
between these fuel types is possible and the allocation of fuel type to reactor type may be
function of time. For instance, a light-water reactor (LWR) may start as uranium-oxide (UOX)
fuel-loaded and may switch to partial mixed oxide (MOX) loading as soon as enough plutonium
is available.
Fuel cycle facilities also have different characteristics and several technological options per fuel
cycle step are available. The user may choose, for instance, that UOX fuel is reprocessed using
aqueous reprocessing technology, MOX using advanced aqueous and metallic fast reactor fuel
using dry reprocessing technologies. These technologies may have different loss fractions (per
element), different transit times, costs, etc. Again, each fuel cycle facility that is considered in
the simulation follows a life-path from ordering until decommissioning where the expenses at
each moment are traced. The technologies follow a technology development path covering 9
technological readiness levels where the duration of each step may be different among
technologies and may vary over time.
All the costs, licensing and construction times, technology development steps, loss fractions, and
others may be experiencing learning curves with individual learning curve coefficients.
The DANESS-model checks the availability of fissile material for the different fuel and reactor
combinations and will order new fuel cycle facilities or will change the reactor park fractions or
fuel cycle options according to the decision criteria set forward by the user. The user may also
choose to define a fuel cycle facility deployment scenario. If economic decision-making is used,
the DANESS-model will try to follow the path forward leading to minimal bus-bar costs.
A uranium price sub-model may be used to vary the uranium price as function of the depletion
of natural uranium resources. This sub-model takes account of expected exploration expenses,
already mined uranium and expected uranium resources availability. Depending on the size of
the region simulated, the depletion of the available uranium resources will be calculated taking
into account the needs of the rest of the world.
Flexible approach of ‘allocation-matrices’
A flexible approach in the DANESS-model is the use of ‘allocation-matrices’ between reactors,
fuels and fuel cycle facilities. This allows the application of a systems-thinking approach in its
broadest sense, i.e. each combination of reactor, fuel, fuel cycle facility and fuel cycle option
may be simulated and these combinations are conditioned by multiple feedback loops. Choosing
the correct characteristics for reactor, fuel and fuel cycle facilities remains the responsibility of
the user. This approach also allows to pre-set DANESS for certain applications, i.e. freezing the
‘allocation-matrices’ to a certain scenario allowing to configure DANESS for use by less
experienced DANESS-users within specific constraints.
This approach is based on an uncoupling between reactor and fuel types, as well as between fuel
types and fuel cycle facilities. This allows the 10 different fuel types being combined with the 10
different reactor types according to technical constraints defined by the user and this
combination of reactor and fuel types may evolve as well over time. The same applies to the
combination between fuel type and fuel cycle facility. Figure 3 shows this basic uncoupling of
the three dimensions: reactors, fuels and fuel cycle facilities.
Figure 3. The basic model approach of DANESS allows a high degree of flexibility in
combining reactor types, fuel types and fuel cycle facilities.
FF.
Fuel Facility Combiaastion matrix
For example, a reactor initially loaded with UOX may gradually be loaded with MOX where the
MOX-fuel will use MOX-fabrication plants instead of UOX-fabrication plants and may
therefore need the construction of such a fabrication plant. The fraction of reactor cores being
MOX-loaded may then become a function of available MOX-fabrication capacity, the amount
of spent UOX-fuel that can be reprocessed and other variables such as economic performance or
others.
This approach allows a high degree of flexibility to simulate whatever nuclear energy system the
user wants to consider and allows analyzing the symbiosis between different reactor systems.
