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Western Canada is endowed with considerable reserves of deep un-mineable coal, which can be converted to syngas by means of a gasification process called underground coal gasification (UCG). The syngas can be transformed into hydrogen (H 2 ) through commercially available technologies employed in conventional fossil-fuel based H 2 production pathways. This paper presents a data-intensive model to evaluate life cycle GHG emissions in H 2 production from UCG with and without CCS. Enhanced oil recovery (EOR) was considered as a sequestration method and included in the LCA. The life cycle GHG emissions are calculated to be 0.91 and 18.00 kg-CO 2 -eq/kg-H 2 in H 2 production from UCG with and without CCS, respectively. In addition, a detailed analysis of the influence of key UCG parameters, i.e., H 2 O-to-O 2 injection ratio, ground water influx, and steam-to-carbon ratio in syngas conversion, is completed on the results. The advantage of adopting UCG–CCS technology for H 2 production is realized over the predominant steam methane reforming (SMR) process; around 15.3 million tonnes of GHG emissions can be mitigated to achieve the projected SCO production rate from the bitumen upgrading in 2022. Furthermore, the sensitivity analysis showed that the life cycle GHG emissions is sensitive to the heat exchanger efficiency and the separation efficiency of the pressure swing adsorption (PSA) unit, with increasing values of these parameters causing an increase and a decrease in the magnitude of life cycle GHG emissions, respectively. [ABSTRACT FROM AUTHOR]

• Novel deep reinforcement learning-accelerated energy management system is developed. • Energy management of a hybrid grid-connected desalination process is investigated. • The partial observability dilemma involved in PV-powered energy systems is resolved. • Developed model was superior to 4 advanced deep reinforcement learning algorithms. • Developed algorithm was benchmarked against a powerful energy management system. This paper proposes a novel deep reinforcement learning-accelerated energy management system for a hybrid grid-connected photovoltaic-reverse osmosis-pressure retarded osmosis desalination plant. The energy management problem is formulated as a partially observable Markov decision process by using historical photovoltaic (PV) power data in order to cope with uncertainties related to the generation of solar power and provide more information regarding the true state of the system. The soft actor-critic (SAC) algorithm is employed as the core of the energy management system to maximize water production rate and contaminant removal efficiency while minimizing the supplied power from the external grid. We introduce 1-dimensional convolutional neural networks (1-D CNNs) to the actor, critic, and value function networks of the SAC algorithm to address the partial observability dilemma involved in PV-powered energy systems, extract essential features from the PV power time series, and achieve immensely improved performance ultimately. Furthermore, it is assumed that the proposed CNN-SAC algorithm does not have access to the current output power data of the PV system. The development of more practical energy management systems necessitates this assumption, and we demonstrate that the proposed method is capable of forecasting the current PV power data. The superiority of the CNN-SAC model is verified by comparing its learning performance and simulation results with those of four state-of-the-art deep reinforcement learning algorithms: Deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), twin delayed DDPG (TD3), and vanilla SAC. The results show that the CNN-SAC model outperforms the benchmark methods in terms of effective solar energy exploitation and power scheduling, manifesting the necessity of exploiting historical PV power data and 1-D CNNs. Moreover, the CNN-SAC algorithm is benchmarked against a powerful energy management system we developed in our previous investigation by studying three scenarios, and it is demonstrated that considerable improvement in energy efficiency can be obtained without using any solar power generation forecasting algorithm. By conducting ablation studies, the critical contribution of the introduced 1-D CNN is demonstrated, and we highlight the significance of providing historical PV power data for substantial performance enhancement. The average and standard deviation of evaluation scores obtained during the last stages of training reveal that the 1-D CNN significantly improves the final performance and stability of the SAC algorithm. These results demonstrate that the modifications we detail in our investigation render deep reinforcement learning algorithms extremely powerful for the energy management of PV-powered microgrids, including PV-driven reverse osmosis desalination plants. [ABSTRACT FROM AUTHOR]

