Nuclear Power Deployment Operation and Sustainability Part 3 potx

Assessment of Deployment Scenarios of New Fuel Cycle Technologies 59 consumption and avoidance of TRU at a given TRU CR. Certainly the rate of TRU consumption from the standpoint of an individual reactor depends on the reactor power and CR; however, from the standpoint of the entire fleet, the rate of TRU consumption and avoidance additionally depends on how fast TRU-consuming reactors (burner FR in this instance) displace TRU-producing reactors (LWRs in this instance), how quickly discharged fuel can be separated and recycled material re-inserted into reactors. Figure 17 shows the impact of increasing the “wet” storage time from 1 to 10 years for a 1-tier CR=0.50 fast reactor case, approximating a shift from onsite to offsite separation and fuel fabrication. The total time from reactor discharge to reinsertion changes from 2 to 11 years. Fig. 15. Waste, uranium, and fuel product mass for a 1-tier recycle case, CR=0.50 fast reactors, no packaging included. Fig. 16. Percent of RU and DU from LWRs used as fast reactor fuel with fast reactors and LWRs in static equilibrium. Nuclear Power – Deployment, Operation and Sustainability 60 Fig. 17. Long-term radiotoxicity of 1-tier fast reactor CR=0.50 with either 1 or 10 year “wet” cooling before a year of separation and fuel fabrication. 5.3 Uranium utilization To start, consider the range of estimates of world uranium resources in Table II relative to the 2006 production rate of 40,000 tonne-U. 19 The current nuclear power production rate would exhaust total estimated conventional resources (16,000,000 tonnes-U) in 400 years. That time scale can drop to within a century with modest nuclear power growth, but extend many centuries if unconventional resources become practical. Conventional resources Reference Tonnes-U Reasonably assured resource, <$130/kg-U Redbook 19 3.3e6 Inferred resources, <$130/kg-U Redbook 19 2.1e6 Prognosticated resources, <$130/kg-U Redbook 19 2.8e6 Speculative resources, <$130/kg-U Redbook 19 4.8e6 Total estimated conventional resources Above 4 categories, <$130/kg-U Above 4 categories, plus “cost range unassigned” Undiscovered + known, <$130/kg-U Undiscovered + known, <$130/kg-U Redbook 19 Redbook 19 Herring 20 Steyn 21 1.3e7 1.6e7 1.5e7 1.6e7 Unconventional resources Reference Tonnes-U Uranium in sandstone deposits Herring 20 1.8e8 Uranium in volcanic deposits Herring 20 2.0e9 Uranium from seawater Herring 20 4.2e9 Uranium in phosphate deposits Herring 20 8.0e11 Table 2. World Potential Uranium Resources Assessment of Deployment Scenarios of New Fuel Cycle Technologies 61 Nuclear fuel isotopes are either fissile or fertile; fissile isotopes are much more readily consumed. The only fissile isotope in nature is U-235, which is 0.7% of uranium ore. The only fertile isotopes in nature are U-238 (99.3% of uranium ore) and Th-232 (100% of thorium ore). To extend ore utilization substantially above 0.7%, one must convert or “breed” fertile to fissile isotopes. Fertile U-238 can be bred to fissile Pu-239, called the uranium-plutonium fuel cycle (or plutonium for short). Fertile Th-232 can be converted to fissile U-233, called the thorium-uranium fuel cycle (or thorium for short). The ratio of producing fissile isotopes (from fertile) to consuming fissile isotopes is called the fissile breeding ratio. A ratio greater than 1 means that more fissile isotopes are bred than consumed, shifting the limiting resource from fissile isotopes to fertile isotopes. All current U.S. reactors have fissile breeding ratio less than 1 and thus use less than 1% of the original uranium ore. Recycling in such reactors is not sufficient to break 1% because their fissile breeding ratios remain below 1. When the fissile breeding ratio is greater than 1, the uranium (or thorium) utilization can exceed 1%. There are exotic concepts in which maximize in-situ breeding without recycling used fuel, it advanced materials can be developed, these may achieve ~10% uranium utilization. With recycling of used fuel in breeder reactors, uranium and thorium utilization can approach 100%, subject to processing losses. Accomplishing 50-100x improvement in uranium utilization means near total replacement of LWRs (or other thermal reactors) with fast reactors. For example, if 10% of the reactor fleet remains LWRs with UOX fuel with 90% of the electricity from fast breeder reactors, the maximum uranium utilization improvement is 10x. Such substantial infrastructure change from LWRs to FRs is notoriously difficult. 