For instance, if less than 10 reactor types or less than 10 fuel types are to be simulated the model
will set the remaining ‘non-active’ reactors and fuels on-hold. Another example involves the use
of fuel types that may follow different fuel cycle paths. For instance, for fast reactor driver and
blanket fuel, the user may specify that the driver fuel is reprocessed by dry reprocessing
techniques (using the attributes for dry reprocessing) where the blanket fuel is reprocessed by an
aqueous process with different attributes (losses, transit time, costs ...). Reactors, for instance
LWRs, may be considered changing fuel loading over time, e.g. UOX to partial MOX-loading
or may change conversion ratio for fast reactors needing different fuel types for the different
conversion ratios. As indicated, it’s up to the user to define the technically possible and intended
combinations between reactor types, fuel types and fuel cycle facilities.
By using the three defined matrices, as shown in figure 3, the user may construct any kind of
nuclear energy system as long as, of course, the technical attributes of reactors, fuels and fuel
cycle facilities match in reality. The matrices themselves are calculated during the simulation
and may - if the user allows - change over time as several decision and feedbacl loops relating to
mass-flow, economics or other considerations apply.
Brief Description of the sub-models in DANESS
Energy-demand scenario model
The DANESS-model is an energy-demand driven model of nuclear energy systems where the
energy demand scenarios are exogenously defined. Energy demand scenarios may be selected
from ILASA/WEC [14] or other studies, exponential growth scenarios or custom defined by the
user. The nuclear energy demand may be decomposed in electricity, water desalination,
hydrogen and other process heat demand. Each of the reactors may be parameterized to deliver a
combination of these energy demands.
The energy market penetration of nuclear energy compared to other energy conversion
technologies (gas, coal, renewables) has been simulated using the ENPEP/BALANCE code [10]
and DYMOND/DANESS. While it is possible to include non-nuclear energy conversion
technologies in DANESS, this more general energy market penetration modeling is currently not
considered for implementation in DANESS as other more elaborate codes exist for this energy
market modeling.
Rest of World model
Depending on the geographical region that is being considered, this model will include the
corresponding energy demand scenario data for the rest of the world in order to calculate the
depletion of natural uranium resources next to other macro-economic parameters. Obviously, a
simulation of the worldwide nuclear energy system will imply that the data in this model
become zero.
Energy utility strategic model
The DANESS-model does not simulate individual utilities but simulates the general behavior of
the utility sector over longer time periods. The question to produce energy according a certain
energy demand will result in an increasing demand for investments in new generating capacity
and thus an increasing demand in funds to finance this growth. Any new investment in
generating capacity will therefore be evaluated on its net-present-value and the market value-
added that it will bring to the utility sector. The net-present-value of an investment is influenced
by the financial situation of the utility sector but is also influenced by governmental action
through tax regulations, funds, price premiums, etc. This sub-model grasps the essence of this
financial evaluation of utilities and incorporates the essential decision drivers for utilities to
invest in new (nuclear) generating capacity.
Reactor and Fuel Cycle Facility Technology development models
Each new reactor type or fuel cycle facility has to emerge from R&D-activities and will evolve
in technological readiness according a specific development path, in general a typical S-curve
covering in total 9 steps. This sub-model traces the technological readiness level for each reactor
type and fuel cycle facility and incorporates elements that may influence the development path.
For instance, preferential government funding may accelerate certain developments, demand
from market, ... The output of this sub-model is a list of available technologies that may be
ordered in the new capacity decision model or new fuel cycle facility decision model.
New Reactor Capacity Decision model
The need to invest in new generating capacity is driven by the projected shortage of energy
generation by the existing park and by shutting-down existing nuclear capacity. From the set of
technologically available reactor types and constrained by fuel cycle mass-flow and financial
considerations this model will simulate the decision-making process to invest in new capacity
and this based on a generic market penetration model.
New Fuel Cycle Facility Capacity Decision model
In analogy with the above, this model simulates the investment in new fuel cycle facilities. New
facilities will be constructed based on the projected need for fuel cycle operations and the
foreseen shutdown of older facilities as well as the technological availability of the intended fuel
cycle facility. The user may also decide to specify a given facility deployment scheme. This
model excludes financial constraints to invest in fuel cycle facilities and only gives the
cumulative investment needed to develop a certain nuclear energy system. It is considered in this
version that existing fuel cycle companies will, anyhow, build the requested fuel cycle capacity
or that new fuel cycle companies would emerge as the demand for fuel cycle services grow. No
limiting feedback loops are currently implemented in this model.