This study developed a novel assessment framework to analyze long-term energy transition in the road transport sector in which various technology options, market shares, policy measures, costs, and greenhouse gas emissions are considered in a single framework analysis. A data-intensive model was developed with the Low Emissions Analysis Platform (LEAP) and used to analyze policy scenarios up to 2050 for Alberta, Canada, a hydrocarbon-rich province with an emission-intensive energy sector. Three key policy measures – carbon pricing, zero-emission vehicle sales mandate, and incentivization – were analyzed in nine individual and combined policy scenarios. The transition to both hydrogen fuel cell electric vehicles and battery electric vehicles was assessed for all vehicle categories. Each fuel's full energy supply chain was modelled, including resource extraction, conversion, transmission and distribution, and fuelling, allowing for final and primary energy analysis. The findings show that carbon price and zero-emission vehicle incentives do not effectively lower greenhouse gas emissions on their own; zero-emission vehicle mandates are needed to transition the sector to a low-carbon energy system. The system-wide greenhouse gas emission footprints of hydrogen and battery electric vehicles are significantly below conventional vehicles in all cases. Scenarios biased towards battery electric vehicles had the most favorable results. The greenhouse gas emission footprint of hydrogen vehicles supplied by auto-thermal reforming with 91% carbon capture was lower than for battery electric vehicles powered by a primarily natural gas-based power grid. The findings on the effectiveness of carbon prices, incentives, and vehicle mandates should be considered by government policymakers aiming to reduce greenhouse gas emissions, infrastructure planners, and other energy stakeholders. [Display omitted] • Assessment of H 2 -FCEVs and BEVs for system-wide energy and emissions. • Impact of carbon price, ZEV sales mandate, and ZEV incentives analyzed up to 2050. • Evaluation of hydrogen production technologies including SMR and ATR with CCS. • Current policies result in 38% decline in system-wide emissions in 2050. • ZEV sales mandate is the most effective in reducing system-wide energy & emissions. [ABSTRACT FROM AUTHOR]

• Analyzed H 2 production from a small modular nuclear-powered-electrolysis plant. • Price-following H 2 dispatch model applied in low and high-carbon electricity markets. • H 2 supply cost is from CAD$2.11–2.77/kg in a low-carbon electricity market. • H 2 supply cost is from CAD$1.77–3.36/kg in a high-carbon electricity market. • H 2 from SMNPP-electrolysis can cost-compete with renewable-based H 2 production. Small modular nuclear reactor power plants (SMNRPPs) for hydrogen cogeneration are in early development, and their cost competitiveness for low-temperature electrolysis has not been well-studied. This study combines a quadratically-constrained economic dispatch model and a levelized cost analysis model to evaluate hydrogen cogeneration from a 300 MW SMNRPP coupled with proton exchange membrane electrolyzers. The SMNRPP adjusts its power output based on fluctuating electricity market prices and a time-invariant conversion value for electricity pricing arbitrage. We evaluated the SMNRPP-electrolysis system for 20 years in two case study locations and found the hydrogen supply cost ranged from CAD$1.77–3.36/kg and CAD$2.11–2.77/kg in electricity markets with high and low fossil fuel-dependence, respectively. The SMNRPP-electrolysis system is more cost-effective than fossil fuel-based hydrogen production at CAD$1.54/kg if the SMNRPP capital cost decreases to CAD$4,700/kW. This is because the SMNRPP capital cost accounts for 75% of the hydrogen supply cost variance, while other techno-economic parameters account for less than 27% of the supply cost variance. The system can achieve the lowest cost hydrogen production in under 17 years if the global learning rate and installation growth rates for SMNRPPs exceed 10% and 15%, respectively, to reach a 4.96 GW global footprint. The study outcomes can help formulate policies for SMNRPPs adoption in the hydrogen economy. [ABSTRACT FROM AUTHOR]

