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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|19318||2012||15 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Nuclear Engineering and Design, Volume 243, February 2012, Pages 120–134
This paper presents a simple, systematic, and integrated methodology to analyse the expected Levelised Cost Of Electricity (LCOE) generation of a new nuclear technology facing significant technological uncertainty. It shows that flexibility in the design and deployment strategy of a demonstration commercial thorium-fuelled Accelerator-Driven Subcritical Reactor (ADSR) park significantly reduces the expected LCOE. The methodology recognizes early in the conceptual design a range of possible technological outcomes for the ADSR accelerator system. It suggests appropriate flexibility “on” and “in” the first-of-a-kind design to modify the demonstration park development path in light of uncertainty realizations. It then incorporates these uncertainties and flexibilities in the design evaluation mechanism. The methodology improves existing approaches for design and engineering decision-making, providing guidance for government support for a new, secure, clean, and publicly acceptable alternative technology for power generation.
Thorium-fuelled Accelerator-Driven Subcritical Reactor (ADSR) technology is a promising avenue for the transmutation of radioactive wastes (Bowman et al., 1992 and Foster, 1974), and for secure, low-emission, and more publicly acceptable power generation (Carminati et al., 1993). It consists of a nuclear reactor core operating subcritically, and a high-power proton accelerator that bombards a spallation target within the reactor core to generate neutrons. These externally supplied neutrons supplement the reactor's own neutron population and sustains a fission chain reaction, as in Fig. 1. This technology offers new opportunities to governments concerned with limiting CO2 emissions, reducing risks associated with nuclear weapons proliferation and geological waste disposal, and sustaining prosperous economic development. In countries with considerable thorium reserves (e.g. India), it has the potential to capture a non-trivial segment of the growing electricity market. In other countries, it can help diversify the portfolio of low CO2-emitting technologies.Developing thorium-fuelled ADSR technology promises to be technically challenging, economically risky, and capital-intensive. Traditional nuclear power technology demands a large capital cost (Pouret et al., 2009), and requires many years of pre-development, construction, and testing before providing online capacity. An ADSR's further requirement of high-powered accelerator technology will demand additional capital commitment, and will therefore involve significant extra financial uncertainty. Given the high upfront cost, one needs a realistic and reliable picture about the expected returns, one that explicitly recognizes how the first-of-a-kind demonstration of the technology might perform. There is much uncertainty associated with how technology will develop during the initial deployment phase of a first-of-a-kind ADSR demonstrator. This uncertainty will ultimately affect the Levelised Cost Of Electricity (LCOE) generation, which is a useful metric for evaluating economic performance and the value that a project is expected to return. One concern unique to ADSRs compared to other nuclear technology relates to the reliability of the accelerator supplying the proton-beam. If an unplanned shutdown of an accelerator leads to an ADSR shutdown, costs will be incurred due to failing to supply the electricity grid (Steer et al., 2009). Alternatively if unplanned shutdowns are eliminated through spending additional time performing maintenance on the accelerator, there is less time to schedule electricity generation and sales. To address these issues, this paper introduces and applies a simple, systematic, and integrated methodology to evaluate design and deployment strategies for innovative systems facing significant technological uncertainty. The starting point of the methodology for ADSRs is the technical design descriptions of a first-of-a-kind ADSR system offered in the companion paper by Steer et al. (2012). The methodology enables engineers and decision-makers to: (1) recognize explicitly uncertainty sources affecting the expected performance of the system; (2) incorporate the concept of flexibility in design and management with the goal of improving performance; and (3) evaluate the design space based on expected economic impact, to guide decision-making for large-scale investment and deployment. The integrated methodology has been applied to investigate the hypothesis that inserting flexibility early in the conceptual design of an ADSR can improve the expected economic performance while testing and validating the technology. One anticipates that flexibility will lower the expected development and deployment cost of the system. The methodology builds upon and extends standard practice for design and decision-making in engineering by considering a priori a range of uncertain outcomes affecting costs, and adequate flexible responses. This approach differs from sensitivity analyses performed after an initial design is selected. It recognizes intelligent design and pro-active system management as uncertainty unfolds. The methodology provides a framework for evaluating designs, and assessing the expected value of flexibility so it can be compared to the cost of acquiring the flexibility. The remainder of the paper is structured as follows. Section 2 provides an overview of related work in flexibility/real options analysis in an engineering context, together with previous work specifically focusing on the nuclear sector. Section 3 explains the integrated methodology, and Section 4 follows with an example application to the deployment of a demonstration commercial ADSR park. Section 5 concludes by discussing modeling assumptions and limitations, as well as findings. It also provides guidance for future work.