Reactor and Fuel Cycle Facility History models
Once reactors and/or fuel cycle facilities are ordered they will follow a certain life-cycle which
may differ between reactor types and between fuel cycle facility types. These two models trace
the different steps in the life-cycle of reactor and fuel cycle facilities respectively and feed back
to the above mentioned decision models on available capacity, projected capacity in the nearby
and long-term future and the replacement capacity to be foreseen.
Two decision-moments are explicitly included in the reactor history model, i.e. the decision to
order a new reactor and the decision to start commercial operation of a reactor. The first decision
is, as said before, triggered by the forecasted energy generation shortage by the nuclear energy
system compared to the demanded nuclear energy. The second demand is triggered by the actual
balance of generation and demand and influences the average capacity factors of reactors.
Fuel cycle mass-flow model
The fuel cycle mass-flow model is the kernel of the DANESS-model and treats all aspects of the
fuel cycle for different fuel types according to 21 possible fuel cycle steps. All fuel cycle
operations, including stocks and delays, are modeled where feedback of fissile material from
reprocessing a certain fuel into another type of fuel is made possible. Up to 10 types of fuel may
be followed in parallel.
Fuel cycle costing model
This model calculates the levelized fuel cycle costs for the different reactors and fuel types. The
levelized fuel cycle cost takes into account the different timing of operations for each fuel batch
of a certain fuel type and recombines these costs on reactor level using one or several types of
fuel. This sub-model also calculates the net-present-value for the whole fuel cycle for a reactor
type. This latter calculation is used in investment appraisals for new reactors.
U-Price model
The evolution of the price of natural uranium is an important given for long-term nuclear energy
system optimization. This model of the evolution of the uranium price takes into account the
exploration for new natural uranium resources, the depletion of existing mines and the long-term
supply and demand balance. It does not simulate short-term (spot-market) price evolutions but it
does provide a model of this uranium price. The price is finally provided as input to the fuel
cycle costing model.
Energy costing model
In analogy to the fuel cycle costing model this model calculates the actual and net-present-value
of the energy produced by each reactor type. The energy cost is divided in a capital cost term, an
O&M cost term and a fuel cycle cost term. Each of these cost terms is calculated and feeds the
new capacity decision-making model for investment appraisals of new reactors.
Government role model
The government may have different mechanisms through which the development of a nuclear
energy system may develop. Government may influence the spent fuel charges, tax rates on
investments, risk-premium reductions for new technologies, R&D-funding, and more. This
model allows the user to simulate the long-term policy impacts of changing these indirect means
of governmental action and their impact on the various other sub-models of DANESS. The full
functionality of this model is only available in the source-code developer’s version of the code
as this model may need customized settings for local (national) policies.
Costing models
The previous costing models include capital, operation & maintenance and fuel cycle costs as
well as assessment of costs associated to licensing, owner’s cost, decommissioning and others.
For each of these cost-categories, more detailed cost models may be implemented and some of
these cost models are considered for implementation in the code.
Isotopics and Waste management
The latest version of DANESS includes the follow-up of the isotopic composition of fuels. In
total some 23 isotopes for actinides and some selected fission products are followed allowing
calculating the impact on various nuclear fuel cycle facilities, especially on waste management.
A specific model is implemented to calculate the heat load of the potential US geological
repository Yucca Mountain and thus to calculate repository capacity expansion possibilities
through reduction of actinides and some fission products confined in the waste.
Life Cycle Analysis
Life cycle analysis (LCA) functionality is under development to address the environmental
dimension of nuclear energy in more detail. This LCA-model shall trace the (in-)direct
emissions from nuclear energy systems in some detail allowing to perform post-processing using
specific LCA-models developed by international research organizations.