22 As most of the U.S. LWR fleet is moving toward a 60-year reactor lifetime, such a replacement of LWRs will take at least 6 decades from the operation of the last LWR. As an example, if fast breeder reactor deployment requires 2 decades from first deployment to 100% of new construction (i.e. allowing 2 decades for industrial scale-up and market penetration); it will be 2120 before the last LWR retires. Predicting uranium resources so far in advance is questionable. The above example assumes that the fast breeder reactors can grow faster than nuclear power growth during its market penetration from 0 to 100%, followed by continued breeder growth at the nuclear power growth rate once it reaches 100% of new construction. The rate of breeder deployment is constrained by fuel supply, which we have tended to assume is transuranic material recycled from LWRs and fast reactors once operating, rather than high enriched uranium (~30% U235). We have derived the required TRU conversion ratio, such that LWR are not required to supply TRU to a growing fleet of fast reactors: () FR mt t CR e   (7) where m is the growth rate; F t is the time for cooling, separation, and fuel fabrication; R t is the time in reactor. Thus, FR tt  is the total turnaround time. As an example, if 0m  , then 1CR  and the system is in balance with no LWRs. Or, if one wants 0m  , then 1CR  . The higher the desired growth rate, the higher the required CR. In addition, because new fast reactors (growing at rate m ) must have 1 R t  additional years’ worth of fuel to start up, equation 1 must be multiplied by another term. 2 2 A core contains t R years worth of fuel, with 1 year’s worth added each year. At startup, there is therefore an extra t R -1 that must be provided. Nuclear Power – Deployment, Operation and Sustainability 62 () (1 ( 1)) FR mt t R CR e m t   (8) At a nominal growth rate of 1.75%/yr, the time lags in the system are important. If 2 F t  (example for onsite recycling) and 4 R t  , then 1.17CR  is required. If 11 F t  (example for offsite recycling) and 4 R t  , then 1.36CR  is required. Fig. 18 shows the required CR as function of desired growth rate and turnaround time. The minimum turnaround time is probably ~5 years (1-year cooling, separation, fabrication and 4 years in reactor). Fig. 18. Required fast reactor TRU conversion ratio at dynamic equilibrium, as a function of growth rate and turnaround, ignoring displacement of pre-existing LWRs or TRU stockpiles. The theoretical maximum CR is ~1.9 because Pu239 dominates fission in a fast reactor and it yields 2.9 neutrons/fission. One neutron must induce the next fission, leaving 1.9 to make more transuranic material from U238. 3 Neutron yields vary slightly by isotope, e.g., 2.4 for U235, 2.9 for Pu241, and 3.2 for Am242m, so the exact theoretical maximum could be slightly different than 1.9. Of course, as neutron leakage and neutron capture by fuel and non-fuel core material is accounted for, the practical maximum conversion ratio will be significantly lower than 1.9. For example, if that maximum is considered to be 1.5, then the maximum rate of breeder reactor introduction can be 4.7% with 6-year turnaround (onsite recycling), but only 2.3% with 15-year turnaround (offsite recycling). The holdup of transuranic material in the system impacts system performance so that short time lags, e.g., when facilities are co-located instead of at different locations, can lead to faster system evolution. 