Without these resources, electricity costs rise rapidly as CO 2 limits near zero

Mitigating climate change while fueling economic growth requires decarbonizing the electricity sector at reasonable cost. Some strategies focus on wind and solar energy, supported by energy storage and demand flexibility. Others also harness “firm” low-carbon resources such as nuclear, reservoir hydro, geothermal, bioenergy, and fossil plants capturing CO 2 . This paper presents a comprehensive techno-economic evaluation of two pathways: one reliant on wind, solar, and batteries, and another also including firm low-carbon options (nuclear, bioenergy, and natural gas with carbon capture and sequestration). Across all cases, the least-cost strategy to decarbonize electricity includes one or more firm low-carbon resources. Without these resources, electricity costs rise rapidly as CO 2 limits approach zero. Batteries and demand flexibility do not substitute for firm resources. Improving the capabilities and spurring adoption of firm low-carbon technologies are key research and policy goals.

We investigate the role of firm low-carbon resources in decarbonizing power generation in combination with variable renewable resources, battery energy storage, demand flexibility, and long-distance transmission. We evaluate nearly 1,000 cases covering varying CO 2 limits, technological uncertainties, and geographic differences in demand and renewable resource potential. Availability of firm low-carbon technologies, including nuclear, natural gas with carbon capture and sequestration, and bioenergy, reduces electricity costs by 10%–62% across fully decarbonized cases. Below 50 gCO 2 /kWh, these resources lower costs in the vast majority of cases. Additionally, as emissions limits decrease, installed capacity of several resources changes non-monotonically. This underscores the need to evaluate near-term policy and investment decisions based on contributions to long-term decarbonization rather than interim goals. Installed capacity for all resources is also strongly affected by uncertain technology parameters. This emphasizes the importance of a broad research portfolio and flexible policy support that expands rather than constrains future options.

Introduction

1 United Nations Framework Convention on Climate Change. (2015). Paris Agreement. Available at: https://unfccc.int/files/essential_background/convention/application/pdf/english_paris_agreement.pdf. , 2 United Nations Framework Convention on Climate Change. Paris Agreement - Status of Ratification. Available at: https://unfccc.int/process/the-paris-agreement/status-of-ratification. (Accessed: 2nd May 2018). 2 emissions from electricity generation must fall nearly to zero 3 IPCC Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. , 4 Krey V.

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Despite general agreement on the need for deep decarbonization of the electric power sector, views differ as to the relative importance of various low-carbon electricity resources in near-zero-emissions power systems.

Technological developments have enlarged the array of low-carbon electricity generation resources to include solar, wind, hydro, biomass, nuclear, geothermal, and fossil energy with carbon capture and sequestration (CCS). Technologies for energy storage and for managing electricity demand are also available. The economic and operational characteristics of these resources vary, as does their ability to contribute to meeting electricity demand reliably. As a result of this diversity, power systems are likely to benefit from harnessing a blend of resources, with the various resource types playing complementary roles and adding distinct value to the overall mix of energy services.

1. “Fuel-saving” variable renewable energy (VRE) resources. These include wind power, solar photovoltaics (PV), concentrating solar power, and run-of-river hydropower. They harness renewable energy inputs (wind, solar insolation, water) that vary on timescales ranging from seconds to hours to seasons, have zero (or near-zero) variable costs, and have no fuel costs. At lower penetration levels, they may displace the need for firm capacity, but, at higher shares, capacity needs are driven by periods with low VRE availability. At high energy shares, these technologies therefore contribute value primarily by displacing other higher variable cost generating technologies whenever available and reducing the total fuel consumption and variable costs of the system.

2. “Fast-burst” balancing resources. These include short-duration energy storage (e.g., Li-ion batteries), flexible demand (or schedulable loads), and demand response (or price-responsive demand curtailment). They are either energy constrained (storage, demand flexibility) or have very high variable cost (demand curtailment). These technologies are therefore poorly suited to operating continuously over long periods of time and are better used during high-value periods when relatively fast bursts of power or quick demand adjustments are needed to balance supply and demand.

