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This paper analyses the scale of asset stranding of global natural gas production and transmission infrastructure between 2015 and 2060 using Gas-GAME-Spot, an agent-based gas-sector model. It extends the existing modelling efforts by considering contract constraints in short-term gas sales and explicitly simulating trade in two types of spot markets. The study also contributes to the methodologies of stranded asset analysis by taking into account two aspects which are commonly overlooked: market fluctuation subject to the changing export capacities and the impacts of market signals on investor decision making. The results of the base scenario indicate that if gas demand follows current policy expectations, the scale of asset stranding is likely to be limited. This is attributed to supply capacity shortage of medium-sized exporters due to a slowing of their investment. Moreover, two alternative scenarios show that, when the markets face sudden reduction in demand, they leverage the flexibilities in their long-term contracts and opt more gas through spot trade. Though the issue of stranding is not significant globally in these scenarios, exporting regions with high short-run delivery costs, especially North America and Australia, have higher risks of asset stranding as they are arguably less competitive in spot sales.

The uncertainties in gas demand levels and geopolitical issues may lead to significant changes in global gas trade. This paper uses an agent-based model to simulate the alternative market futures under two demand trajectories: a baseline following current policy pledges until 2060 and another where demand shifts to a lower level in 2030. Endogenously generated capacity investments are driven by long-term bilateral contracts between importers and exporters, where investors are assumed to evaluate the potential risks of demand changes while making their decisions. The results suggest that, when the demand decreases in 2030, the Middle East takes the dominant position in Eastern Asia, whereas this role is occupied by North America in the current policy scenario. In addition, the impacts of a 25% tariff by China on U.S. natural gas are studied for both scenarios. The revenue of North American gas trade is only marginally affected by this tariff. Under the normal demand trajectory, the tariff influences the Chinese market more notably in the longer term when global supply is tightened by decommissioning. In the case of lower global gas demand, the market share of Russia in Western Europe could be threatened by increasing North American export there.

Different technologies produce hydrogen with varying cost and carbon footprints over the entire resource supply chain and manufacturing steps. This paper examines the relative costs of carbon mitigation from a life cycle perspective for 12 different hydrogen production techniques using fossil fuels, nuclear energy and renewable sources by technology substitution. Production costs and life cycle emissions are parameterized and re-estimated from currently available assessments to produce robust ranges to describe uncertainties for each technology. Hydrogen production routes are then compared using a combination of metrics, levelized cost of carbon mitigation and the proportional decarbonization benchmarked against steam methane reforming, to provide a clearer picture of the relative merits of various hydrogen production pathways, the limitations of technologies and the research challenges that need to be addressed for cost-effective decarbonization pathways. The results show that there is a trade-off between the cost of mitigation and the proportion of decarbonization achieved. The most cost-effective methods of decarbonization still utilize fossil feedstocks due to their low cost of extraction and processing, but only offer moderate decarbonisation levels due to previous underestimations of supply chain emissions contributions. Methane pyrolysis may be the most cost-effective short-term abatement solution, but its emissions reduction performance is heavily dependent on managing supply chain emissions whilst cost effectiveness is governed by the price of solid carbon. Renewable electrolytic routes offer significantly higher emissions reductions, but production routes are more complex than those that utilise naturally-occurring energy-dense fuels and hydrogen costs are high at modest renewable energy capacity factors. Nuclear routes are highly cost-effective mitigation options, but could suffer from regionally varied perceptions of safety and concerns regarding proliferation and the available data lacks depth and transparency. Better-performing fossil-based hydrogen production technologies with lower decarbonization fractions will be required to minimise the total cost of decarbonization but may not be commensurate with ambitious climate targets.

The power sector needs to ensure a rapid transition towards a low-carbon energy system to avoid the dangerous consequences of greenhouse gas emissions. Storage technologies are a promising option to provide the power system with the flexibility required when intermittent renewables are present in the electricity generation mix. This paper focuses on the role of electricity storage in energy systems with high shares of renewable sources. The study encompasses a model comparison approach where four models ( G E N e S Y S - M O D , M U S E , N A T E M , and u r b s − M X ) are used to analyse the storage uptake in North America. The analysis addresses the conditions affecting storage uptake in each country and its dependence on resource availability, technology costs, and public policies. Results show that storage may promote emissions reduction at lower costs when renewable mandates are in place whereas in presence of carbon taxes, renewables may compete with other low-carbon options. The study also highlights the main modelling approach shortcomings in the modelling of electricity storage in integrated assessment models.

Producción Científica, Europe’s capacity to explore the envisaged pathways that achieve its near- and long-term energy and climate objectives needs to be significantly enhanced. In this perspective, we discuss how this capacity is supported by energy and climate-economy models, and how international modelling teams are organised within structured communication channels and consortia as well as coordinate multi-model analyses to provide robust scientific evidence. Noting the lack of such a dedicated channel for the highly active yet currently fragmented European modelling landscape, we highlight the importance of transparency of modelling capabilities and processes, harmonisation of modelling parameters, disclosure of input and output datasets, interlinkages among models of different geographic granularity, and employment of models that transcend the highly harmonised core of tools used in model inter-comparisons. Finally, drawing from the COVID-19 pandemic, we discuss the need to expand the modelling comfort zone, by exploring extreme scenarios, disruptive innovations, and questions that transcend the energy and climate goals across the sustainability spectrum. A comprehensive and comprehensible multi-model framework offers a real example of “collective” science diplomacy, as an instrument to further support the ambitious goals of the EU Green Deal, in compliance with the EU claim to responsible research., H2020 European Commission Projects “PARIS REINFORCE” under Grant Agreement No. 820846, “LOCOMOTION” under Grant Agreement No. 821105, “SENTINEL” under Grant Agreement No. 837089, and “NAVIGATE” under Grant Agreement No. 821124.