3 The theoretical maximum is actually smaller than 1.9 because some neutrons absorbed into fuel necessarily lead to (n, γ) reactions instead of (n,fission). However, some of the (n, γ) products and their successors will fission, so the reduction of the maximum below 1.9 is somewhat complicated and beyond the scope of this illustrative calculation. Assessment of Deployment Scenarios of New Fuel Cycle Technologies 63 5.4 Proliferation resistance and physical protection Barriers to acquisition of a nuclear weapon/explosive are called “proliferation resistance” for a host nation of nuclear facilities and “physical protection” for a subnational or terrorist group. An evaluation methodology should include the four stages toward a weapon – (1) diversion (if host nation) or theft (if subnational), (2) transportation, (3) transformation, and (4) weapon fabrication and indicate how the various indicators are to be combined. First, observe that although there is significant reduction of TRU relative to once through (avoided and consumed), there remains significant TRU material throughout a fuel cycle system. Figure 19 illustrates that there is substantial reduction of TRU material relative to once-through (via avoidance and consumption) but also that there is substantial TRU in many parts of the system. Fig. 19. Location of TRU material in a 1-tier recycle case. The second proliferation resistance observation is that the mass flow of material through separations can vary significantly both quantitatively and by type of separation, independent of separation efficiency. Figure 20 shows the total mass sent through separations (the sum of the flow tonnes-TRU/yr times the number of years) as a function of fast reactor conversion ratio for a 1-tier simulation; this figure keeps the fast reactor fuel constant (metal) with onsite processing. As CR increases, there are fewer LWRs hence less processing of used LWR fuel; but there are more fast reactors and more processing of fast reactor fuel. These may be of different technologies and the siting strategy could differ, e.g., large centrally located aqueous separation of used UOX fuel versus at-reactor electrochemical separation of used fast reactor metal fuel. In such cases, the proliferation risk posed by different technologies and locations would vary. The third proliferation resistance observation is that the recycled material composition will change significantly with time. Figure 21 shows evolution of the recycle mix as TRU material is repeatedly recycled, in this case as mixed oxide fuel in LWRs. 12 This calculation Nuclear Power – Deployment, Operation and Sustainability 64 uses heterogeneous inert matrix fuel (IMF) 4 to keep the material fissile, i.e., each recycle is a mixture of fresh UOX and IMF made with TRU recovered from the previous recycle. The figure shows that the Cm and Cf isotopes, which emit high numbers of neutrons, increase up to four orders of magnitude between the first recycle and equilibrium. Figure 21 compares MOX and metal fast reactor fuel (at CR=0.75, comparable to the CR of MOX) at the first and equilibrium recycle. Both MOX-TRU and FR-TRU evolve considerably from the first to the equilibrium recycle. FR-TRU has higher Pu content but lower amounts of the highest TRU isotopes (Cf) that tend to dominate neutron emission. Fig. 20. Total mass of TRU material sent through separations in 1-tier recycle case as a function of fast reactor TRU conversion ratio; metal fuel, on-site processing assumed. Figure 22 shows that MOX-TRU and FR-TRU vary little after the first recycle (square data points), with major differences only in the Cf isotopes. (Composition impacts many areas, not just proliferation and physical security.) At equilibrium recycle (circle data points), MOX-TRU and FR-TRU differ less than an order of magnitude below Cm244, about an order of magnitude from Cm244 to Cm248 and over an order of magnitude for the Cf isotopes. High gamma emitting isotopes are found throughout the actinide chain and therefore the total gamma comparison between MOX-TRU and FR-TRU is merely an order of magnitude. The highest neutron emitters are located at the top of the TRU chain and therefore the neutron emission comparison between MOX-TRU and FR-TRU grows over an order of magnitude. Still, both MOX and FR at equilibrium have higher gamma and neutron emission than either has at the start of recycling. The fourth and final proliferation resistance observation is that the quality of Pu does not change dramatically throughout the century. The quality of Pu measured as the fraction of 4 MOX fuel has U and one or more TRU elements mixed in each fuel pellet and fuel pin. A homogeneous IMF fuel has only TRU. A heterogeneous IMF fuel is a mix of IMF fuel pins and UOX fuel pins. Assessment of Deployment Scenarios of New Fuel Cycle Technologies 65 Pu-239 to total Pu in the system only changes from 0.55 (once through) to ~0.50 for the two recycle cases. Fig. 21. Isotopic mix for discharged MOX-TRU as a function of how many times transuranic material is. Transmutation data from ref. 16. Fig. 22. Isotopic mix for discharged MOX-TRU and FR-metal-TRU for first and equilibrium recycle. Transmutation data from ref. 12 and 16. 