23 October 2015 The Role of Nuclear Power in the Middle East Electricity Industry By Richard Druce Introduction The power systems of the Middle East are facing a range of challenges, including keeping pace with rapid demand growth, rising costs, and a desire to moderate the environmental impact of the regional utility industry. These challenges, which are, in fact, common to many economies throughout the world, are leading utilities and policymakers to consider diversification away from reliance on locally produced fossil fuel to alternative sources such as renewables and nuclear. This paper sets out an economic framework for identifying the least-cost mix of generation technologies, which is a key target for policymakers when assessing the potential advantages and disadvantages of nuclear power. Our economic framework considers, using a “fundamentals” approach, the challenges faced by a central planner to minimise costs. However, as becomes clear from the discussion in this paper, this economic framework requires a large number of assumptions about the future, which may or may not be valid. Using market information to better understand this range of uncertainty can mitigate this challenge, but, as this paper discusses, the assessment of the optimal technology mix can also be supported through the introduction of more competition into power procurement arrangements. Finally, as this paper also discusses, Middle Eastern governments’ ultimate choice of whether to pursue a nuclear generation programme will consider both an optimisation of future generation costs, for which the economic framework we describe is a potentially useful tool, and a range of other environmental and political factors. These factors are discussed only briefly in this paper. This paper was first presented and prepared for the PowerGEN Middle East Conference in Abu Dhabi on 5 October 2015. www.nera.com 2 A Basic Framework for Identifying the Least-Cost Generation Mix Simple comparisons of levelised generation costs At its simplest, the role for new nuclear as compared to other generation technologies can be assessed through a simple comparison of the Levelised Cost of Energy (LCOE)1 across alternative technologies. Numerous studies have sought to estimate the levelised costs of generation for competing technologies. One such comparison is shown in Table 1 (page 19). But such a comparison is simplistic, as it ignores some fundamental differences between competing technologies. Most notably, nuclear plants have very low marginal costs of production, so will run baseload in most power systems, ahead of plants with higher variable costs of production. This is not the case for fossil fuel-fired plants, especially combustion turbines and older coal-fired steam units, which tend to have higher variable production costs, making it uneconomic for them to run in off-peak hours. In other words, nuclear and other technologies have a different balance between fixed and variable costs. Comparisons of levelised costs, such as that shown in Table 1, can consider this factor by making assumptions on the plant’s relative load factors, but it provides no basis for testing this assumption. Extending this analysis to a “screening curve” framework A theoretical framework for selecting least-cost generation deployment In light of the significant limitations associated with a comparison of levelised generation costs, a more rigorous approach is to compare generation costs using a “screening curve” framework to identify the least-cost mix of generation technologies. This well-established framework is illustrated in Figure 1, the top panel of which shows a series of upward-sloping cost curves. Each of these lines represents how the total cost of producing electricity from each generation technology changes (the vertical axis) as it runs for an increasing number of hours over the year (the horizontal axis). For this illustration, we assume that the electricity market is served by three defined generation technologies: • Baseload plants: we define “baseload” plants as those (like nuclear) with high fixed costs of construction and operation, and low variable costs of generation. Accordingly, the green cost curve begins at a high cost per MW-year at zero hours of generation, but rises slowly (flat slope) with the number of hours of generation. • Peaking plants: we define peaking plants, such as open cycle gas turbines (OCGTs2) as those with relatively low fixed costs but higher variable costs of generation. The orange cost curve for this technology therefore has a lower intercept with the vertical axis, but rises more steeply with hours of generation. • Mid-merit plants: denoted by the orange cost curve, these plants have fixed and variable costs that sit in between those of baseload and peaking plants. However, it is not necessarily efficient to provide generation capacity to serve very high levels of demand that only occur in a small number of hours per year; it may be cheaper to “shed load” and compensate consumers for the supply foregone at a price defined by the Value of Lost Load (VOLL). This option is represented by the red cost curve, which begins at the origin and has a very steep gradient reflecting the high cost of curtailing users. www.nera.com 3 This set of four individual cost curves then establishes an “envelope” that traces out the leastcost generation mix, given the current available technologies. This envelope, the screening curve, is illustrated by the thicker lines in the top panel of Figure 1, which essentially shows the lowest cost plant types as a function of their operating hours during the year. This screening curve can be mapped onto a “load duration” curve (as shown in the bottom panel of Figure 1), which ranks demand across all hours of the year from highest to lowest, and thus