Agricultural direct energy use is responsible for about 1–2% of global emissions and is the major emitting sector for methane (2.9 GtCO2eq y−1) and nitrous oxide (2.3 GtCO2eq y−1). In the last century, farm mechanisation has brought higher productivity levels and lower land demands at the expense of an increase in fossil energy and agrochemicals use. The expected increase in certain food and bioenergy crops and the uncertain mitigation options available for non-CO2 emissions make of vital importance the assessment of the use of energy and the related emissions attributable to this sector. The aim of this paper is to present a simulation framework able to forecast energy demand, technological diffusion, required investment and land use change of specific agricultural crops. MUSE-Ag & LU, a novel energy systems-oriented agricultural and land use model, has been used for this purpose. As case study, four main crops (maize, soybean, wheat and rice) have been modelled in mainland China. Besides conventional direct energy use, the model considers inputs such as fertiliser and labour demand. Outputs suggest that the modernisation of agricultural processes in China could have the capacity to reduce by 2050 on-farm emissions intensity from 0.024 to 0.016 GtCO2eq PJcrop−1 (−35.6%), requiring a necessary total investment of approximately 319.4 billion 2017$US.

Fuel cell developments are driven by the need for more efficient and cleaner energy provision; however, current costs make it uneconomic in wastewater treatment plants. Interventions via policy instruments and business models may be required for cost reduction until the fuel cell is driven purely by market forces. In this work a novel market potential assessment methodology is developed and applied to quantify the impact of various interventions on biogas fuelled solid oxide fuel cell cost reduction and synthesize pathways to its commercialisation. The method is applied to 6181 plants in 27 European countries. Results show that 71% cost reduction is required for a medium sized fuel cell to be market driven. Existing incentives can trigger cost reduction by 13–38% but are not able to sustain it until the fuel cell is market driven. Innovations in business models, and incentivising business models instead of technologies can trigger and sustain cost reduction. Results also show that under today’s high capital cost, the number of economically attractive plants required to install fuel cells are lowest when business models are incentivised compared to other interventions. Incentivising new business models to encourage innovation in the sector has more impact that incentivising technologies. The framework is also relevant for creating narratives around the commercialisation of new technologies.

Existing studies on high renewable share electricity systems are usually based on least cost optimization. Running the related models can be time-consuming when space-time resolution is high. This study investigates the optimal mix of climate-related energies for most countries in the world with optimization models based on three criteria: cost, residual load variability, and portfolio output variability. The objectives of minimizing residual load variability and portfolio output variability are to ensure the overall complementarity of the generation portfolio, which may result in less conventional dispatchable units needed in a system. Compromise solutions based on the three objectives are proposed as the optimal mix. This method can produce solutions in acceptable modelling time, and considers the portfolio output characteristics which can make higher climate-related energy penetration more practical. The results show that the compromise solutions can effectively minimize the three objective values in most countries. The results also suggest that wind power is crucial in higher renewable share systems while solar power does not reach over 50% capacity share.

Graphical abstract, Highlights • Large on-site survey to provide real-world investment data of 108 iron-steel plants. • Agent-based integrated assessment framework to assess industry fuel-switching. • Fuel-switching assessment including 4 investment metrics and 5 comparable scenarios. • Partial-equilibrium agent-based scenarios of an evolving socio-technical system. • Partial-equilibrium agent interactions produce non-smooth gas uptake patterns., This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India’s iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of ~12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price).

Within Canada, Mexico or the United States, policy-making organizations are evaluating energy markets and energy trade within their own borders often by ignoring how these countries’ energy systems are integrated with each other. These analytical gaps provided the main motivation for the Energy Modeling Forum (EMF) 34 study on North American energy integration and trade. This paper compares North American results from 17 models and discusses their policy motivation. Oil and natural gas production in the three major countries are modestly sensitive to crude oil and natural gas price changes, although these elasticities are below unity. Carbon taxes displace coal and some natural gas with renewables within all three power markets. Lower natural gas prices replace coal and some renewables with natural gas within electric generation. Higher intermittent renewable penetration in the power sector displaces coal and some natural gas. A key conclusion is that much remains to be done in integrating future analyses and in sharing and improving the quality and consistency of the underlying data.