5.5 Economics In any area of technology, the cheapest situation occurs when raw materials are very low cost and one is allowed to just walk away from waste. As raw material cost increases, the incentive to recycle materials increase. As waste disposal costs increase, the incentive to reduce, re-use, and recycle increases. Nuclear Power – Deployment, Operation and Sustainability 66 Unsurprisingly, therefore, for nuclear fuel cycles, there are major uncertainties associated with the future cost of uranium (or thorium), any waste repository, and any new technologies (reactors, fuels, separation, waste forms) that may be involved. Were uranium and waste disposal inexpensive, it would be difficult to economically justify new technologies. The average cost of electricity from current U.S. nuclear power plants is less than $0.018/kilowatt-hour or 18 mills/kilowatt-hour (18 mills/kW-hr) because their capital costs have mostly been depreciated. Cost projections for new plants in the next decade range from 47 to 71 mills/kW-hr which include capital recovery. Fuel cycle costs are about 6 mills/kW- hr. Of this, 1 mill/kW-hr is the fee currently paid by U.S. utilities to the Federal government for future geologic disposal, covering projected disposal costs. To date, estimates of the cost of relatively traditional alternative fuel cycle options (most uranium cost increases, Yucca Mtn repository, and GNEP technology options) suggest uncertainties of a few mills/kW-hr, and possible increased cost (relative to once through) ranging from zero to a few mills/kW-hr, or 0-10% of total nuclear energy cost. The first is that dynamic versus static will impact economic assessments. A static quilibrium is appropriate when discount rates, the time value of money, and cash flows are not addressed. A dynamic equilibrium comes closer to cash flows if the time value of money is accounted for as costs that lead others are given greater weight; cash flows that lag others are given less weight. Table III lists key lead and lag items in dynamic equilibria. For example, one builds LWRs relatively early in the process of generating electricity; therefore, when time value of money is considered, the relative contribution of LWRs to total cost increases. Conversely, fast reactors and waste disposal are bought relatively late; therefore, their relative contribution to total cost decreases. Leading Purchase relatively soon Lagging Purchase relatively late Increase or decrease when shifting from static to dynamic equilibrium Increase, hence factor might be more important than predicted by static equilibrium Decrease, smaller impact than might be predicted by static equilibrium Material inputs Natural uranium Depleted uranium Enriched uranium Zirconium and steel Types of reactors Number of thermal reactors using uranium oxide fuel Number of fast reactors Thermal efficiency increases Types of facilities Fabrication plants Separation plants Material output Waste disposal Table 3. Lead and Lag Items in Dynamic Equilibria The fraction of fast reactors in time will be much lower than predicted by simple “static equilibrium” calculations due to multiple system constraints that impact the amount of TRU available for fueling new reactors at startup. This is illustrated in figure 23. Assessment of Deployment Scenarios of New Fuel Cycle Technologies 67 Fig. 23. Fraction of electricity generated by fast reactor at dynamic equilibrium (near 2100) as function of fast reactor TRU conversion rate and nuclear electricity power growth rate, calculations assumed metal fuel and onsite processing. The final observation is that fuel and separation facilities must accommodate variation in fuel mixture elemental composition. This composition will vary as reactor type, fuel type, burnup, aging of used fuel, number of recycles, separation purity, etc. 6. Acknowledgments This chapter was prepared for the U.S. Department of Energy Office of Nuclear Energy, Science, and Technology under DOE Idaho Operations Office Contract DE-AC07-05ID14517. 