Multi-regional nuclear energy systems
An additional dimension in the DANESS code consisting of simulating in parallel up to six
world regions each with its own nuclear energy system deployment scenario is currently being
developed aiming to address specific issues in worldwide nuclear energy system deployment.
Sensitivity/Uncertainty analysis
Based on IThink’s possibilities to perform sensitivity and uncertainty analysis, DANESS offers
the possibility to perform various sensitivity/uncertainty analyses on various parameters. This
functionality is rather unique among the various integrated nuclear energy systems under
development today.
DANESS MS-Access Attributes database.
Input of the more than 2000 parameters for a simulation is performed through an easy-to-use
MS-Access database allowing information retrieval from previous simulations, archiving
technology characteristics (reactors, fuels, fuel cycle facilities and others), and organizing all the
data needs for a simulation. The DANESS-model includes graph-functions and allows
transferring all the results of a simulation into a template MS-Excel file. This template-file also
allows automatic graphing of the results and facilitates comparison between simulations. The
link between the database and the model can be automatically updated but is not necessary if the
initial data does not change between different simulations.
DANESS-Output MS-Excel template
The results of DANESS-simulations are available in MS-Excel format enabling a smooth and
fast visualization of the results. Graphs of the main variables in the simulation are automatically
drafted where the format of the output-files also allow direct comparison between different
simulations.
Model-environment of DANESS
DANESS is build using system dynamics software, i.e. [Think [15]. DANESS consists of about
8 200 relations between the approximately 16 000 variables, 2 600 stocks/conveyors and about
6000 flows. The model-file is 39 Mb large. A 100-year simulation in one-month time-steps
covering typically a world nuclear reactor park takes less than a few minutes on modern PCs
which is a very important criterion when DANESS is used as on-line simulation code for
training and educational purposes as well as for uncertainty/sensitivity analysis.
Benchmarking and validation of DANESS
The validation of DANESS through benchmarking is a multi-year activity covering different
disciplines and organized in cooperation with various DANESS-users. There are multiple facets
to the validation of this kind of large integrated nuclear energy systems model code:
e Sub-model validation: the concept of DANESS as an integrated nuclear energy system
model covering various facets, i.e. mass-flow up to economics and life-cycle analysis,
makes that the validation of such a code asks for a multi-phased approach, i.e.
o Validation of individual sub-models whenever another comparable model is
available. The mass-flow calculations in DANESS are verified using well-
known and internationally validated/benchmarked codes such that DANESS
makes use of best knowledge and characterization of reactors and fuels
available. The mass-flow analysis in DANESS is then validated by use of test-
cases reported in other studies or, if possible, by blind benchmarks with
comparable mass-flow codes.
o Isolation of various sub-models for validation when no comparable model is
available. Some sub-models in DANESS do not have an analogue which is
readily available for validation/benchmark purposes. This is the case for the
government role model, accounting model and U-Price model. These models
have been derived from other studies or models without having these tested in
comparable integrated model situations. The validation of DANESS in these
cases is based on isolating these sub-models and verifying their performance
using some idealized and well-characterized cases. The interaction between
these sub-models with the other validated sub-models is then once again tested
in some simplified and well-characterized cases. However, comparable codes for
so-called blind benchmarking are not available and this limits the full validation
of DANESS.
Full model validation: two codes have been developed by ANL, i.e. DYMOND and
DANESS. The main difference between these two codes is that DYMOND is a
customized model for the US nuclear energy park and is also a more continuous model
where DANESS is fully parametrical and is based on some discrete modeling of certain
sub-models (mass-flows, loading patterns in reactors, fuel cycle infrastructure expansion
...). DYMOND does not included all sub-models of DANESS but allows to verify a
significant part of DANESS relating to mass-flow analysis, fuel cycle expansion
decision-making, ... and this essentially by user-specified deployment scenarios in
DYMOND which are recalculated by DANESS using the inherent decision-making
capability of DANESS.