In Brazil, bioelectricity generation from sugarcane bagasse and black liquor is regarded as a sustainable electricity supply option. However, questions regarding land use, investment decisions, and demand for paper, ethanol and sugar make its future role uncertain. The aim of this paper is to present a novel modelling framework based on a soft-link between a multi-sectoral Brazilian integrated assessment model (MUSE-Brazil) and an electricity portfolio optimisation model (EPOM). The proposed framework is capable of dynamically simulating sectoral electricity demand, regional bioenergy production under land use constraints and optimal power sector technological shares in each of the electricity subsystems. Considering Brazil under a 2 °C carbon budget, two scenarios based on economic attractiveness of producing second-generation ethanol have been investigated. Under the scenario where second-generation ethanol is not produced, outputs indicate that by 2050, Brazil would increase sugarcane and wood production by 68% and 49% respectively without causing direct or indirect deforestation. Agriculture intensification is evidenced as an alternative for reducing land use disruptions. Bioelectricity share is projected to remain around 9–10%. However, if second generation ethanol becomes cost-effective, thus limiting bagasse availability, the share of bioelectricity production would decrease to approximately 7.7%, with natural gas-fired plants playing a stronger role in the future power system expansion, causing an increase on electricity sector emissions.

China is the leading ammonia producer and relies on a coal-based technology which makes the already energy intensive Haber-Bosch process, one of the most emission intensive in the world. This work is the first to propose an agent-based modelling framework to model the Chinese ammonia industry as it characterises the specific goals and barriers towards fuel switching and carbon capture and storage adoption for small, medium, and large enterprises either private or state-owned. The results show that facilitated access to capital makes investments in sustainable technologies more attractive for all firms, especially for small and medium enterprises. Without policy instruments such as carbon price, the decrease in emissions in the long-term is due to investments in natural gas-based technologies, as they typically have lower capital and operating costs, and also lower electricity consumption than coal-based production. Conversely, with policy instruments in place, a strong decrease in emissions occurs between 2060 and 2080 due to investors choosing natural gas and biomethane-based technologies, with carbon capture and storage. In the long term, natural gas and biomethane could compete, with the outcome depending on infrastructure, supply chain availability and land use constraints.

This study analyzes the road freight sector of São Paulo state to identify the best options to reduce greenhouse gases emissions and local pollutants, such as particulate matter, nitrogen oxides, carbon monoxide, and hydrocarbons. Additionally, the investment cost of each vehicle is also analyzed. Results show that electric options, including hybrid, battery, and hydrogen fuel-cell electric vehicles represent the best options to reduce pollutants and greenhouse gases emissions concomitantly, but considerable barriers for their deployment are still in place. With little long-term planning on the state level, electrification of the transport system, in combination with increased renewable electricity generation, would require considerable financial support to achieve the desired emissions reductions without increasing energy insecurity.

Projections of decarbonisation pathways have typically involved reducing dependence on natural gas grids via greater electrification of heat using heat pumps or even electric heaters. However, many technical, economic and consumer barriers to electrification of heat persist. The gas network holds value in relation to flexibility of operation, requiring simpler control and enabling less expensive storage. There may be value in retaining and repurposing gas infrastructure where there are feasible routes to decarbonisation. This study quantifies and analyses the decarbonisation potential associated with the conversion of gas grids to deliver hydrogen, focusing on supply chains. Routes to produce hydrogen for gas grids are categorised as: reforming natural gas with (or without) carbon capture and storage (CCS); gasification of coal with (or without) CCS; gasification of biomass with (or without) CCS; electrolysis using low carbon electricity. The overall range of greenhouse gas emissions across routes is extremely large, from − 371 to 642 gCO 2 eq/kW h H2 . Therefore, when including supply chain emissions, hydrogen can have a range of carbon intensities and cannot be assumed to be low carbon. Emissions estimates for natural gas reforming with CCS lie in the range of 23–150 g/kW h H2 , with CCS typically reducing CO 2 emissions by 75%. Hydrogen from electrolysis ranges from 24 to 178 gCO 2 eq/kW h H2 for renewable electricity sources, where wind electricity results in the lowest CO 2 emissions. Solar PV electricity typically exhibits higher emissions and varies significantly by geographical region. The emissions from upstream supply chains is a major contributor to total emissions and varies considerably across different routes to hydrogen. Biomass gasification is characterised by very large negative emissions in the supply chain and very large positive emissions in the gasification process. Therefore, improvements in total emissions are large if even small improvements to gasification emissions can be made, either through process efficiency or CCS capture rate.

Spatial resolution is often cited as a crucial determinant of results from energy systems models. However, there is no study that comprehensively analyses the effect of spatial resolution. This paper addresses this gap by applying the Heat Infrastructure and Technology heat decarbonisation optimisation model in six UK Local Authorities representing a range of rural/urban areas, at three levels of spatial resolution, in order to systematically compare results. Results show the importance of spatial resolution for optimal allocation of heat supply technologies and infrastructure across different urban/rural areas. Firstly, for the studied cases, differences of up to 30% in heat network uptake were observed when comparing results between different resolutions for a given area. Secondly, for areas that generally exhibit the high and low extremes of linear heat density, results are less dependent on spatial resolution. Also, spatial resolution effects are more significant when there is higher variability of linear heat density throughout zones. Finally, results show that it is important to use finer resolutions when using optimisation models to inform detailed network planning and expansion. Higher spatial resolutions provide more detailed information on zones that act as anchors that can seed network growth and on location of network supply technologies.