The diversification of Canadian oil sands markets is imperative for the long-term economic growth of oil sands products. To ensure a competitive place in the global market, supply chain costs of oil sands must be as low as possible. This study conducts a comparative techno-economic analysis of potential pathways for the transportation of Canadian oil sands products (synthetic crude oil and diluted bitumen) to seaport destinations in the Asia-Pacific region. We developed data-intensive techno-economic models to estimate total supply chain costs from the production site in Alberta to ports in China, Japan, and India. Four pathways were developed using production (steam assisted gravity drainage), transportation (production-upgrader-port in Vancouver), upgrading, and shipping operations. A sensitivity analysis was conducted to identify cost ranges with their occurrence probability measures and evaluate the effect of key parameters for each stage of operation. Supply chain costs (C$ per barrel of bitumen) to China, Japan, and India are from 61–87, 60–86, and 62–90, respectively. Overall supply chain costs of dilbit (a blend of bitumen and diluent) and synthetic crude oil (SCO) are affected most by production and upgrading costs. The production and upgrading costs are influenced by capital cost, while pipeline lifetime and capacity highly impact transportation (pipeline) and shipping costs, respectively. The developed models can be used to predict total supply chain costs of different pathways in Canadian oil sand markets. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions are currently at the crux of political, environmental, technological, and cultural discussions due to climate change. A drastic reduction of GHG emissions is needed in order to mitigate potentially catastrophic climate change impacts. Thus, thoroughly understanding emission sources is imperative. A disaggregated analysis of Canada’s future GHG emission projections has not yet been conducted. The objectives in this paper are to assess disaggregated GHG emissions in Canada for the years 2014, 2030, and 2050, and analyze the results through Sankey diagrams. Emissions are calculated using a bottom-up multi-regional accounting-based Long-range Energy Alternative Planning systems model. Each major economic sector in Canada is analyzed including the electricity generation, residential, commercial and institutional, industrial, transportation, and agriculture sectors. The emissions released in these sectors are traced to the resources and end-uses responsible. Results are presented for Canada and for provinces individually. GHG emissions contained in exported resources are evaluated. Results show that Canada’s business-as-usual GHG emissions will grow from 732 million tonnes in 2014 to 780 and 798 million tonnes in 2030 and 2050, respectively. Canada exports more emissions contained in resources than it emits. Per capita emissions intensity will fall by 14% between 2014 and 2050. Results are compared to climate targets and key areas of GHG mitigation potential are identified. Alberta’s oil and gas sector and Ontario’s transportation sector are the two single largest sectoral sources of emissions by 2050. This research can help policy makers, innovators, and the public better understand GHG emissions, which can lead to more effective GHG mitigation. [ABSTRACT FROM AUTHOR]

To ensure the use of water today does not damage the prospects for its use by future generations, there is need to understand long-term water demand and supply through energy production, conversion, and use. This study aims to develop an integrated framework to assess the long-term impacts of climate change scenarios on water requirements and greenhouse gas (GHG) emissions. The framework includes the integration of the Water Evaluation And Planning model (WEAP) and the Long-range Energy Alternative Planning Systems model (LEAP). As many countries are planning to move towards a cleaner electricity grid to mitigate climate change, this work attempts to present the impact of various scenarios on water demand, GHG emissions, and cost effectiveness. This is done by conducting a case study of the western Canadian province of Alberta where more than 85% of electricity is generated by fossil fuels. This paper provides a comprehensive overview of nine integrated LEAP-WEAP climate change scenarios for the years 2015–2050 by forecasting water consumption and greenhouse gas emissions from the power sector. The economic aspects of the developed scenarios are discussed in the form of a cost curve that shows the GHG saving potential, water use, and GHG mitigation costs for each scenario. For the Business-As-Usual (BAU) scenario (coal power phase out by 2030), GHG emissions and water demand will fall by 44% and 34%, respectively, in 2030. The integrated results show that the scenarios will mitigate carbon emissions but will result in higher water consumption, which will directly affect water resources in the region. Because of the high investment cost to install the considered renewable power plants in the climate change scenarios, the cost of mitigating carbon emissions in the power sector is high. Early coal-to-gas power plant conversion is the only scenario that is expected to save water (67 million m 3 ) and reduce emissions (40 million tonnes of CO 2 eq.) and be cost effective ($68/tonne of CO 2 eq.). These LEAP-WEAP model results can help create awareness among policy makers to understand the water-energy demand and supply relationship in a quantifiable way. [ABSTRACT FROM AUTHOR]

Growing concern over climate change has created pressure on the oil and gas industry to reduce their greenhouse gas emissions (GHG). There have been multiple well-to-tank + combustion (WTT + C) studies that have examined various crude oils in an attempt to determine their GHG emission intensities. The majority of these studies published deterministic point estimates with a limited sensitivity analysis. Due to the variation in results between studies and the lack of uncertainty analysis the usefulness of these studies to policy makers and industry representatives is limited. The goal of this study is to expand on the previous literature by identifying a range of WTT + C emissions for crude oils from Saudi Arabia, Venezuela, and Iran. First, the previously published FUN damental E N gineering Principl E s-based Mode L for Estimation of G reen H ouse G ases in C onventional C rude O ils (FUNNEL-GHG-CCO) was used to perform a WTT + C analysis of the crudes GHG emissions. Then a Monte Carlo simulation was carried out using existing literature to define input distributions for the key inputs. The resulting gasoline WTT + C GHG emission ranges are 113.6–138.5 (Venezuela High Steam), 101.6–109.9 (Venezuela Low Steam), 101.1–109.2 (Sirri, Iran), and 95.3–99.9 gCO 2 eq/MJ (Saudi Arabia). This result indicates that even when uncertainty is taken into account the Venezuelan high steam crude clearly has higher emissions than the Saudi Arabia crude. The results of this study will give policy makers and industry representatives a better understanding of how the WTT + C GHG emissions vary between various crude oils. [ABSTRACT FROM AUTHOR]