3. 12 Keppler, J.H., and Cometto, M. (2012). Nuclear energy and renewables: system effects in low-carbon electricity systems. NEA no. 7056. Available at: https://www.oecd-nea.org/ndd/pubs/2012/7056-system-effects.pdf. , 13 Lokhov A. Technical and Economic Aspects of Load Following with Nuclear Power Plants. , 14 Ponciroli R. Wang Y. Zhou Z. Botterud A. Jenkins J. Vilim R. Ganda F. Profitability evaluation of load-following nuclear units with physics-induced operational constraints. , 15 Jenkins J.D. Zhou Z. Ponciroli R. Vilim R.B. Ganda F. de Sisternes F. Botterud A. The benefits of nuclear flexibility in power system operations with renewable energy. 16 Craig M.T. Jaramillo P. Zhai H. Klima K. The economic merits of flexible carbon capture and sequestration as a compliance strategy with the clean power plan. , 17 Heuberger C.F. Staffell I. Shah N. Mac Dowell N. Quantifying the value of CCS for the future electricity system. 7 Sanchez D.L. Nelson J.H. Johnston J. Mileva A. Kammen D.M. Biomass enables the transition to a carbon-negative power system across western North America. “Firm” low-carbon resources. These are technologies that can be counted on to meet demand when needed in all seasons and over long durations (e.g., weeks or longer) and include nuclear power plants capable of flexible operations,hydro plants with high-capacity reservoirs, coal and natural gas plants with CCS and capable of flexible operations,geothermal power, and biomass- and biogas-fueled power plants.

Electricity generation technologies have traditionally been classified based on their relative variable costs and the resulting frequency with which they are called upon to meet electricity demand or “load”; e.g., “baseload,” “load-following,” and “peaking” resources. This classification is no longer meaningful in power systems with substantial penetration of wind and solar energy, since dispatch of each technology is also driven by the irregular variability of these renewable resources. Moreover, most available low-carbon technologies are capital intensive and have very low variable costs. In this context, the distinguishing attributes of electricity technologies relate more to their resource availability and ability to adapt production output in order to meet instantaneous demand. We therefore propose a new taxonomy that divides low-carbon electricity technologies into three different sub-categories (see Figure S1 ):

18 Becker S.

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Kammen D.M. Power system balancing for deep decarbonization of the electricity sector. , 28 Schlachtberger D.P.

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Greiner M.

von Bremen L.

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Hand M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. , 34 Safaei H.

Keith D.W. How much bulk energy storage is needed to decarbonize electricity?. , 35 de Sisternes F.J.

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Jacobson M.Z. Flexibility mechanisms and pathways to a highly renewable US electricity future. 20 Frew B.A.

Becker S.

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Jacobson M.Z. Flexibility mechanisms and pathways to a highly renewable US electricity future. , 27 MacDonald A.E.

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Baldwin S.F. Envisioning a renewable electricity future for the United States. In light of recent cost improvements and the rapid expansion of wind power and solar photovoltaics, many recent papers have explored opportunities and challenges associated with achieving very high shares of these VRE resources in power systems.Much of this work has focused on how to provide the enhanced operational flexibility on various time scales (from seconds to seasons) needed to balance variable output from high shares of wind and solar energy,including the potential role of energy storage,demand-side flexibility,and long-distance transmission expansion to smooth variability of renewable output across wider geographic areas.

17 Heuberger C.F.

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Kammen D.M. Power system balancing for deep decarbonization of the electricity sector. , 34 Safaei H.

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Jenkins J.D.

Botterud A. The value of energy storage in decarbonizing the electricity sector. , 36 Heuberger C.F.

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Kammen D.M. Power system balancing for deep decarbonization of the electricity sector. Some of this work has excluded firm low-carbon resources ex ante, in part because of the societal challenges or current costs associated with some of these resources. Other researchhas found that harnessing firm low-carbon resources capable of responding to variations in both demand and renewable energy output can lower the cost of low-carbon power systems by reducing the amount of needed generating and storage capacity, improving asset utilization, and avoiding substantial curtailment of renewable energy output. These studies have typically focused on the role of a specific resource (e.g., energy storageor CCS) and have explored a relatively narrow range of possible uncertainties.

37 Peters G.P.

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Fuss S.

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Le Quéré C.

Nakicenovic N. Key indicators to track current progress and future ambition of the Paris agreement. 37 Peters G.P.

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Nakicenovic N. Key indicators to track current progress and future ambition of the Paris agreement. Global deployment of nuclear and CCS is lagging well behind the pace envisioned by scenarios to limit global warming to 2°C in the 2014 Intergovernmental Panel on Climate Change assessment report.Both technologies face a range of challenges to greater adoption, including high construction costs and financial risks, technology immaturity (in the case of CCS and next-generation nuclear designs), and risk (both real and perceived). Use of biomass for energy is currently on pace with global 2°C scenarios,but large-scale reliance on bioenergy for power generation competes with other land uses, including food and environmental conservation, as well as other uses for bioenergy in transportation, heat, and industrial sectors. Other firm low-carbon resources are constrained to specific favorable geographies (conventional geothermal, reservoir hydropower), entail significant environmental impacts (reservoir hydro), or remain pre-commercial (enhanced geothermal energy systems). Overcoming challenges to large-scale use of these firm low-carbon resources may prove difficult. Whether it makes sense to take on this task depends partly on the benefits associated with having one or more viable firm low-carbon resources available to contribute to power sector decarbonization.