Collaboration with other DANESS-users and other organizations having some comparable sub-
models is ongoing extending the validation of DANESS.
Use of DANESS
The use of DANESS is focused on scenario-analysis of different development paths for
nuclear energy systems and this from a governmental, utility or R&D perspective. Its
intended use comprises:
Analysis of development paths for nuclear energy: the impact of new developments
in nuclear reactor development and fuel cycle operations may be analyzed from an
integrated perspective. The impact on inventories in the fuel cycle, on costs of
energy generation, waste production as well as the level of compliance with
sustainability goals can be analyzed.
Integrated process model for the cost-benefit analysis of specific new technologies
(reactor or fuel cycle facilities) in order to guide the R&D or engineering design of
new facilities. In an early phase of R&D or system development project is the
evaluation of system performance and the projection of system costs crucial for the
economic evaluation of these projects. Early R&D stages as well as the pre-
conceptual design stage involve the design and costing of large-scale systems with
significant extrapolation of data obtained from the R&D-program. An integrated
process model (IPM) including mathematical representation of the key system
physics and using cost-scaling relationships enables to analyze the optimization
parameters for the industrial system and the economic viability of the final system.
¢ Parameter scoping for new designs: in analogy with the above use as IPM
DANESS can assist in analyzing the possible impacts of new technologies in
complete nuclear energy systems and help guide the R&D-effort identifying the key
development drivers for new technologies and the trade-offs between these
parameters. For instance, a trade-off will exist in multiple recycling fuel cycles
between the burn-up of the fuel and the separation yield in order to maximize the
net reduction of transuranics (TRUs) going to waste. Guidance in preferential key
R&D directions may be obtained through analysis of the impact of changing
technological characteristics of reactor and fuel cycle facilities (including learning
effects).
¢ Economic analysis of nuclear energy systems: utilities operating nuclear plants are
continuously striving to minimize the energy generation costs. DANESS can be
used as a tool to calculate today's and projected nuclear energy costs based on
different development scenarios of price, technical characteristics of plant, fuel
cycle operations and costs as well as impact of government regulation. This analysis
may be done on a short-term as well as on a long-term time horizon.
e Life cycle analysis (in preparation): Total life cycle ecological impact of the
proposed nuclear energy park development pathway is currently being modeled.
This modeling encompasses activities upstream of facility deployment, facility
operation, and downstream waste handling and decommissioning steps in the life
cycle.
¢ Government role: governments willing to analyze the role of nuclear energy in a
sustainable energy future may seek to analyze the possible policy-tools to be used to
influence the energy sector to (re-)invest in nuclear energy or to change the fuel
cycle option with a more sustainable but economic perspective. Facility for
exercising several policy options are included in DANESS, i.e. R&D-funding, tax
rates, price premiums, prototype funding, risk-premium reduction for investments,
¢ Educational use: the ability to simulate different fuel cycle scenarios and the impact
and role of different reactor types may facilitate the understanding of nuclear energy
systems in nuclear engineering education as well for the broader public, including
policy-makers. The architecture of the model facilitates the transparency of the
simulation results.
While this list is not exhaustive, it does indicate the type of applications where DANESS is
intended for. Again, the use of DANESS is not aimed at predicting the future but merely on
helping to project and analyze different nuclear energy development paths in a consistent
way.
Simple examples of applications
Two typical simplified examples of applications will be given to illustrate the diversity in
applications that may be addressed with DANESS.
Closing the Nuclear Fuel Cycle
The US-DOE has started the Advanced Fuel Cycle Initiative (AFCI) [5] aimed at developing
advanced fuel cycle technologies rendering the use of scarce natural resources and the arising of
highly radioactive waste to a minimum. This involves closing the nuclear fuel cycle for all
actinides thereby alleviating the continuous need for new repositories. A significant reduction, a
hundred-fold, of the radiotoxicity in the repository is also achieved allowing to drastically
shorten the long-term stewardship for such repositories by better managing the heat load and
radiotoxicity of the buried waste.