7. References [1] U. S. Department of Energy, Office of Nuclear Energy, Science, and Technology, Report to Congress – Advanced Fuel Cycle Initiative: Objectives, Approach, and Technology Summary, May (2005). [2] U. S. Department of Energy, Office of Nuclear Energy, Science and Technology, Advanced Fuel Cycle Initiative (AFCI) Comparison Report, FY 2005, May (2005). [3] U. S. Department of Energy, Office of Nuclear Energy, Science, and Technology, Advanced Fuel Cycle Initiative (AFCI) Comparison Report, FY 2006 Update, July (2006). [4] J. J. Jacobson, A.M. Yacout, G.E. Matthern, S.J. Piet, D.E. Shropshire, R.F. Jeffers, T. Schweitzer, “Verifiable Fuel Cycle Simulation Model (VISION): A tool for Analyzing Nuclear Fuel Cycle Futures”, Nuclear Technology, Volume 172, Number 2, November 2010. [5] S. J. Piet, “Selection of Isotopes and Elements for Fuel Cycle Analysis”, Advances in Nuclear Fuel Management IV, April 12-15, 2009. Nuclear Power – Deployment, Operation and Sustainability 68 [6] J. W. Forrester, Principles of Systems, Wright-Allen Press, Inc, 1971. [7] Powersim Software AS, Bergen, Norway, www.powersim.com. [8] J. Grouiller, G. Glamenbaum, B. Sicard, M. Mus, J. Martin, J. Devezeaux de Lavergne, O. Comellini. COSI, A Simulation Software for a Pool of Reactors and Fuel Cycle Plants: Application to the Study of the Deployment of Fast Breeder Reactors. Proceedings of the International Conference on Fast Reactors and Related Fuel Cycles, Kyoto, Japan, October 1991. [9] C. G. Bathke and E. A. Schneider. Report of the COSI and NFCSim Benchmark. Los Alamos National Laboratory (2003). LA-UR-03-8051. [10] J. A. Stillman, “Homogeneous Recycling Strategies in LWRs for Plutonium, Neptunium, and Americium Management,” Argonne National Laboratory, ANL- AFCI-124, August 2004. [11] E. A. Hoffman, W. S. Yang, R. N. Hill, Preliminary Core Design Studies for the Advanced Burner Reactor over a Wide Range of Conversion Ratios, ANL-AFCI- 177, September 29, 2006. [12] E. A. Hoffman, “Updated Design Studies for the Advanced Burner Reactor over a Wide Range of Conversion Ratios,” Argonne National Laboratory report, ANL- AFCI-189, May 31 (2007). [13] E. A. Hoffman, “FY09 ANL AFCI Transmutation Studies,” Argonne National Laboratory report, ANL-AFCI-271, August 31 (2007). [14] M. Asgari, B. Forget, S. Piet, R. Ferrer, S. Bays, Computational Neutronics Methods and Transmutation Performance Analyses for Light Water Reactors, INL/EXT-07- 12472, March 2007. [15] R. M. Ferrer, M. Asgari, S. E. Bays, B. Forget, “Fast Reactor Alternative Studies: Effects of Transuranic Groupings on Metal and Oxide Sodium Fast Reactor Designs,” INL/EXT-07-13236, September 2007. [16] G. Youinou and S. Bays, “Homogeneous recycling of Pu or Pu with Minor Actinides in PWRs loaded with MOX-UE fuel (MOX with U-235 enriched U support), INL/EXT-09-16091, AFCI-SYSA-TRAN-SS-RT-2009-000055, June (2009). [17] OECD Nuclear Energy Agency, Nuclear Fuel Cycle Transition Scenario Studies Status Report (2009). [18] S. J. PIET, G. E. Matthern, J. J. Jacobson, C. T. Laws, L. C. Cadwallader (INL), A. M. Yacout, R. N. Hill (ANL), J. D. Smith, A. S. Goldmann, G. Bailey (SNL), “Fuel Cycle Scenario Definition, Evaluation, and Trade-offs,” INL report, INL/EXT-06-11683, August (2006). [19] OECD Nuclear Energy Agency and International Atomic Energy Agency, Uranium 2007: Resources, Production and Demand, NEA No. 6345 (2008). [20] J. S. Herring, “Uranium and Thorium Resources,” in The Encyclopedia of Energy, Cutler J. Cleveland, editor in chief, Academic Press, (2004). [21] J. J. Steyn, “Uranium Resources: Need For 21st Century Advanced Fuel Cycles,” Energy Resources International, Inc., NEI International Fuel Seminar (2003). [22] D. J. Rose, Learning About Energy, Plenum Press, New York (1986). [...]