identifies the optimal deployment of each technology to serve a given load. In this illustration, this optimal mix includes A megawatts (MW) of baseload plant, B megawatts of mid-merit plant, C MW of peaking plant, leaving D MW of load unserved at peak. Implementing this modelling framework To demonstrate the implications of applying this framework for selecting the optimal mix of generation technologies in the Middle East, we have incorporated it into NERA’s fundamentals model of the interconnected GCC power system,3 implemented using the Aurora modelling framework.4 Our modelling approach is described below in Figure 2. Figure 1. Derivation of Least-Cost Generation Mix Peakers VOLL Baseload Baseload capacity Mid-merit Capacity /Load (MW) D C B A Total cost ($/MW) Hours per year Hours per year VOLL capacity Mid-merit capacity Peaking capacity www.nera.com 4 As well as applying the framework described above in Figure 1, which is itself an improvement on the simple comparison of LCOEs shown in Table 1, NERA’s model actually improves on this basic theoretical framework described in the section titled “A theoretical framework for selecting least-cost generation deployment,” by accounting for trade between markets, as well as the dynamic operating constraints and unit commitment costs of competing technologies. For instance: • Solar PV, like other technologies whose output is dependent on factors such as the weather (sunlight, wind, rainfall, etc.), have variable and intermittent production profiles. This has a range of implications for optimising the generation mix. The variability of their production means that the unit commitment costs of other thermal plants on the system may increase, such as the costs of starting up or part-loading thermal capacity. For instance, as production from solar generators wanes towards the end of the day, it may be necessary to replace that production by starting up thermal plants, which imposes some costs on the system, as we discuss further in the section below titled “The future role for solar in the Middle East generation mix.” • There may also be some periods of time in which high levels of output from solar and/or nuclear plants (which have low variable costs of production) mean there is more production than the system can absorb, and some has to be curtailed. This would prevent these plants from running at the load factors assumed in computing their LCOEs (see Table 1). In the Middle East, this effect may be compounded by the prevalence of cogeneration desalination plants, which must operate for much of the year in order to meet water demand, whether their power output is required or not. Figure 2. NERA’s Aurora Modelling Framework • Market-leading dispatch software • Chronological dispatch algorithm • Projects entry/exit decisions and dispatch using an iterative algorithm Inputs • Existing generation capacities and technical capabilities of units (e.g. unit commitment costs, efficiencies, O&M costs) • Committed expansions in gereration capacity, e.g. plant under construction & new renewables • Generator fuel, variable O&M costs and emissions cost (if applicable) • Fixed O&M costs and the costs of new entry Outputs • Optimal scheduling and despatch of plant to meet energy demand and reserve requirements • Forecasts of plant output, fuel, CO2 and O&M costs, etc • Projections of new investment by technology and location • Projections of existing generators’ closure dates • Modelled flows across interconnectors • Where applicable, power price forecasts www.nera.com 5 • Power systems also need to maintain “spinning reserve,” which is usually at least sufficient to fulfil the deficit in production left by the loss of the largest in-feed to the system. Because nuclear plants have relatively large unit sizes, the introduction of nuclear can increase spinning reserve requirements, which also increases system costs. However, this cost does not rise as the system adds more nuclear plants of the same size, so the cost can be thought of as an overhead associated with deploying any number of large nuclear units. The intermittency of technologies like solar may also increase reserve costs, as despatchers need to hold more capacity in reserve to compensate for unexpected changes in the weather that affect production from solar plants.5 As Figure 2 summarises, NERA’s optimisation framework accounts for these effects using a mixed integer linear program (MIP) to optimise scheduling and despatch in a chronological framework, which accounts for unit commitment costs and dynamic constraints, such as solar plants’ variable production profiles. The model then iterates to select generation investments (new plant commissioning and plant closures) to optimise the balance between investment and despatch costs. Projecting the optimal capacity mix for the interconnected GCC power system A baseline optimised expansion plan for the interconnected GCC power system, as projected by the NERA model, is shown in Figure 3. It shows that, given the range of generation costs in Table 1, the least cost expansion plan consists of a mix of gas-fired combined-cycle (CC) and open cycle gas turbines (OCGTs), as well as 8 GW of additional new nuclear capacity beyond those units already in development in the United Arab Emirates.6 The model chooses not to develop any new solar capacity based on our baseline assumptions on its cost and production profile. However, as the relatively simple comparisons of levelised generation costs in Table 1 demonstrates, the economics of these technologies depend crucially on the level of fossil fuel and CO2 prices— higher fossil fuel prices and higher CO2 prices improve the economics of these low carbon technologies, and vice versa. Given the long construction time and high capital costs associated with new nuclear plant, the