Several advanced nuclear fuel cycle scenarios are under consideration where a quick and
comparative analysis is needed to assess the different available options and to guide
decision-makers in prioritizing resources. DANESS is used by ANL to perform these kind
of comparative analysis between different advanced fuel cycles with respect to the mass-
flows and resulting stocks of fuel and separated materials as well as the resulting economic
picture for the government and utilities. An expected nuclear energy demand growth in the
US of 2%/yr after 2010 is used to illustrate the effects of the different fuel cycle scenarios
on the variables investigated. Figure 4 shows a typical result of the comparison of the
amount of spent fuel disposed in repository and the actinide inventory in the fuel cycle for
three scenarios. The three scenarios are nuclear phase-out, business-as-usual of the existing
reactor park with new orders of LWRs and a LWR+FR type of nuclear energy system with
FRs acting as TRU-burners. In this latter case, a user-defined reprocessing capacity
deployment scenario was used with 5 000 tHM/yr LWR-UOX reprocessing capacity in
place by mid-century and a doubling by the end of the century.
Figure 4. Comparison of the amount of spent fuel and high level waste to be disposed in
repositories for three US nuclear energy system scenarios. Right figure shows the
corresponding amount of actinides in the fuel cycle (i.e. out-of-reactor and out-of-
repository) for the three scenarios
DUPIC Nuclear Fuel Cycle Development
Those countries having a mix of LWR and CANDU reactors might consider developing the
so-called DUPIC nuclear fuel cycle. Spent LWR fuel is dry reprocessed through the use of
the OREOX process where the fabricated fuel is recycled in CANDU reactors. A DANESS
simulation, using unit costs as reported in literature [4], indicates the evolution of the
aggregated bus-bar cost of electricity generation for a reactor park of 12 LWRs and 4
CANDUs. Figure 5 shows the evolution of the amount of spent fuel over time for this
reactor park (assuming 60 years lifetime for reactors without new reactors being ordered).
The levelized fuel cycle costs for the fuel for LWRs are calculated as 5.9 mills/kWhe, for
CANDU-UOX 4.8 mills/kWhe and for CANDU-DUPIC fuel 6.8 mills/kWhe.
Figure 5. Spent Fuel Amount for a mixed LWR-CANDU reactor park with different fuel
cycle options, i.e. once-through and DUPIC.
Future developments
DANESS is currently used in different projects. Based on this experience feedback new
add-ins will be developed in the coming years. Developments currently under consideration
are:
e Extending the cost database and implementation of scaling laws for these costs for
reactor and fuel cycle facilities.
e Refinement of economic decision model by including market mechanisms and
specific models for financial parameters, e.g. risk premium, ...
e Integration of macro-economic energy balance.
Both ANL’s integrated nuclear energy system models DYMOND and DANESS have been
developed initially based on a technical description of nuclear energy systems with
subsequently integration of environmental and socio-political aspects of nuclear energy.
Interaction with other research groups in these fields, not essentially in the nuclear energy
domain, is sought in order to improve these environmental and socio-political aspects and
the decision-making processes in these fields.
Con
An
clusion
ew code DANESS for the dynamic analysis of complex nuclear energy systems has
been developed. This code allows the user to simulate all aspects of a varying mix of
react
‘tor and fuel types including the economic performance of such systems. The code is
based on system dynamics modeling and further expansion of the model to cover all
sustainability dimensions in the assessment of nuclear technology is envisaged.
The
strength of using system dynamics in this quite large nuclear energy system model
relates to the combined capability of performing detailed supply chain simulation while
also
incorporating decision-making feedback loops and all this in a transparent model
environment. This allows using the DANESS-model from a technical level up to an energy
policy support level while allowing incorporation of different decision-making feedback
loops in assessing energy policy options.
References
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(1
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