A life cycle assessment (LCA) is an extremely useful tool to assess the greenhouse gas (GHG) emissions associated with all the stages of a crude oil’s life from well-to-wheel (WTW). All of the WTW life cycle stages of crude oil consume energy and produce significant amounts of GHG emissions. The present study attempts to quantify the WTW life cycle GHG emissions for transportation fuels derived from five North American conventional crudes through the development of an LCA model called FUNNEL-GHG-CCO ( FUN damental E n gineering Principl E s-based Mode L for Estimation of G reen H ouse G ases in C onventional C rude O ils). This model estimates GHG emissions from all the life cycle stages from recovery of crude to the combustion of transportation fuels in vehicle engines. The contribution of recovery emissions in the total WTW GHG emissions ranges from 3.12% for Mars crude to 24.25% for California’s Kern County heavy oil. The transportation of crude oil and refined fuel contributes only 0.44–1.73% of the total WTW life cycle GHG emissions, depending on the transportation methods and total distance transported. The GHG emissions for refining were calculated from the amount of energy use in the refining of crude oil to produce transportation fuels. All the upstream GHG emissions were allocated to gasoline, diesel, and jet fuel. Refining GHG emissions vary from 13.66–18.70 g-CO 2 eq/MJ-gasoline, 9.71–15.33 g-CO 2 eq/MJ-diesel, and 6.38–9.92 g-CO 2 eq/MJ-jet fuel derived from Alaska North Slope and California’s Kern County heavy oil, respectively. The total WTW life cycle GHG emissions range from 97.55 g-CO 2 eq/MJ-gasoline derived from Mars crude to 127.74 g-CO 2 eq/MJ-gasoline derived from California’s Kern County heavy oil. [ABSTRACT FROM AUTHOR]

A model – FUNNEL-GHG-OS ( FUN damental E N gineering Principl E s-based Mode L for Estimation of G reen H ouse G ases in the O il S ands) was developed to estimate project-specific energy consumption and greenhouse gas emissions (GHGs) in major recovery and extraction processes in the oil sands, namely surface mining and in␣situ production. This model estimates consumption of diesel (4.4–7.1 MJ/GJ of bitumen), natural gas (52.7–86.4 MJ/GJ of bitumen) and electricity (1.8–2.1 kW h/GJ of bitumen) as fuels in surface mining. The model also estimates the consumption of natural gas (123–462.7 MJ/GJ of bitumen) and electricity (1.2–3.5 kW h/GJ of bitumen) in steam assisted gravity drainage (SAGD), based on fundamental engineering principles. Cogeneration in the oil sands, with excess electricity exported to Alberta’s grid, was also explored. Natural gas consumption forms a major portion of the total energy consumption in surface mining and SAGD and thus is a main contributor to GHG emissions. Emissions in surface mining and SAGD range from 4.4 to 7.4 gCO 2 eq/MJ of bitumen and 8.0 to 34.0 gCO 2 eq/MJ of bitumen, respectively, representing a wide range of variability in oil sands projects. Depending upon the cogeneration technology and the efficiency of the process, emissions in oil sands recovery and extraction can be reduced by 16–25% in surface mining and 33–48% in SAGD. Further, a sensitivity analysis was performed to determine the effects of key parameters on the GHG emissions in surface mining and SAGD. Temperature and the consumption of warm water in surface mining and the steam-to-oil ratio (SOR) in SAGD are major parameters affecting GHG emissions. The developed model can predict the energy consumption and emissions for surface mining and SAGD for oil sands. [ABSTRACT FROM AUTHOR]