economics of the candidate technologies also depend crucially on financing costs, which may vary across technologies. www.nera.com 6 To illustrate the effects of these sensitivities, Figure 4 shows how this baseline would change in four alternative scenarios regarding the cost of the candidate technologies: • Higher fossil fuel prices: – In the baseline scenario, we derive natural gas prices from a netback calculation that seeks to reflect the opportunity cost of gas for export from the region. This approach reflects an assumption that regional gas markets are (in the vernacular of economists) “perfectly competitive.” Specifically, we take the mid-point between the netback price of LNG from Qatar (plus the cost of liquefaction and regasification) to Asia (upper bound) and to Europe (lower bound). Both forecasts are based on market forward prices for gas as of early 2014 in the short-term and long-term forecasts from the IEA’s World Energy Outlook (WEO), “New Policies Scenario.” According to our baseline forecasts, gas prices fall from $9.5MMBtu to around $6.8/MMBtu by 2017 before rising steadily to $12.6MMBtu by 2038. – However, given the uncertainty around the value that power system planners in the GCC place on the value of fuel for planning purposes, and the possibility of market power in the local upstream gas market that would inflate prices compared to the notionally competitive level, we run a sensitivity in which we increase our baseline fossil fuel prices by 25%. This has the effect of making nuclear and solar more attractive compared to gas-fired CCGT plant, increasing solar penetration by 200 MW and nuclear by 9 GW compared to the baseline scenario by 2030. Figure 3. Optimised Capacity Expansions in the GCC: Baseline Scenario 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) Existing CCGT New CCGT Existing OCGT New OCGT Nuclear Other Solar Steam Turbines Peak Load www.nera.com 7 • Lower carbon prices: In this case, given the uncertainty around the social cost that policymakers and power system planners in the GCC place on CO2 emissions, we run a sensitivity with a zero cost attached to CO2 emissions from thermal plant. In this case, no new nuclear or solar plant is developed by the model, beyond what is already in development. This contrasts with the baseline, in which we assume CO2 prices take a value of $22/tonne in 2020, rising to $37/tonne by 2030 based on assumptions in the WEO “New Policies Scenario” regarding the value placed on reductions of CO2 emissions in Europe. • Higher risk premium for new nuclear: Reflecting the specific challenges associated with financing new nuclear and the uncertainties nuclear developers face (see below), we have performed a sensitivity in which we increase the WACC for nuclear by 100 basis points, leaving the WACC for all other technologies unchanged. This has the effect of reducing nuclear deployment compared to the baseline, replacing nuclear capacity with additional CCGTs. It also improves the competitiveness of solar, increasing penetration by around 1 GW compared to the baseline. • Higher risk premium for new nuclear combined with higher fossil-fuel prices: We have also combined the scenarios in which we assume relatively high financing costs for nuclear with 25% higher fossil fuel prices. This reflects a scenario in which governments place a high value on conserving fuel and avoiding CO2 emissions, but financing new nuclear proves relatively difficult. In this case, the model develops 3 GW more solar and 8 GW more nuclear capacity than in the baseline, albeit less than in the scenario where we increase fossil fuel prices without adjusting the WACC for new nuclear. www.nera.com 8 The detrimental effects of ignoring unit commitment and dynamic constraints in system planning Following the discussion of unit commitment costs in the section titled “Implementing this modelling framework,” we have also estimated the impact on the optimised capacity mix that would come from ignoring unit commitment costs in selecting the optimal generation mix. For this sensitivity, we assume all plants have zero start-up costs, no minimum stable load, and no must-run constraints. Figure 5 shows that, by ignoring these costs in planning, the model develops materially more new nuclear plant, as it appears to be a relatively cheap source of energy when these costs are ignored, and materially less new OCGT capacity, which is relatively flexible compared to CCGT plant. If, instead of the baseline capacity mix in Figure 3, the generation mix were planned in a way that ignores unit commitment and dynamic constraints (see Figure 5), it would impose significant costs on the GCC power system. By despatching the mix shown in Figure 5 in a way that accounts for unit commitment costs, we estimate additional costs compared to the baseline in Figure 3 of $400 million per annum over the modelling horizon to 2030, or around 2% of total system costs. In essence, this figure illustrates the quantum of saving that can be realised through rigorous system planning to account for these real-life technical characteristics of alternative generation technologies. Figure 4. Alternative Scenarios on Commodity Prices and Financing Costs Zero Cost of CO2 Emissions 25% Higher Fossil Fuel Prices 25% Higher Fossil Fuel Prices, and 100 Basis Points Higher WACC for Nuclear 100 Basis Points Increase in WACC for Nuclear Existing CCGT New CCGT Existing OCGT New OCGT Nuclear Other Solar Steam Turbines Peak Load 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) www.nera.com 9 The benefits of trade in optimising the generation mix Unlike a simple comparison of levelised generation costs, and even the basic cost minimisation framework set out in the section “A theoretical framework for selecting least-cost generation deployment,” the Aurora modelling