This paper studies the economic feasibility of installing hydrogen networks for decarbonising heat in urban areas. The study uses the Heat Infrastructure and Technology (HIT) spatially-resolved optimisation model to trade-off energy supply, infrastructure and end-use technology costs for the most important heat-related energy vectors; gas, heat, electricity, and hydrogen. Two model formulations are applied to UK urban area: one with an independent hydrogen network, and one that allows for retrofitting the gas network into hydrogen. Results show that for average hydrogen price projections, cost-effective pathways for heat decarbonisation towards 2050 comprise including heat networks supplied by a combination of district level heat pumps and gas boilers in the domestic and commercial sectors, and hydrogen boilers in the domestic sector. For a low hydrogen price scenario, when retrofitting the gas network into hydrogen, a cost-effective pathway is replacing gas by hydrogen boilers in the commercial sector, and a mixture of hydrogen boilers and heat networks supplied by district level heat pumps, gas, and hydrogen boilers for the domestic sector. Compared to the first modelled year, CO2 emissions reductions of 88% are achieved by 2050. These results build on previous research on the role of hydrogen in cost-effective heat decarbonisation pathways.

International shipping faces strong challenges with new legally binding air quality regulations and a 50% decarbonisation target by 2050. Liquefied natural gas (LNG) is a widely used alternative to liquid fossil fuels, but methane emissions reduce its overall climate benefit. This study utilises new emissions measurements and supply-chain data to conduct a comprehensive environmental life cycle and cost assessment of LNG as a shipping fuel, compared to heavy fuel oil (HFO), marine diesel oil (MDO), methanol and prospective renewable fuels (hydrogen, ammonia, biogas and biomethanol). LNG gives improved air quality impacts, reduced fuel costs and moderate climate benefits compared to liquid fossil fuels, but with large variation across different LNG engine types. Methane slip from some engines is unacceptably high, whereas the best performing LNG engine offers up to 28% reduction in global warming potential when combined with the best-case LNG supply chain. Total methane emissions must be reduced to 0.8–1.6% to ensure climate benefit is realised across all timescales compared to current liquid fuels. However, it is no longer acceptable to merely match incumbent fuels; progress must be made towards decarbonisation targets. With methane emissions reduced to 0.5% of throughput, energy efficiency must increase 35% to meet a 50% decarbonisation target.

Natural gas plays an important role in the Southern cone energy system, and is expected to increase in primary supply in the future. This paper presents a new energy systems model for the Southern Cone region of Latin America, covering five regions (Argentina, Bolivia, South and Centre Chile, North Chile, and Brazil) with the aim to explore, up to 2030, the interplay between (i) the expected consumption of natural gas for electricity generation and end-use consumption (i.e. residential, commercial, transport and industry) in each country, (ii) the inter- and intra-country potential role as producer and consumer of natural gas, and (iii) the possible supply network of LNG and natural gas via pipeline and domestic production. It is found that, under a Constrained Investment Scenario, the gross domestic gas production of the Southern Cone from 2012 to 2030 could be 62 Tcf, whereas under an Unconstrained Scenario, it could rise to 75 Tcf. This highlights the economic potential of the unconventional gas resources of Argentina and projections of associated gas from the Campos and Santos basins in Brazil. However, accessing these resources poses financial challenges. Nonetheless, results clearly indicate significant potential for an increase in regional natural gas trade in the Southern Cone.

Decarbonising electricity systems is essential for mitigating climate change. Future systems will likely incorporate higher penetrations of intermittent renewable and inflexible nuclear power. This will significantly impact on system operations, particularly the requirements for flexibility in terms of reserves and the cost of such services. This paper estimates the interrelated changes in wholesale electricity and reserve prices using two novel methods. Firstly, it simulates the short run marginal cost of generation using a unit commitment model with post-processing to achieve realistic prices. It also introduces a new reserve price model, which mimics actual operation by first calculating the day ahead schedules and then letting deviations from schedule drive reserve prices. The UK is used as a case study to compare these models with traditional methods from the literature. The model gives good agreement with and historic prices in 2015. In a 2035 scenario, increased renewables penetration reduces mean electricity prices, and leads to price spikes due to expensive plants being brought online briefly to cope with net load variations. Contrary to views previously held in literature, a renewable intensive scenario does not lead to a higher reserve price than a fossil fuel intensive scenario. Demand response technology is shown to offer sizeable economic benefits when maintaining system balance. More broadly, this framework can be used to evaluate the economics of providing reserve services by aggregating decentralised energy resources such as heat pumps, micro-CHP and electric vehicles.

Whether and how long-term energy and climate targets can be reached depend on a range of interlinked factors: technology, economy, environment, policy, and society at large. Integrated assessment models of climate change or energy-system models have limited representations of societal transformations, such as behavior of various actors, transformation dynamics in time, and heterogeneity across and within societies. After reviewing the state of the art, we propose a research agenda to guide experiments to integrate more insights from social sciences into models: (1) map and assess societal assumptions in existing models, (2) conduct empirical research on generalizable and quantifiable patterns to be integrated into models, and (3) build and extensively validate modified or new models. Our proposed agenda offers three benefits: interdisciplinary learning between modelers and social scientists, improved models with a more complete representation of multifaceted reality, and identification of new and more effective solutions to energy and climate challenges.