... larger than 31 0 million tons CO2e, the changes in marginal CO2 emission reduction amounts are very tiny 31 2.90 200 32 7 .37 141.92 Total Investment Cost (10^6 yuan) Fig 5c Emission reduction amount under different total investment cost 25000 27500 30 000 32 500 36 .30 35 000 0.00 37 500 100 0 32 5.50 252.04 30 0 40000 Emission Reduction (10^6 ton CO2e) 400 88 Nuclear Power – Deployment, Operation and Sustainability. .. unexpected events (nuclear accidents), and at any time horizon t , the probability of nuclear accidents happen will be t and the 74 Nuclear Power – Deployment, Operation and Sustainability probability of nuclear accidents do not happen will be 1  t ; u represents the damage or loss caused by nuclear accidents during nuclear operation, and S  M  L  1 Considering different level of nuclear accidents... initiatives of advanced nuclear technologies Tronea (2011) has discussed the European quest for standardisation of nuclear power reactors, including nuclear power design, new reactors standard and nuclear safety Yan et.al (2011) has introduced the development of nuclear power and third-generation nuclear power demonstration projects in China, and they also have forecasted the future demand of uranium fuel... Portfolio and Policy Implications Based on Portfolio Theory Energy 35 : 139 1-1402 [33 ] Fan, Y et.al 2010 An analysis on Carbon Capture and Storage Technology, Regulations and Its Emission Reduction Potential Advances in Climate Change Research 5: 36 2 -36 9 (In Chinese) [34 ] Pindyck, R.S 19 93 Investments of Uncertain Cost Journal of Financial Economics 34 : 53- 76 [35 ] Gollier, C et.al, 2005 Choice of nuclear power. .. investment of third-generation nuclear power 72 Nuclear Power – Deployment, Operation and Sustainability As NPV based evaluation method can not fully catch the impacts of these uncertainties on nuclear power investment, it is necessary to develop a proper method to handle such kinds of uncertainties to evaluate the demonstration and deployment of third-generation nuclear power plants in China Real options... third-generation nuclear power investment, if the electricity price is set by the government and nuclear power can not join CDM, the value of nuclear power plant is 0 and the investment has been abandoned in all paths This means, because of high investment cost and uncertainty, under current level of constant electricity price for nuclear power, third-generation nuclear power is not worth investing in China And. .. Evaluation of Third-Generation Nuclear Power - From the Perspective of Real Options 73 t  T / N , and define tn  nt , n  0,1, N All the units for the parameters described below is displayed in table 1 3. 1 Modeling third-generation nuclear power operation At nuclear power plant operation period, first it is in need to calculate the cash flow during nuclear power operation Assuming at any period... third-generation nuclear power project-Sanmen nuclear power plant in Zhejiang province, to evaluate the value of thirdgeneration nuclear power plant from the perspective of power generation enterprises Several technical and economic uncertainty factors (deployment cost, generating cost and nuclear accident), and two price mechanisms (electricity price and CDM) have been considered in the model and it is solved... Nuclear Power – Deployment, Operation and Sustainability 4 Evaluation of third-generation nuclear power investment in China Take the value of parameters into the model, and simulate the future changes of uncertainty factors according to their initial settings, then we can calculate the nuclear power plant value with abandon option by LSM method Considering the Randomness of Monte Carlo simulation and. .. Combined Cycle power plant Energy Economics, 30 : 1850-1881 [17] Maribu, K.M et al 2007 Distributed energy resources market diffusion model Energy Policy, 35 : 4471-4484 90 Nuclear Power – Deployment, Operation and Sustainability [18] Siddiqui, A.S., Marnay, C 2008 Distributed generation investment by a microgrid under uncertainty Energy, 33 : 1729-1 737 [19] Siddiqui, A.S., Maribu, K 2009 Investment and upgrade . unexpected events (nuclear accidents), and at any time horizon t  , the probability of nuclear accidents happen will be t and the Nuclear Power – Deployment, Operation and Sustainability . new reactors standard and nuclear safety. Yan et.al (2011) has introduced the development of nuclear power and third-generation nuclear power demonstration projects in China, and they also have. climate policy and trading mechanism of CDM (Bilateral or Unilateral) will also affects the investment of third-generation nuclear power. Nuclear Power – Deployment, Operation and Sustainability