framework described in Figure 2 can account for the role of trade amongst those jurisdictions connected by power transmission, including the effect of any transmission constraints on optimal generation expansion. However, in the baseline generation mix shown in Figure 3, we assume that the trade of energy between countries within the interconnected GCC power system is limited to the level required to provide support in case of acute shortages. We achieve this in the model by assuming the transmission system connecting the six GCC countries is present, but we apply high wheeling charges to power exchanges (of $1,000/MWh) to prevent trade in all conditions, save for where it avoids load-shedding in one or more systems. If we relax this constraint and allow the model to re-optimise the generation mix, as Figure 6 shows, the model deploys a slightly different mix of OCGT and CCGT technology, and more nuclear capacity. Intuitively, the ability to trade energy allows the model to dispose of surplus new nuclear energy from individual power systems in periods of low demand or high supply from other sources, so it deploys more nuclear capacity when optimising the mix. Compared to the baseline scenario, allowing trade of energy and planning the system to maximise the gains from trading energy has the potential to save around $630 million per annum, or around 2.5% of total system costs. Figure 5. Optimised Capacity Expansions in the GCC, Ignoring Unit Commitment Costs 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) Existing CCGT New CCGT Existing OCGT New OCGT Nuclear Other Solar Steam Turbines Peak Load www.nera.com 10 The future role for solar in the Middle East generation mix Across the range of scenarios set out above, it is noteworthy that the model does not select significant quantities of solar capacity in the mix, even when we increase fuel or CO2 prices or the financing costs of new nuclear. On the face of it, this may appear surprising, given the widely cited potential of this technology to compete with conventional fossil fuel-fired plant. We therefore considered a further scenario in which we doubled the learning rates for solar compared to those projected by the IEA in the generation cost data shown in Table 1. As Figure 7 illustrates, this assumption results in around 15 GW of new solar by 2030, displacing a mix of CCGT and OCGT plant. However, the model still selects the same quantity of new nuclear. Figure 6. Optimised Capacity Expansions in the GCC, Allowing Energy Trade 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) Existing CCGT New CCGT Existing OCGT New OCGT Nuclear Other Solar Steam Turbines Peak Load www.nera.com 11 This modelling does not, therefore, support the hypothesis put forward by some commentators that solar plant will meet a high proportion of the GCC’s power needs in the coming two decades. The model’s reluctance to develop new solar generation capacity, even at relatively low cost, can be understood by examining the hourly profile of production assumed for this technology. As Figure 8 shows, the output profile from solar PV allows it to contribute to meeting the mid-afternoon peak in demand during the summer months. However, once the sun goes down and production from solar PV tails off, output from gas turbines must increase to meet the shortfall in production from solar. Hence, the relatively flat diurnal pattern of demand means that solar PV has relatively low capacity value, and the model must still build gas turbines for back-up. Specifically, the capacity value of solar PV in the power system as a whole is (approximately) limited to the difference between the mid-afternoon peak in load, and the level of demand during the following evening, when gas turbines are required to ensure demand can be met. And even this may err on the high side, as output from solar PV cannot be guaranteed due to the weather.7 Of course, combining solar PV with electrical storage capacity could address this problem, but this would materially increase the capital costs of PV development, and the cost of bulk electrical storage is subject to a wide range of uncertainty. Figure 7. Optimised Capacity Expansions in the GCC, Faster Learning Rates for Solar Existing CCGT New CCGT Existing OCGT New OCGT Nuclear Other Solar Steam Turbines Peak Load 0 20 40 60 80 100 120 140 160 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 GW (Net) www.nera.com 12 Extending the Framework to Account for Uncertain Future Costs Low carbon technologies like nuclear and solar provide utilities with a hedge against variation in the price and availability of fossil fuels, which is another potential benefit of low carbon plant ignored by the analysis presented above. In markets where end user prices cannot be readily adjusted to account for such year-to-year variations in input prices, such as political or regulatory constraints on tariff adjustment, this type of hedge may protect utilities against fluctuating costs. In accounting for cost uncertainty, however, it would also be desirable to account for other risks besides fossil fuel price variation. For instance, uncertainty around the future economic/social cost of CO2 emissions, generation operating costs, the costs of nuclear decommissioning, etc. should all be taken into account. Utilities should also consider the extent to which contracts with developers/vendors leave the buyer/offtaker exposed to construction and operating cost risk, which is an important consideration in the case of new nuclear, as some existing projects have experienced significant cost overruns. Modelling tools (such as those we describe above) that optimise investment whilst accounting for dynamic constraints and unit commitment could, in principle, be extended to a stochastic framework, but this is computationally