Variable renewable energy is set to become a key energy source worldwide, but there is concern regarding the impact of the intermittency of its output when penetration is high. Energy system models need to tackle this issue by improving modelling resolution and scope. To allow for such modelling, more and better input datasets are needed on variable renewable energy potentials and yields. These need to be of global scope, of sufficient spatial and temporal resolution, and generated with transparent, consistent methods. This study develops the methods and applies it to generate these datasets at subnational and hourly resolution. The assessment is carried out for wind and solar technologies with consistent constraints including geographical, social and economic aspects. Features from the OpenStreetMap are converted into land cover and land use datasets and applied. Hourly energy output is simulated using NASA MERRA-2 meteorological datasets, reconciled with resource maps from the Global Wind Atlas and Global Solar Atlas platforms. Capacity supply curves are provided for 731 terrestrial zones and 339 offshore zones worldwide, along with corresponding hourly output profiles over a 10-year simulation period. The proposed energy potentials are relative conservative compared with other studies. The datasets can serve as input for regional or global energy system models when analyzing high variable renewable energy shares.

There is strong agreement across the energy modelling community that wind energy will be a key route to mitigating carbon emissions in the electricity sector. This paper presents a Geospatial Information System methodology for estimating spatially-resolved levelised cost of electricity for offshore wind, globally. The principal spatial characteristics of capital costs are transmission distance (i.e. the distance to grid connection) and water depth, because of the disparate costs of turbine foundation technologies. High resolution capacity factors are estimated from a bottom-up estimation of global wind speeds calculated from several decades of wind speed data. A technology-rich description of fixed and floating foundation types allows the levelised cost of electricity to be calculated for 1 × 1 km grid cells, relative to location-specific annual energy production, and accounting for exclusion areas, array losses and turbine availability. These data can be used to assess the economically viable offshore wind energy potential, globally and on a country basis, and can serve as inputs to energy systems models.

Due to its low global share of direct energy consumption and greenhouse gas emissions (1–2%), the implications of technological transitions in the agricultural and forestry sector on the energy system have been overlooked. This paper introduces the Agriculture and Land Use Sector module part of the ModUlar energy System Environment (MUSE), a novel energy system simulation model. The study presents a generalisable method that enables energy modellers to characterise agricultural technologies within an energy system modelling framework. Different mechanisation processes were characterised to simulate intensification/extensification transitions in the sector and its wider implications in the energy and land use system aiming at providing reliable non-energy outputs similarly to those found in dedicated land use models. Additionally, a forest growth model has been integrated to explore the role of reforestation alongside decarbonisation measures in the energy system in achieving carbon mitigation pathways. To illustrate the model's capabilities, Brazil is used as case study. Outputs suggest that by 2030 under a 2 °C mitigation scenario, most of Brazil agricultural production would move from ‘transitional’ to ‘modern’ practices, improving productivity and reducing deforestation rates, at the expense of higher energy and fertiliser demand. By mid-century Brazil has the potential to liberate around 24.4 Mha of agricultural land, where large-scale reforestation could have the capacity to sequester around 5.6 GtCO2, alleviating mitigation efforts in the energy system, especially reducing carbon capture and storage technology investments in the industry and power sector.

With UK housing contributing almost one-third of national final energy consumption, energy research and policy are striving to understand how residents perceive and use domestic energy-saving technologies, including smart heating controls. Smart heating controls have been projected to generate significant energy savings, but there is little evidence to suggest that they are more usable than traditional thermostats, and that they cause residents to use their heating more efficiently. Our study aims to contribute to this evidence, by assessing how the perception and use of heating controls changed for a small group of residents in London, United Kingdom, after their dwellings were upgraded to smart heating controls. The residents were part of an interesting niche supplied by a district heat network (DHN) through slow-response underfloor heating systems, originally controlled by traditional programmable thermostats which residents used infrequently and perceived as complicated. The study focused on assessing one particular heating behaviour of residents: using the controls to program heating schedules that reflect their occupancy patterns, a behaviour of great interest to DHN operators due to its potential to help improve the accuracy of forecasting residential heating demand. The study also assessed residents’ perceptions of the usability of heating controls and of their heating systems’ matching of temperature adjustments. We found that after upgrading to smart thermostats, more residents programmed heating schedules to match their occupancy patterns, but they also increased the frequency of ad-hoc temperature and schedule adjustments, indicating that heating demand may vary significantly outside of pre-set schedules, and not conform as well to predictable patterns as expected. Perceived usability of the heating controls also increased after the upgrade, but perceptions of whether the system matched temperature adjustments showed that respondents still found it difficult to understand the intrinsic slow response of their underfloor heating system. Our findings provide insight into the perception and specific use of heating controls for a unique sample of residents, “twice removed” from the most intuitive methods of heating control and challenged by unique issues with their heating supply and system. However, the study is limited by the small sample size and lack of long-term monitoring of heating behaviour, and we recommend further research on larger samples and over longer timescales in order to investigate long-term change and whether this could improve demand forecasting accuracy and contribute to achieving higher efficiency, and thus reduced emissions, in DHN operation.

Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment) but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available.