نتیجه گیری انگلیسی
This paper has presented and applied a simple, integrated methodology intended to improve or ratify the design of innovative technology in terms of its economic performance and costs. The impact of uncertainties associated with a technology's future performance on its economic performance has been emphasised, specifically with relation to decisions that are made in the early conceptual design phase. Suggestions were made on how the expected cost of the case study technology, the ADSR, could be improved through the identification of specific flexibilities that could be integrated into the design, enabling it to better cope with a range of future technology performance scenarios. The paper highlighted the importance of considering uncertainty and flexibility in the early conceptual design of technological development, as opposed to later in the detailed design phase, where it would no longer be cost effective to take advantage of alternative design and development pathways. Through subjecting developing engineering systems to a cost analysis that is more realistic than typical DCF – i.e. by recognizing uncertainty explicitly – one ascertains a better understanding of the expected cost of the technology. Furthermore, it is possible to identify design improvements that reduce the expected cost of the technology. This knowledge is valuable not only to investors but also from a policy standpoint for public investment in research and development of such capital intensive, risky, but promising technological ventures. 5.1. Key contributions There are two central aspects to this work: first, the application of the integrated methodology was demonstrated to analyse a complex engineering system under development. This contributes to further validating the methodology, although full validation would require more applications – beyond the scope of this paper. Second, the design of ADSR nuclear reactor technology demonstrably benefited from scrutiny using the ROA-inspired methodology. This gave rise to a set of design and deployment strategies that demonstrably improved the E[LCOE] of this particular example of engineering systems. 5.1.1. Demonstration of the methodology The first key contribution was to demonstrate application of the 4-step integrated methodology to the analysis of a complex engineering system under technology uncertainty. This methodology aims to help designers and decision-makers of complex systems consider uncertainty and flexibility more systematically in the early conceptual phases of design. The general formulation presented in Section 3 involved nothing specific to ADSR systems and, although beyond the scope of this paper, it is expected that it will be applicable to the analysis of other engineering systems. It should be clear that the methodology augments typical sensitivity analyses because it incorporates decision-makers’ capacity to adapt to various situations along a development path. The economic assessment presented here was therefore more realistic than a typical DCF approach. However, it was not overly complex and therefore untenably time consuming. The analysis explicitly recognized a range of possible uncertainty scenarios, and took pro-active steps for managing these uncertainties by means of flexibility. The demonstration economic analysis used E[LCOE] as the metric for assessing flexible reactor park designs compared to a benchmark design. After defining three scenarios for the future performance of accelerator technology and assuming complete uncertainty regarding which scenario could occur, the E[LCOE] of a flexible design was found to be reduced by 12% compared to an inflexible deployment strategy (from £68.09/MWh for the inflexible 1 accelerator/1 reactor design to £59.71/MWh for the flexible 1 accelerator/1 reactor design). Even a reduction in E[LCOE] of only 1% will be significant for such a multi-billion £ investment. On the other hand, if the probability assigned to an optimistic scenario is higher compared to the central and pessimistic scenarios, the value of flexibility will reduce accordingly, and an inflexible design will be favored. Indeed, the sensitivity analysis in Fig. 11 showed that the inflexible benchmark ADSR design would become more valuable than the flexible alternatives for cases of high confidence in the good performance of the accelerator system. Such optimism would reflect a risk-seeking profile, which would amount to ignoring the possibility of central and pessimistic scenarios (i.e. uncertainty altogether). Indeed, flexibility has value only if one recognizes uncertainty, and decides to act on it in a pro-active manner. 5.1.2. Flexibility/real options strategies Regarding the second contribution, three specific real options for the accelerator system of a commercial ADSR demonstrator were suggested and investigated. They arose because of the specific focus on technology and EA uncertainty drivers. Although not the only ones available or feasible, these strategies were suggested to enable flexibility “in” and “on” the project in the face of technology uncertainties elicited via the integrated methodology. Strategy 1 consists of an operational flexibility strategy “in” the project that requires the beam transport systems of the accelerators to be integrated into a single delivery network. Through this network, it should be possible for each accelerator to swiftly deliver its beam to any one of the reactors. This strategy builds upon and integrates with flexibility Strategy 2, which consists of a tactical scale alteration real option “in” the engineering design. Strategy 2 is obtained by designing the system with contingency to add one more accelerator to increase power generation, in case this is too low due to frequent unscheduled accelerator shutdowns, or because of extensive scheduled maintenance. These two strategies enable dynamic adjustment of the degree of accelerator redundancy provided to the reactors throughout their lifetime. It is expected that, through careful planning, another direct benefit of these strategies will be that while each reactor is successively closed for maintenance, there will be periods where a redundant accelerator is available. This redundancy will enable shutting down another accelerator if need be. This accelerator could then be visited for maintenance. This benefit has not been factored explicitly in the analytical model, and could be included in a subsequent and more detailed model of the system. Flexibility Strategy 3 is a growth option “on” the project. It gives the “right but not the obligation” to expand to a demonstration reactor park in Phase 2 if and only if accelerator performance (and the performance of other systems not considered in this paper) is good enough. This paper focused on the analysis of Strategies 1 and 2 because they require more thinking from an engineering design standpoint. These were integrated conceptually into a suggested design and development plan for commercial thorium-fuelled ADSR technology shown in Fig. 9. Strategy 3 was not studied in detail here, although the companion paper by Steer et al. (2012) gives it consideration. The design and development strategies obtained are radically different to those arising from extending current ADSR design and technology to a commercial demonstration park. Economic quantification was provided to support the design decision-making process depending on risk profiles and different probability assignments. Such explicit quantification enabled discriminating between two seemingly valuable flexible design configurations, a flexible 1 accelerator/1 reactor and a flexible 2 accelerators/1 reactor design. 