challenging. Moreover, stochastic cost modelling requires that the planner select and parameterise probability distributions for the key dimensions of uncertainty, which is challenging. A more tractable approach is to (1) factor in such risks through a mix of comprehensive sensitivity analysis, such as that shown in Figure 4, and (2) to account for risk and uncertainty when estimating the WACC used to annuitise the investment costs in competing generation technologies. This, in essence, allows the utility to draw on market-based information on the financial consequences of risk in its assessment of alternative generation technologies. However, there is a paucity of market information that provides direct evidence on how the costs of financing different types of generation investment differ. Controlling for other factors that influence the market perception of risk, such as differences in country and inflation risk premia, differences in the institutional and energy sector regulatory framework, etc., also presents challenges. Figure 8. Hourly production profiles with Significant Solar Penetration: 48 Hours in July 0 20 40 60 80 100 120 140 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 Production (GW/hr) Hour Other Solar GT CCGT ST Nuclear www.nera.com 13 There is, therefore, no silver bullet for addressing this issue, but it is an important consideration, especially when financing costs represent such a high share of the total costs of generation investments. For instance, the modelling presented above assumes all fixed costs for all technologies are annuitized using a real pre-tax WACC of 5.7% (see Table 1). However, as Figure 4 shows, adding 100 basis points to this figure in the case of nuclear, such as an attempt to reflect the effect of construction risks, changes the optimised generation mix materially. Energy Efficiency Improvements as a Substitute for Adding Generation All the modelling presented above has assumed an exogenously defined demand growth assumption, and the model then choses how this demand should be met at the least cost. In reality, however, opportunities may exist to conserve electricity consumption. If the cost of conserving consumption, such as through new energy efficiency measures, is less than the avoided cost of supplying it, it would be economically efficient to reduce consumption rather than provide additional generation production and/or capacity. The most efficient means of promoting efficient levels of energy efficiency is through the development of cost-reflective end-user tariffs, a key challenge in the GCC given the current wide-spread use of subsidised rates. And it is possible that further interventions by policymakers may be justified to promote the efficient uptake of energy efficiency measures. However, whilst important, factoring energy efficiency measures into an optimal expansion planning study is beyond the scope of this paper. www.nera.com 14 A Transition to Market-Based Procurement The discussion above sets out an economic framework and quantitative tools for identifying the least-cost mix of power generation technologies, and identifies some of the key value drivers for new nuclear power as compared to competing technologies like solar. However, the framework and tools are designed as a means of addressing the challenge faced by central planners of an electricity system. From an institutional perspective, central planning of an electricity generation mix is not necessarily the best way to achieve efficiency. In theory, the most efficient means of identifying the efficient mix of generation technologies is to create competitive, efficient markets for power, in which price signals inform investors as to what mix of generation technologies can be most efficiently—and profitably—developed. Such competitive markets do not, however, exist in the electricity industries of the Middle East. In fact, a number of institutional and structural barriers currently inhibit competition, such as the prevalence of subsidised end-user prices and fuel subsidies. Given these constraints, some Middle Eastern states provide electricity through appointment of vertically integrated, state-owned entities, so markets are necessarily absent. Those jurisdictions that have restructured away from this model to introduce more competition have adopted variants of the “single buyer” model in which procurement and planning decisions are taken centrally, and market signals about the value of alternative generation technologies remain limited. Nonetheless, even within the single buyer model, it may be possible to organise power procurement in such a way as to introduce some competition and wholesale price discovery. This is illustrated by recent developments in Oman, where the Oman Power and Water Procurement Company has stated that it intends to develop new procurement arrangements that include the introduction of a wholesale spot market to “operate alongside and in conjunction with the existing system of power purchase agreements (PPAs) and power and water purchase agreements (PWPAs),” and to introduce “a more flexible process for the awarding of new PPAs and PWPAs by OPWP, aimed at increasing competition, including between new-build and existing plants.”8 While such reforms might materially improve the efficiency of system planning, the need for some central planning in selecting the role for nuclear and other low carbon technologies such as solar is probably inevitable. For instance, all government decisions to accept and/or promote investments in new nuclear power require an assessment of the positive and negative externalities it brings, and these cannot readily be priced into the revenues earned (or the costs faced) by private generation investors, as discussed further below in the Annex. The economic frameworks and tools described