In this paper we provide a more comprehensive evaluation of the economic and operational benefits of firm low-carbon technologies in achieving deep decarbonization targets, with a focus on nuclear, natural gas with CCS, biogas, and biomass. The analysis examines the interactions between these firm low-carbon resources; fuel-saving variable renewable resources; and fast-burst resources, including short-duration battery energy storage and demand-side flexibility. It also investigates the impact of long-distance transmission interconnections.

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Delarue E. Applicability of a clustered unit commitment model in power system modeling. , 42 Poncelet, K. (2018). Long-term energy-system optimization models: capturing the challenges of integrating intermittent renewable energy sources and assessing the suitability for descriptive scenario analyses. PhD thesis (KU Leuven). https://doi.org/10.13140/RG.2.2.21586.45762. We use an advanced electric power system investment and operations modelto compare, under several increasingly ambitious decarbonization targets, the economic performance of two kinds of power systems: those that include firm low-carbon technologies among the available resources, and those that exclude these firm resources ex ante. Operational details captured by the model include a full year of hourly chronological variability in both renewable energy output and electricity demand and detailed power system operating constraints such as integer power plant start-up and shut-down costs, minimum stable output limits for thermal power plants, and limits on hourly changes in power plant output. Commonly used but simpler models can result in significant errors due to abstraction of relevant power system details and the failure to account for the full variability of renewable resources and inter-temporal constraints on energy storage and thermal power plants.

A large number of scenarios are analyzed. First, we account for geographic differences in renewable resource potential and patterns of demand using data from two dissimilar US regions: a “northern” system with the demand profile and relatively modest renewable resource potential typical of New England (peak demand of 34 GW at 4:00 pm on a July weekday), and a “southern” system with the demand profile and higher renewable resource availability characteristic of the Electricity Reliability Corporation of Texas region (peak demand of 94 GW at 4:00 pm on an August weekday).

Table 1 Technological Assumptions Technology Conservative Mid-range Very Low Solar ($/kW-AC) 1,800 a a 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 900 b b 50% cost reduction from conservative. 670 c c 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL Wind ($/kW) 1,455 d d 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 1,091 e e 25% cost reduction from conservative. 927 f f 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 4-hr Li-ion battery ($/kWh) 440 g g 44 Lazard. (2017). Lazard’s levelized cost of storage analysis — version 3.0. Available at: https://www.lazard.com/media/450338/lazard-levelized-cost-of-storage-version-30.pdf. From Lazard 220 h h 50% cost reduction from conservative. 110 i i 75% cost reduction from conservative. 6-hr Li-ion battery ($/kWh) 420 i i 75% cost reduction from conservative. 210 j j 44 Lazard. (2017). Lazard’s levelized cost of storage analysis — version 3.0. Available at: https://www.lazard.com/media/450338/lazard-levelized-cost-of-storage-version-30.pdf. From Lazard 105 i i 75% cost reduction from conservative. Natural gas CCGT with CCS ($/kW) (CO 2 capture rate) NA 1,720 (90%) k k 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 1,050 (100%) l l Net power Nth of a kind plant, May 2015 briefing. Nuclear ($/kW) (size) 7,000 (1,000 MW) m m 45 Georgia Public Service Commission Joint Post Hearing Brief of Public Interest Advocacy staff and Georgia Power Company. Based on Georgia Public Service Commission. 4,700 (1,000 MW) n n 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 4,200 (300 MW) o o 46 IEA