Energy-related investment decisions in the buildings sector are heterogeneous in that the outcome for each individual varies according to budget, values, and perception of a technology, even if an apparently identical decision task is faced. In particular, the rate of adoption of new energy-efficient technologies is often hard to model and underlines the need for an advanced approach to capture diversity in decision-making, and enable the inclusion of economic, comfort, environmental and social aspects. This paper presents an enhanced agent-based model that captures several characteristics of consumer behaviour that influence investment decisions. Multiple agents with different objectives, search strategies, and decision methods are implemented. A case study is presented which illustrates the benefits of the approach for the residential sector in the UK. The agent-based method shows diversity in investment decisions, without requiring the constraints on uptake needed in many models. This leads to a range of technologies in the market during a transition phase, continuous investment in low capital cost technologies, and eventually the emergence of a low carbon system based on new mass market technologies. The system that emerges is vastly different from one observed when economically rational investment is assumed and uptake constraints are applied.

Higher penetration of renewable sources of energy is essential for mitigating climate change. This introduces problems related to the balance of supply and demand. Instances in which the generation from intermittent and inflexible sources is in excess of system load are expected to increase in low carbon futures. Curtailment is likely to involve high constraint payments to renewable sources, and failing to curtail threatens the stability of the system. This work investigates a solution that makes use of residential heating systems to absorb the excess generation. Consumers are incentivised to increase consumption via a demand turn up mechanism that sets the electricity price to zero when excess generation occurs. The reduction in electricity price significantly weakens the economic case of dwelling-scale micro-cogeneration units. But technologies that use electricity are able to charge the thermal store when free electricity is available and discharge it when electricity prices are high. Such actions reduce the equivalent annual cost by 50 percent for a resistive heater and by 60 percent for a heat pump. Without disincentives, resistive heaters are likely to be chosen over heat pumps since they are easy to install, do not involve high upfront costs and can provide significant economic benefits.

Limiting cumulative carbon emissions to keep global temperature increase to well below 2 °C (and as low as 1.5 °C) is an extremely challenging task, requiring rapid reduction in the carbon intensity of all sectors of the economy and with limited leeway for residual emissions. Addressing residual emissions in ‘challenging-to-decarbonise’ sectors such as the industrial and aviation sectors relies on the development and commercialization of innovative advanced technologies, currently still in their infancy. The aim of this study was to (a) explore the role of advanced technologies in achieving deep decarbonisation of the energy system and (b) provide technology-specific details of how rapid and deep carbon intensity reductions can be achieved in the energy demand sectors. This was done using TIAM-Grantham – a linear cost optimization model of the global energy system with a detailed representation of demand-side technologies. We find that the inclusion of advanced technologies in the demand sectors, together with energy demand reduction through behavioural changes, enables the model to achieve the rapid and deep decarbonisation of the energy system associated with limiting global warming to below 2 °C whilst at the same time reduces reliance on negative emissions technologies by up to ∼18% compared to the same scenario with a standard set of technologies. Realising such advanced technologies at commercial scales, as well as achieving such significant reductions in energy demand, represents a major challenge for policy makers, businesses and civil society. There is an urgent need for continued R&D efforts in the demand sectors to ensure that advanced technologies become commercially available when we need them and to avoid the gamble of overreliance on negative emissions technologies to offset residual emissions.

Natural gas is typically considered to be the cleaner-burning fossil fuel that could play an important role within a restricted carbon budget. While natural gas emits less CO2 when burned than other fossil fuels, its main constituent is methane, which has a much stronger climate forcing impact than CO2 in the short term. Estimates of methane emissions in the natural gas supply chain have been the subject of much controversy, due to uncertainties associated with estimation methods, data quality, and assumptions used. This Perspective presents a comprehensive compilation of estimated CO2 and methane emissions across the global natural gas supply chain, with the aim of providing a balanced insight for academia, industry, and policy makers by summarizing the reported data, locating the areas of major uncertainty, and identifying where further work is needed to reduce or remove this uncertainty. Overall, the range of documented estimates of methane emissions across the supply chain is vast among an aggregation of different geological formations, technologies, plant age, gas composition, and regional regulation, not to mention differences in estimation methods. Estimates of combined methane and CO2 emissions ranged from 2 to 42 g CO2 eq/MJ HHV, while methane-only emissions ranged from 0.2% to 10% of produced methane. The methane emissions at the extraction stage are the most contentious issue, with limited data available but potentially large impacts associated with well completions for unconventional gas, liquids unloading, and also the transmission stage. From the range of literature estimates, a constrained range of emissions was estimated that reflects the most recent and reliable estimates: total supply chain GHG emissions were estimated to be between 3.6 and 42.4 g CO2 eq/MJ HHV, with a central estimate of 10.5. The presence of “super emitters”, a small number of facilities or equipment that cause extremely high emissions, is found across all supply chain stages creating a highly skewed emissions distribution. However, various new technologies, mitigation and maintenance approaches, and legislation are driving significant reductions in methane leakage across the natural gas supply chain.