5.2. Study limitations The flexibility strategies suggested here were based on the authors’ familiarity with the system. It is possible, however, that other strategies and design configurations could be crafted to favor reliable electricity production. The strategies studied here emerged by considering the canonical flexibility/real option strategies suggested by Trigeorgis (1996). They were extensions at a higher systems-level perspective of the widely used concept of redundancy in engineering practice, and current understanding of ADSR design (Pierini et al., 2003). To this end the paper provides a systematic methodology to explore other uncertainty sources and flexibility/real option strategies thoroughly and rigorously. The idea that flexibility can be used at the R&D level and later for large-scale deployment of innovative technology is applicable to other systems beyond the nuclear sector. Of course validating the generality of this statement is beyond the scope of this paper, given this study only provides one case demonstration. On the other hand, it is sensible that ideas of flexibility at the R&D level and large-scale deployment could be applied to many systems to reduce expected costs. This may help convince investors and/or funding agencies that money is used wisely all along the research process. The use of decision analysis in this study had both benefits and drawbacks. One drawback was the difficulty to consider many stages and uncertainty sources. The dimensionality of a decision tree is known to explode quickly with the number of stages and states. Another drawback was that decision analysis did not lead to an economic assessment of flexibility as rigorous as that provided by traditional ROA. On the other hand, these techniques rely on assumptions about markets and availability of data that may or may not be realistic for ADSR demonstration prototypes–and innovative technologies in general. For example, ROA based on arbitrage-enforced pricing assumes markets of comparable tradable assets exist, are complete, and frictionless. This enables constructing a replicating portfolio hedging perfectly the cash flows produced by the asset, helping to deduce the value of the flexibility (Cox et al., 1979, Dixit and Pindyck, 1994 and Trigeorgis, 1996). Such markets and ideal conditions may not exist for new ADSR technology. Another ROA approach based on equilibrium asset pricing makes the less stringent assumption of equilibrium within and across the markets for the relevant asset types (Arnold and Crack, 2003 and Rubinstein, 1976). Such valuation, however, relies on data about market equilibrium for supply, demand, prices, together with trends and volatility for comparable assets. Although data exist for electricity supply and demand, it is not clear whether market data is readily available for establishing the costs of innovative ADSR technology. In addition, many of these economically rigorous techniques rely on concepts that may not be familiar to practicing engineers. This represents an important barrier to dissemination in real-world design and decision-making practice (Barman and Nash, 2007 and Engel and Browning, 2008). Another important downside from using decision analysis related to the choice of probability distributions. As explained by Morgan and Henrion (1990), analysts must sometimes rely on educated guesses, especially when there is no historical data or clear Bayesian process to support inferences. This was the case here because no clear data existed on the probability of each EA scenario. This is why the sensitivity analysis performed over all possible distributions was conducted, and presented in Fig. 11. This analysis provided a graphical way to visualize the thresholds at which design decisions may have shifted. In this case it did show that flexibility would be valuable in most cases as opposed to a rigid, inflexible design strategy. Such decision thresholds may not be as clear for other systems. At the expense of economic rigor, decision analysis had the advantage of offering better transparency to expose the concepts above. By extension, it could help designers and decision-makers consider more explicitly in the early stages of design the different scenarios arising in the future. A graphical view of the decision nodes followed by uncertainty/chance nodes may induce more pro-active considerations of the “what-if” scenarios by means of flexibility. Another advantage of decision analysis was to enable a relatively quick evaluation and rank ordering of the different design alternatives. This was the essential value proposition for design decision-making in this paper. It was feasible without a deep understanding of the rigorous economic concepts explained above. Also, decision analysis was useful to analyse the systems exhibiting significant path dependencies. It enabled drawing explicitly the different development pathways under uncertainty, without assuming path recombination as done in traditional ROA (Copeland and Antikarov, 2003 and Cox et al., 1979). Indeed, the assumption of path independence – central to these techniques – cannot be fulfilled in almost all cases of engineering systems design (Wang and de Neufville, 2005). 5.3. Future work A natural extension of this work would be to develop a simulation-based model to account for more uncertainty sources, scenarios, and design details in technology modeling. The same integrated methodology could be used to explore other possibilities systematically. Techniques developed by Cardin et al. (under review) and Mikaelian et al. (2011) could be used to stimulate flexibility generation and identification. If many more design alternatives arise, the analysis could build upon and extend the work by Lin et al. (2009), Wang (2005), and Yang (2009). These authors developed a screening approach based on optimizations and design of experiments to explore the design space efficiently for valuable design configurations. The analysis could also be extended to the possibility of driving ADSRs with other alternative compact accelerators, such as non-scaling Fixed-Field Alternating Gradient (ns-FFAG) accelerators, synchrotrons, or superconducting cyclotrons.