• Life cycle assessment of utility-scale solar energy is performed. • Upstream production and assembly are the most emission-intensive life cycle stages. • Energy use during production and plant lifetime are the most sensitive parameters. • The system shows a net energy production with a net energy ratio up to 6.6. • The life cycle GHG emissions range from 98.3 to 149.3 g CO 2 eq /kWh. Grid-connected utility-scale solar PV has emerged as a potential pathway to ensure deep decarbonization of electricity in regions with fossil fuel-dominated energy mixes. Research on utility-scale solar PV projects mainly focuses on assessing technical or economic feasibility. Environmental performance assessments of large-scale solar applications are scarce. There is limited information on the greenhouse gas (GHG) emissions and energy footprints of utility-scale solar energy systems. Earlier studies conducted on small-scale solar systems have limited application in the grid system. We developed a comprehensive bottom-up life cycle assessment model to evaluate the life cycle GHG emissions and energy profiles of utility-scale solar photovoltaic (PV) system with lithium-ion battery storage to provide a consistent electricity supply to the grid with peak load options. We conducted a case study for a fossil fuel-based energy jurisdiction, Alberta (a western province in Canada). The results of the energy assessment show that raw material extraction, production, and assembly of solar panels are the key drivers, accounting for 53% of the total consumption. Energy consumed during battery manufacturing is responsible for 28%. The system shows a net energy production with a mean net energy ratio as high as 6.6 for two-axis sun tracking orientation. The life cycle GHG emissions range from 98.3 to 149.3 g CO 2 eq /kWh with a mean value of 123.8 g CO 2 eq /kWh. The largest emissions contribution is due to the manufacturing of batteries, 54% of the total emissions. The solar PV system offers a mean energy payback time of 3.8 years (with a range of 3.3 to 4.2 years). The results are highly sensitive to the expected lifetime of the system, the panel's peak wattage, and process energy consumption at various life cycle stages. [ABSTRACT FROM AUTHOR]

• Novel integrated assessment framework for electricity decarbonization. • Electricity generation greenhouse gases decrease 49% in the current policy scenario. • Electricity generation water consumption increases 22% in the current policy scenario. • Electricity decarbonization reduced the water consumption increase by 8% • The marginal abatement cost for decarbonization was found to be −$17.5/tonne. The generation of electricity can have major environmental impacts not only in terms of greenhouse gas (GHG) emissions but also in terms of water use. There has been limited integrated assessment of the environmental footprints of deep decarbonization of electricity generation. This research develops an integrated approach to evaluate the GHG emission mitigation, water footprints, and marginal abatement costs of electricity generation decarbonization pathways. The decarbonization of Canada's electricity sector was used as a case study. A Long-range Energy Alternative Planning model of the electricity generation sector was developed to determine future regional electricity generation technology mixes. A Water Evaluation and Planning model was also developed for the sector considering 530 power plants, 156 electricity generation water demand sites, and 74 major rivers. The energy and water-use models were integrated, and two scenarios were evaluated for a planning horizon of 2019–2050 using this integrated framework. The first scenario was based on current policy trajectories and the second was developed with a deep electricity decarbonization target of 100%. In the current policy scenario, water consumption and GHG emissions increased 22% and decreased 49%, respectively, by 2050 from 2019. The fully decarbonized scenario resulted in water consumption savings and negative marginal GHG abatement costs. The results quantify the co-benefits of decarbonizing electricity systems and show that transitioning toward renewables can reduce GHG emissions, water consumption, and system costs. These results may be important for policy development at the regional and national levels as well as internationally for jurisdictions looking to transition to carbon-free electricity. [ABSTRACT FROM AUTHOR]

• Provides key performance metrics for gas separation in electrochemical reactors. • Bench-scale demonstration of continuous operation for flue gas CO 2 capture. • Offers performance metrics to assess amine regeneration energy at scale. • Offers flexible and modular amine regeneration for 'plug-and-play' CO 2 capture. The electrochemically-mediated amine regeneration (EMAR) process uses electrons to modulate amine capacity to achieve CO 2 separation from flue gas as an alternative to the traditional thermal regeneration process for CO 2 capture. The EMAR separation scheme is validated in a batch system designed to evaluate efficiency losses. Current and voltage responses of the electrochemical process were analyzed in a flow system operated continuously for up to 50 h. An isothermal EMAR system can achieve separation efficiencies above 80% from a 15% CO 2 feed, which is representative of the CO 2 composition in a flue gas. This bench scale continuous system can operate at 40–80 kJ e /mol CO 2 with an amine regeneration of between 0.12 and 0.62 mol CO 2 /mol amine. The ability to separate CO 2 at high electron utilization and moderate electrical energy consumption will prompt future research into optimization of the electrochemical separation unit to obtain long-term and stable operations for flue gas scrubbing. [ABSTRACT FROM AUTHOR]