As the energy system progresses towards full decarbonization, natural gas could play an important role in it with its relatively low carbon characteristics and its abundant supply. At the core of the paper is a modelling analysis of the European Union (EU) natural gas network resilience in case of short-term supply disruption or unexpected increase in demand. The adopted linear programming model solves for the most cost effective transmission of gas flows, capacity and storage utilization in an interconnected EU gas system. Results presented in the paper show a significant increase in liquefied natural gas (LNG) costs (+40%) when commodity price increases (+40%) and LNG prices decreases (−20%), and an equally significant decline in transport and LNG costs (−30%,-50%) when storage volumes varies (−35%,+35%).The analysis highlights a complementary role between LNG and storage in ensuring a cost-effective response to a natural gas supply shock. It also indicates that LNG alone is inadequate in providing system resilience in case of an emergency in supply, stressing the importance of storage in the gas market and its intrinsic value in the system. The study emphasizes the need to further investigate the reliability and value of gas storage to reinforce energy security in Europe.

This work presents the COMET (Cities Optimisation Model for Energy Technologies) model, a spatially-resolved urban energy systems model that takes into account energy service demands for heating, cooling, electricity, and transport, and finds cost-effective pathways for supplying these demands under carbon constraints, trading-off energy supply, network infrastructure, and end-use technologies. Spatially-resolved energy service demands were obtained for the city of Sao Paulo, and six scenarios were modelled. Results show that district cooling is cost-effective in the highest linear cooling density zones, with full penetration in zones with over 1100 kWh/m by 2050. This threshold diminishes with tighter carbon constraints. Heating is electrified in all scenarios, with electric boilers and air-source heat pumps being the main supply technologies for the domestic and commercial sectors respectively by 2050. In the most carbon constrained scenario with a medium decarbonised electricity grid, ground source heat pumps and hydrogen boilers appear as transition technologies between 2030 and 2045 for the commercial and domestic sectors respectively, reaching 95% and 40% of each sector’s heat installed capacity in 2030. In the transport sector, ethanol cars replace gasoline, diesel, and compressed natural gas cars; compressed natural gas buses replace diesel and electric buses; and lorries continue using diesel. In carbon constrained scenarios, higher penetrations of electric cars and buses are obtained, while no change is observed for lorries. Finally, the most expensive scenario was only 6% more expensive than the reference scenario, meaning that achieving decarbonisation targets is not much costlier when comparing scenarios from a system-wide perspective.

In the urgent quest for solutions to mitigate climate change, the industry is one of the most challenging sectors to decarbonize. In this work, a novel simulation framework is presented to model the investment decisions in industry, the Industrial Sector Module (ISM) of the ModUlar energy system Simulation Environment (MUSE). This work uses the ISM to quantify effects of three combined measures for CO2 emission reduction in industry, i.e. fuel switching, electrification, and adoption of Carbon Capture and Storage (CCS) and to simulate plausible scenarios (base scenario and climate ambitious scenario) for curbing emissions in the iron and steel sector in the USA between 2010 and 2050. Results show that when the climate ambitious scenario is applied, the cumulative emissions into the atmosphere (2,158 Mt CO2) are reduced by 40% in comparison to the base scenario (3,608 Mt CO2). This decarbonization gap between both scenarios intensifies over time; in the year 2050, the CO2 intensity in the climate ambitious scenario is 81% lower in comparison to the base scenario. The study shows that major contributions to industry decarbonization can come from the further uptake of secondary steel production. Results show also that a carbon tax drives the decarbonization process but is not sufficient on its own. In addition, the uptake of innovative low-carbon breakthrough technologies is necessary. It is concluded that industrial electrification is counterproductive for climate change mitigation, if electricity is not provided by low-carbon sources. Overall, fuel switching, industrial electrification, and CCS adoption as single measures have a limited decarbonization impact, compared to an integrated approach that implements all the measures together providing a much more attractive solution for CO2 mitigation.

International shipping provides 80–90% of global trade, but strict environmental regulations around NOX, SOX and greenhouse gas (GHG) emissions are set to cause major technological shifts. The pathway to achieving the international target of 50% GHG reduction by 2050 is unclear, but numerous promising options exist. This study provides a holistic assessment of these options and their combined potential to decarbonise international shipping, from a technology, environmental and policy perspective. Liquefied natural gas (LNG) is reaching mainstream and provides 20–30% CO2 reductions whilst minimising SOX and other emissions. Costs are favourable, but GHG benefits are reduced by methane slip, which varies across engine types. Biofuels, hydrogen, nuclear and carbon capture and storage (CCS) could all decarbonise much further, but each faces significant barriers around their economics, resource potentials and public acceptability. Regarding efficiency measures, considerable fuel and GHG savings could be attained by slow-steaming, ship design changes and utilising renewable resources. There is clearly no single route and a multifaceted response is required for deep decarbonisation. The scale of this challenge is explored by estimating the combined decarbonisation potential of multiple options. Achieving 50% decarbonisation with LNG or electric propulsion would likely require 4 or more complementary efficiency measures to be applied simultaneously. Broadly, larger GHG reductions require stronger policy and may differentiate between short- and long-term approaches. With LNG being economically feasible and offering moderate environmental benefits, this may have short-term promise with minor policy intervention. Longer term, deeper decarbonisation will require strong financial incentives. Lowest-cost policy options should be fuel- or technology-agnostic, internationally applied and will require action now to ensure targets are met by 2050.