above, therefore, remain of significant value to governments, regulators, and utilities when planning the future evolution of the generation mix. www.nera.com 15 Conclusions This paper sets out an economic framework and describes a range of economic modelling techniques for assessing whether it is economically efficient to deploy nuclear power, as compared to other generation technologies such as gas-fired CCGT and solar PV. While there are a range of costs and benefits associated with new nuclear power, some of which are hard to quantify (see the Annex), sound policymaking on new nuclear requires an objective assessment of its pros and cons. Even if some of the costs of nuclear power are not readily quantifiable, making a reasonable effort to quantify them provides an estimate of the economic benefits of nuclear that are available from including it in (or foregone by excluding it from) the generation mix in the Middle East. The modelling work discussed in this paper also leads to a range of conclusions regarding the optimal mix of generation technologies in the Middle East: • In most scenarios, the optimal generation mix contains a balance of CCGT and OCGT capacity, with nuclear competing with CCGT to meet baseload demand. The value of OCGTs diminishes when we allow the model to trade energy amongst regional markets, and if we ignore unit commitment costs and dynamic constraints in system planning. • The role for new solar generation appears limited, driven mainly by its low capacity value for meeting summer evening demand. The model builds OCGTs to compensate for the lack of solar output at these times. However, the economics of solar may improve if/when electrical storage technologies become more economical. • New nuclear is developed under a range of scenarios, but its economics are highly sensitive to assumptions on fuel prices, the value placed on CO2 emissions reduction, construction, and operating costs, and financing costs. Nuclear becomes more economical when the model is allowed to trade energy amongst regional markets. www.nera.com 16 Annex: Other Considerations in Assessing the Economics of New Nuclear The economic framework described in this paper, which aims to minimise the cost of the generation mix, assumes that all the costs and benefits associated with investments in new nuclear, as well as competing technologies, are faced by generation developers. In essence, it assumes that none of the candidate generation technologies confer higher benefits or costs on the power system or society than any other technology in a way that is not already captured by the cost assumptions. The discussion below considers some costs or benefits of competing technologies that may not be priced into the quantitative assessment above but that, nonetheless, require consideration by central planners and policymakers in assessing the role for new nuclear. Network Investment Costs The above comparison of new entrant costs has not considered potential differences in the transmission and distribution network investment costs associated with integrating new generation plant onto the GCC power system. For instance, it may be necessary to locate new nuclear plant in locations that are a relatively long distance from major demand and population centres. In some jurisdictions it may be possible, especially where there is vertical unbundling of network businesses, to set transportation tariffs that reflect locational differences in the network investment costs associated with developing new generation capacity. Such tariffs would allow generators to factor these tariffs into any investment decisions. But in jurisdictions where transmission is not unbundled from generation, a rigorous assessment of alternative generation investments ought to consider the impact of generation planning decisions on network investment costs. Long-Term Fuel Storage and Decommissioning Costs Decommissioning costs represent a material cost associated with a nuclear generation programme. They vary according to the type of reactor, its size, and its location (in particular the proximity to the nearest nuclear disposal site). They also tend to be unpredictable given the changeable regulatory environment surrounding nuclear power and the longer time horizons over which decommissioning costs are incurred. While this study has taken assumptions on these important categories of cost (see Table 1), specialist research beyond the scope of this report would be required to estimate them in the context of particular projects. These costs also have important implications for the role of the state in planning, regulating, and supporting the finance of nuclear generation assets. Some jurisdictions make the funding of decommissioning the responsibility of the nuclear plant owner, thus consistent with the “polluter pays” principle, as a means of internalising the externality associated with these costs. Nonetheless, to some extent there inevitably remains a role for government in ensuring nuclear power plant owners set aside sufficient funds whilst in operation to cover these high and often unpredictable costs. Moreover, in any event, the government takes on the role of “funder of last resort,” given the possibility that private investors may “walk away” once a plant has ceased operation, knowing that the government would pick up the pieces. In economic terms, there is a “moral hazard” problem associated with investments in nuclear power, as government always has an incentive to step in to ensure the safe and secure decommissioning of generation infrastructure and long-term storage of fuel. www.nera.com 17 In any event, a government decision to pursue a nuclear generation programme requires that governments develop a coherent plan for addressing the long-term challenges associated with decommissioning and waste