Displacement of fossil fuel-based power through biomass co-firing could reduce the greenhouse gas (GHG) emissions from fossil fuels. In this study, data-intensive techno-economic models were developed to evaluate different co-firing technologies as well as the configurations of these technologies. The models were developed to study 60 different scenarios involving various biomass feedstocks (wood chips, wheat straw, and forest residues) co-fired either with coal in a 500 MW subcritical pulverized coal (PC) plant or with natural gas in a 500 MW natural gas combined cycle (NGCC) plant to determine their technical potential and costs, as well as to determine environmental benefits. The results obtained reveal that the fully paid-off coal-fired power plant co-fired with forest residues is the most attractive option, having levelized costs of electricity (LCOE) of $53.12–$54.50/MW h and CO 2 abatement costs of $27.41–$31.15/tCO 2 . When whole forest chips are co-fired with coal in a fully paid-off plant, the LCOE and CO 2 abatement costs range from $54.68 to $56.41/MW h and $35.60 to $41.78/tCO 2 , respectively. The LCOE and CO 2 abatement costs for straw range from $54.62 to $57.35/MW h and $35.07 to $38.48/tCO 2 , respectively. [ABSTRACT FROM AUTHOR]

• The large-scale production pathway of high-performance perovskite solar cell modules is developed. • Scale factors were developed for each cost component of perovskite solar cells. • Perovskite solar panels for residential home application were designed. • The levelized cost of electricity is from 11.4 to 14.8 ¢/kWh. • The minimum sustainable price is $0.53-$0.9/W, comparable to commercial photovoltaic technologies. Perovskite solar cells (PSCs) have shown remarkable progress in recent years. Different materials and structures have been developed to improve the photoconversion efficiency and operational stability of PSCs. However, the economic and technical impacts of materials and design choice on the large-scale deployment are not well addressed in the literature. In this research, a pathway for producing titanium dioxide (TiO 2) nanorod-based perovskite solar modules was established and their manufacturing cost was estimated through the development of data-intensive, bottom-up techno-economic models. Material, utilities, and equipment requirements from available laboratory data to a mass production annual capacity of up to 21 MW were estimated through the development of scale factors. The minimum sustainable price and levelized cost of electricity were calculated. The direct manufacturing cost of the reference PSC module was estimated at $80.23/m2 and $0.73/W with a production capacity of 3.5 MWp. These costs decline to $47.15/m2 and $0.43/W at 21 MW production capacity. Material costs dominate the overall costs, fluorine-doped tin oxide glass being the most expensive material. The perovskite solar cell panels, when installed in residential homes in Alberta, Canada, were calculated to have a competitive levelized cost of electricity ranging from 7 to 17 cents per kWh. However, the cost was found to be extremely sensitive to the module efficiency, lifetime, and the solar insolation at the location of installation. [ABSTRACT FROM AUTHOR]

• A techno-economic assessment of battery storage systems was conducted. • Data-intensive cost models were developed for five battery technologies. • Four different stationary applications of batteries were evaluated. • The levelized cost of storage decreases with increases in discharge duration. • The levelized cost of storage is lowest for bulk energy storage. The decision to use a certain type of energy storage system for a stationary application depends largely on its economic performance. The electro-chemical energy storage systems are well known for transportation and portable applications. The evaluation of techno-economic feasibility of different electro-chemical energy storage systems for utility-scale stationary applications has received less attention. In this study, bottom-up techno-economic models were developed for five electro-chemical battery storage technologies: sodium-sulfur, lithium-ion, valve-regulated lead-acid, nickel–cadmium, and vanadium redox flow. Four stationary application scenarios – bulk energy storage, transmission and distribution investment deferral, frequency regulation, and support of voltage regulation – were assessed to evaluate the techno-economic feasibility. Life cycle costs were estimated for capacities of 5–100 MW for bulk energy storage, 5–25 MW for transmission and distribution investment deferral, 5–100 MW for frequency regulation, and 5–30 MW for support of voltage regulation. Sensitivity and uncertainty analyses were carried out to examine the extent to which the levelized cost of storage is affected by changes in input parameters. The base case results show that the levelized cost of storage are in the range of $199–$941/MWh for the sodium-sulfur, $180–$1032/MWh for the lithium-ion, $410–$1184/MWh for the valve-regulated lead-acid, $802–$1991/MWh for the nickel–cadmium, and $267–$3794/MWh for the vanadium redox flow, depending on the application scenario. The results also show that when the discharge duration increases, the levelized cost of storage decreases because of economies of scale. A vanadium redox flow battery is not economically suitable for frequency regulation and support of voltage regulation given its short discharge duration. [ABSTRACT FROM AUTHOR]