NEA Projected Costs of Generating Electricity. From IEA and NEA Biomass ($/kW) (maximum energy share) ($/MMBTU) 3,800 (5%) (3) p p 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 3,800 (5%) (3) p p 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 3,400 (35%) (7) q q 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL Biogas ($/kW) (maximum energy share) ($/MMBTU) NA 890 (2%) (7.5) r r 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL 790 (10%) (15) r r 43 NREL (National Renewable Energy Laboratory). (2017). 2017 Annual Technology Baseline. Available at: https://atb.nrel.gov/electricity/2017/. From NREL CCGT, combined-cycle gas turbine; NA, not available. Table 2 Technological Scenarios Scenario 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 VRE and storage a a This analysis is limited to lithium-ion battery energy storage systems, which are currently widely scalable, face no geographic constraints, and are expected to benefit from further cost reductions due to economies of scale, learning-by-doing, and spillovers from battery production for electric vehicles. Longer-duration pumped-hydro storage resources are difficult to expand due to siting challenges, and medium- and long-duration energy storage options that may provide longer-duration storage capacity (e.g., days rather than hours) are currently too costly or face scalability challenges. However, if a storage technology capable of supplying several days or more of sustained power output becomes economically and technically viable, this technology could serve as a firm low-carbon resource (unlike shorter-duration battery storage). It would be productive for future research to explore the impact of long-duration energy storage options on deep decarbonization and the cost and performance parameters necessary for very-long-duration storage to cost-effectively contribute to decarbonization. b b These resources are grouped in order to keep the number of scenarios manageable. Solar, wind, and battery storage are grouped to reflect the more rapid pace of likely cost declines for these technologies. Nuclear and gas with CCS are likewise grouped based on their relatively high costs and slower rate of likely cost declines. Finally, biomass and biogas resources are grouped to reflect their common feedstocks, with the three technology cases reflecting increasing levels of feedstock supply. C M M M L L L M M M L L L M M M L L L Nuclear and CCS b b These resources are grouped in order to keep the number of scenarios manageable. Solar, wind, and battery storage are grouped to reflect the more rapid pace of likely cost declines for these technologies. Nuclear and gas with CCS are likewise grouped based on their relatively high costs and slower rate of likely cost declines. Finally, biomass and biogas resources are grouped to reflect their common feedstocks, with the three technology cases reflecting increasing levels of feedstock supply. C C C C C C C M M M M M M L L L L L L Biomass and biogas b b These resources are grouped in order to keep the number of scenarios manageable. Solar, wind, and battery storage are grouped to reflect the more rapid pace of likely cost declines for these technologies. Nuclear and gas with CCS are likewise grouped based on their relatively high costs and slower rate of likely cost declines. Finally, biomass and biogas resources are grouped to reflect their common feedstocks, with the three technology cases reflecting increasing levels of feedstock supply. C C M L C M L C M L C M L C M L C M L C, conservative; M, mid-range; L, very low technology cost assumptions ( Table 1 ). Second, we account for uncertainties surrounding future technology costs and availabilities by introducing discrete cost assumptions (“conservative,” “mid-range,” and “very low”; see Table 1 ) for each of three groupings of technologies: (1) VRE resources (onshore wind and solar PV) and Li-ion battery energy storage (with 4 or 6 hr of output at maximum discharge rate; see footnotes in Table 2 ). (2) Light-water nuclear reactors and natural gas plants with CCS. (3) Biogas- and solid biomass-fueled plants (see footnotes in Table 2 ). Using these cost assumptions, we construct and analyze 19 technology cost and availability scenarios (see Table 2 ). Additionally, we analyze the impact of increasing flexibility in demand scheduling and price-responsive demand curtailment, as well as the effect of increasing long-distance transmission interconnection capacity between the northern and southern systems.

2 emissions limits, from 200 gCO 2 /kWh down to zero emissions. For context, the direct emissions rate of CO 2 from power generation in the United States in 2017 was 436.6 g/kWh. Emissions reductions pledged by the United States under the Paris Agreement use 2005 as a baseline year, in which the CO 2 emissions rate from power generation was 595.8 g/kWh. 47 US Energy Information Administration. (2018). Carbon dioxide emissions from energy consumption: electric power sector. Available at: https://www.eia.gov/totalenergy/data/monthly/pdf/sec12_9.pdf. , 48 US Energy Information Administration. (2018). Electricity data browser: net generation by energy source: electric utilities. Available at: https://www.eia.gov/electricity/data/browser/. Third, we analyze the economic impact of different emissions reduction targets by modeling each technology and regional scenario subject to seven progressively more stringent COemissions limits, from 200 gCO/kWh down to zero emissions. For context, the direct emissions rate of COfrom power generation in the United States in 2017 was 436.6 g/kWh. Emissions reductions pledged by the United States under the Paris Agreement use 2005 as a baseline year, in which the COemissions rate from power generation was 595.8 g/kWh.

Altogether we evaluate 912 distinct scenarios. A “core” set of 532 scenarios comprises the 19 technology availability and cost scenarios in each of the two power systems, subject to seven different decarbonization targets with and without firm low-carbon resources. We also consider 380 “sensitivity” scenarios that explore the effects of five different levels of demand-side resource availability and two levels of long-distance transmission capacity linking the two power systems.

To our knowledge, this is the first work to systematically explore the feasibility and cost of achieving deep decarbonization goals (up to 100% reductions in power sector CO 2 emissions) across such a wide range of conditions and technology cost projections. This comprehensive analysis increases the robustness of our findings.

The next two sections of the paper present the results of our analysis, focusing first on our core scenarios and subsequently on the impact of demand-side flexibility and increased regional interconnections. This is followed by a discussion of the policy implications and recommendations, and a description of the experimental procedure and assumptions used in our work.