storage, and have measures in place for funding. Safety and Possible Local Environmental Effects As a number of high-profile accidents have illustrated, accidents at nuclear power stations have the potential to cause significant and lasting harm to both the local environment surrounding the facility, and the local population. In economic terms, this factor is an externality that it is probably not practical to internalise by imposing it on private investors entirely. Necessarily, it falls on government to assess these risks in taking decisions over whether to accept new nuclear, and regulate safety adequately to minimise risk. Accordingly, this topic is beyond the scope of this paper. Security of Supply and Fuel Diversity Advocates of nuclear sometimes argue that nuclear power resolves the perceived security of supply problem faced by nations reliant on imported fuel by making them less reliant on foreign fuel. In particular, nuclear has the advantage of being divorced from many of the risks associated with imported fuel, such as gas grid failures. However, whilst security of supply is an important aspect of sustainable energy development, the benefits must be carefully weighed against the costs. In particular, in conditions where nuclear power is not assessed to be economic before considering security of supply benefits (e.g., where policymakers place little value on the avoidance of CO2 emissions), optimal system planning requires that other means of enhancing security of supply be considered. For instance, alternative means of enhancing security of supply may include building or expanding electricity transmission to other markets, keeping larger stocks of back-up fuel supply for fossilfuel generators, or diversifying gas supply sources through construction of LNG importation capacity. Also, where competitive markets for fuel and power exist, it may be sufficient for governments and regulators to rely on buyers and sellers of these commodities to optimise the diversity of fuel procurement. It is also important not to double-count the benefit that comes from insulating power systems from fossil-fuel price volatility. Where fuel markets are competitive, fuel scarcity tends to show up in price spikes that in turn feed through into the costs of the generation plant that burn these fuels. The prices charged in some long-term fuel supply contracts may also include premia compared to current spot prices reflecting the value that comes from securing supplies over a long time horizon. Developing New Industries and “Green Jobs” Like any major capital investment project, nuclear plants have the potential to create positive spill-over effects in terms of employment and the development of industries. Such effects are sometimes cited by governments as a material benefit of new nuclear investments. Investments in other low carbon generation technologies such as solar power tend to be supported with similar arguments in relation to development of “green jobs.” www.nera.com 18 However, such arguments should be met with a dose of scepticism. For such a benefit to feed into the decision as to whether governments should sponsor new investments in nuclear power or renewables, it would be necessary to demonstrate that any government support offered could not be better provided to other sectors, such as education, health, and transport, or redistributed to the population in the form of lower taxation. Adjusting for Project-Specific Costs The generation cost assumptions shown in Table 1 and used for the analysis presented in this paper are taken from a range of sources. In particular, generation capital and operating costs are taken primarily from the IEA’s 2014 World Energy Investment Outlook, so are “based on a review of the latest country data available and on assumptions of their evolution over the projection period,” and the data was reviewed through a survey of “external experts from utilities, equipment vendor, government agencies, universities, international organisations and non-governmental organisations across the world”.9 They therefore represent reasonable averages, but in the context of any specific investment would require adjustment and tailoring to the choice of reactor or generation equipment available to the utility in question, as well as the commercial structure of the project. For example, in the case of nuclear, the choice of reactor technologies may be constrained by geopolitical factors, such as international non-proliferation treaties. www.nera.com 19 GAS CCGT Gas Turbine CCGT CHP12 CCGT + CCS Capital Costs1 $/kW 800 450 1,040 1,440 Construction Period1 Years 2.5 2.0 2.5 4.0 WACC2 Real, pre-tax (%) 5.7% 5.7% 5.7% 5.7% Interest During $/kW/yr 82.1 39.2 106.7 217.2 Construction3 Asset Life1 Years 25 25 25 25 Total O&M Costs4 $/kW/yr 28.0 22.5 41.6 50.4 Thermal Efficiency5 Net, HHV (%) 53% 34% 74% 45% Emissions Rate6 Tonne/GJ 0.0514 0.0514 0.0514 0.00514 Fuel Price7 $/MWh 17.19 17.19 17.19 17.19 Emissions Price8 $/tonne 22.0 22.0 22.0 22.0 Long-term Waste $/MWh 0.0 0.0 0.0 0.0 and Decommissioning9 Total Fixed Costs10 $/kW/yr 95.0 59.7 128.8 176.4 Assumed Load Factor11 % 90% 25% 90% 90% Total Variable Costs10 $/MWh 40.3 63.2 28.9 38.7 Total Levelised Cost10 $/MWh 52.4 90.5 45.2 61.1 COAL Subcritical Supercritical Coal + CCS IGCC Capital Costs1 $/kW 1,300 1,600 2,880 2,100 Construction Period1 Years 4.5 4.5 4.5 5.0 WACC2 Real, pre-tax (%) 5.7% 5.7% 5.7% 5.7% Interest During $/kW/yr 217.8 268.1 482.5 387.6 Construction3 Asset Life1 Years 30 30 30 25 Total O&M Costs4 $/kW/yr 45.5 64.0 115.2 94.5 Thermal Efficiency5 Net, HHV (%) 35% 39% 30% 41% Emissions Rate6 Tonne/GJ 0.0881 0.0881 0.00881 0.0881 Fuel Price7 $/MWh 5.32 5.32 5.32 5.32 Emissions Price8 $/tonne 22.0 22.0 22.0 22.0 Long-term Waste $/MWh 0.0 0.0 0.0 0.0 and Decommissioning9 Total Fixed Costs10 $/kW/yr 152.3 195.4 351.7 283.6 Assumed Load Factor11 % 90% 90% 90% 90% Total Variable Costs10 $/MWh 35.4 31.5 19.8 30.0 Total Levelised Cost10 $/MWh 54.7 56.3 64.4 66.0 Table 1. The Levelised Costs of Selected Generation Technologies Appendix www.nera.com 20 NUCLEAR SELECTED RENEWABLES Nuclear Wind Onshore Wind Offshore Solar PV Solar PV (large scale) (buildings) Capital Costs1 $/kW 3,500 1,490 3,820 1,840 2,520 Construction Period1 Years 5.0 2.0 3.5 1.5 1.0 WACC2 Real, pre-tax (%) 5.7% 5.7% 5.7% 5.7% 5.7% Interest During $/kW/yr 646.0 130.6 513.7 132.3 143.6 Construction3 Asset Life1 Years 60 24 22 25 25 Total O&M Costs4 $/kW/yr 140.0 36.0 134.0 25.0 34.0 Thermal Efficiency5 Net, HHV (%) 33% 100% 100% 100% 100% Emissions Rate6 Tonne/GJ 0 0 0 0 0 Fuel Price7 $/MWh 2.75 0.00 0.00 0.00 0.00 Emissions Price8 $/tonne 22.0 22.0 22.0 22.0 22.0 Long-term Waste $/MWh 3.3 0.0 0.0 0.0 0.0 and Decommissioning9 Total Fixed Costs10 $/kW/yr 385.1 161.6 484.6 174.9 236.5 Assumed Load Factor11 % 90% 24% 40% 29% 29% Total Variable Costs10 $/MWh 11.6 0.0 0.0 0.0 0.0 Total Levelised Cost10 $/MWh 60.5 76.9 138.3 68.9 93.1 1 Source: IEA World Energy Investment Outlook, 2014. Cost data taken for the Middle East, and represents overnight capital cost. 2 NERA Assumptions on the Weighted Average Cost of Capital for a contracted IPP in the GCC. 3 Interest During Construction calculated assuming capital costs are incurred at a constant rate throughout the construction period. 4 Source: IEA World Energy Investment Outlook, 2014. Cost Data taken for the Middle East. We make the simplifying assumption that all O&M costs are fixed. 5 Source: IEA World Energy Investment Outlook, 2014. Data taken for the Middle East, and converted from Gross LHV to Net HHV using standard conversion rates. 6 NERA assumptions based on data from various sources. 7 Illustrative assumptions on on delivered fuel prices to generation plant in the GCC in 2020, based broadly on the principle of net back pricing of fossil fuels to the region relative to EU and Asian benchmarks - see below. The figures shown are for 2020. We recognise there is some variation in fuel prices used in the electricity sectors of the GCC, but for the purpose of this analysis we assume one fuel price across the region that, for example, ignores the potentially distortionary effects of fuel subsidies. 8 Source: IEA World Energy Outlook, 2014 (“New Policies” scenario). The IEA WEO has no carbon pricing information for the GCC, so we take the value presented for the European Union. While we do not necessarily anticipate that power generators in the GCC will face the costs of carbon emissions (through, for instance, a CO2 tax or permitting scheme) in the foreseeable future, we have assumed GCC governments plan their power systems in a way that values reductions in CO2 emissions, which is justified on the basis that we observe programmes for developing low carbon generation sources are being pursued in some jurisdictions. 9 Source: Mott MacDonald, UK Electricity Generation Costs Update, June 2010. 10 Calculated from above data. 11 NERA assumptions based on data from various sources. 12 The IEA WEO data suggests CCGT+CHP technology has an extremely high thermal efficiency, which we assume reflects the usage of heat, eg. for desalination. The feasibility of developing CHP varies across geographies, so we only consider the gas-fired CCGT technology in this study. Table 1. The Levelised Costs of Selected Generation Technologies continued www.nera.com 21 1 LCOE is defined as the Net Present Value (NPV) of costs for a particular generation technology, divided by the net present value of its expected production. NPVs are calculated over the life of the technology. 2 OCGTs are also widely referred to, particularly in North America, as Combustion Turbines. 3 This area covers Kuwait, Qatar, Bahrain, Abu Dhabi, Oman, and the Eastern Operating Area of Saudi Arabia, which are connected by a transmission system known as the GCC Interconnector. 4 Aurora XMP is vended by EPIS Inc. NERA’s modelling framework, which uses the Aurora platform, has been developed and tested extensively through project work for a range of clients in the GCC, including the Saudi Electric Company and the GCC Interconnection Authority. 5 The need for reserves may also increase in systems with high penetrations of wind and/or PV due to a reduction in system inertia as a result of having less “spinning” plant on the system. 6 We assume the new nuclear units in the UAE will come online come-what-may, so the model does not decide whether or when to deploy these units. 7 In this modelling, we assume a profile of production from solar that varies across hours. This hourly profile is known by the model, with no uncertainty around it. Hence, this modelling would not pick up the reduction in the capacity value of solar that comes from uncertainty around its output. This approach is, however, arguably consistent with the approach taken for other plant, as we do not model random outages for thermal technologies like CCGT and nuclear. 8 OPWP Announces New Power and Water Procurement Arrangements, OPWP, 30 January 2014. URL: http:// www.omanpwp.com/Docs/OPWP%20Announces%20New%20Power%20and%20Water%20Procurement%20 Arrangements.pdf 9 Source: IEA Website, visited on 15 September 2015. URL: http://www.worldenergyoutlook.org/weomodel/ investmentcosts/ Notes Report Qualifications/Assumptions & Limiting Conditions NERA shall not have any liability to any third party in respect of this report or any actions taken or decisions made as a consequence of the results, advice or recommendations set forth herein. This report does not represent investment advice or provide an opinion regarding the fairness of any transaction to any and all parties. 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