Recent low gas prices have greatly increased pressure on drilling companies to reduce costs and increase efficiency. Field trials have shown that implementing automation can dramatically reduce drilling costs by reducing the time required to drill wells. This study uses the DYNamic upstreAm gAs MOdel (DYNAAMO), a new techno-economic, bottom-up model of natural gas supply, to quantitatively assess the economic impact of lower drilling costs on the US upstream gas industry. A sensitivity analysis of three key economic indicators is presented, with results quoted for the most common field types currently producing, including unconventional and offshore gas. While all operating environments show increased profitability from drilling automation, it is found that conventional onshore reserves can benefit to the greatest extent. For large gas fields, a 50% reduction in drilling costs is found to reduce initial project breakevens by up to 17 million USD per billion cubic metres (MUSD/BCM) and mid-plateau breakevens by up to 8 MUSD/BCM. In this same scenario, additional volumes of around 160 BCM of unconventional gas are shown to become commercial due to both the lower costs of additional production wells in mature fields and the viability of developing new resources held in smaller fields. The capital efficiency of onshore projects increases by 50%&ndash, 100%, with initial project net present value (NPV) gains of up to 32%.

Coupling the electricity and heat sectors is receiving interest as a potential source of flexibility to help absorb surplus renewable electricity. The flexibility afforded by power-to-heat systems in dwellings has yet to be quantified in terms of time, energy and costs, and especially in cases where homeowners are heterogeneous prosumers. Flexibility quantification whilst accounting for prosumer heterogeneity is non-trivial. Therefore in this work a novel two-step optimization framework is proposed to quantify the potential of prosumers to absorb surplus renewable electricity through the integration of air source heat pumps and thermal energy storage. The first step is formulated as a multi-period mixed integer linear programming problem to determine the optimal energy system, and the quantity of surplus electricity absorbed. The second step is formulated as a linear programming problem to determine the price a prosumer will accept for absorbing surplus electricity, and thus the number of active prosumers in the market. A case study of 445 prosumers is presented to illustrate the approach. Results show that the number of active prosumers is affected by the quantity of absorbed electricity, frequency of requests, the price offered by aggregators and how prosumers determine the acceptable value of flexibility provided. This study is a step towards reducing the need for renewable curtailment and increasing pricing transparency in relation to demand-side response.

This article evaluates the impact of a range of dynamic performance parameters on the techno-economics of fuel cell based micro co-generation. The main novelties in methodology are: (1) Analysis in the context of future power system decarbonisation, (2) Use of the Long Run Marginal Cost of electricity, (3) Combination of the above with dynamic aspects such as start-up cost, ramping limit, turn down ratio, minimum up time and minimum down time and (4) Identification of sensitive parameters for future research. To this end it combines a national level energy systems model with an individual heating system model. A case study of the United Kingdom is considered for the year 2035. Economic viability of fuel cell based micro co-generation hinges upon the use of an optimized control strategy. With such a control strategy, a hot start-up approach offers much greater economic potential than a cold start-up approach. The best case ratio of maximum allowable hot standby power to the nominal value is 4.2 while the ratio for cold start is only 1.1. Combinations involving low ramping limits less than 70 W/min and limited turn down ratios above 35% need to be avoided as they seriously hinder economic performance.

There is an ongoing debate over future decarbonisation of gas networks using biomethane, and increasingly hydrogen, in gas network infrastructure. Some emerging research presents gas network decarbonisation options as a tractable alternative to ‘all-electric’ scenarios that use electric appliances to deliver the traditional gas services such as heating and cooking. However, there is some uncertainty as to the technical feasibility, cost and carbon emissions of gas network decarbonisation options. In response to this debate the Sustainable Gas Institute at Imperial College London has conducted a rigorous systematic review of the evidence surrounding gas network decarbonisation options. The study focuses on the technologies used to generate biomethane and hydrogen, and examines the technical potentials, economic costs and emissions associated with the full supply chains involved. The following summarises the main findings of this research. The report concludes that there are a number of options that could significantly decarbonise the gas network, and doing so would provide energy system flexibility utilising existing assets. However, these options will be more expensive than the existing gas system, and the GHG intensity of these options may vary significantly. In addition, more research is required, particularly in relation to the capabilities of existing pipework to transport hydrogen safely.

This paper presents the Dynamic Upstream Gas Model (DYNAAMO); a new, global, bottom-up model of natural gas supply. In contrast to most “static” supply-side models, which bracket resources by average cost, DYNAAMO creates a range of dynamic outputs by simulating investment and operating decisions in the upstream gas industry triggered in response to investors’ expectations of future gas prices. Industrial data from thousands of gas fields is analysed and used to build production and expenditure profiles which drive the economics of supply at field level. Using these profiles, a novel methodology for estimating supply curves is developed which incorporates the size, age and operating environment of gas fields, and treats explicitly the fiscal, abandonment, exploration and emissions costs of production. The model is validated using the US shale gas boom in the 2000s as a historic case study. It is found that the modelled market share of supply by field environment replicates the observed trend during the period 2000–2010, and that the model price response during the same period – due to lower capacity margins and the financing of new projects – is consistent with market behaviour.

Wastewater treatment plants (WWTP) are one of the most energy intensive public utilities. The valorization of the biogas produced from the sludge in combined heat and power (CHP) systems allows important energy and emissions reduction, particularly if highly efficient engines, like solid-oxide fuel cells (SOFCs) are used. This paper proposes a two-stage stochastic optimization approach to assess the market potential for SOFCs in WWTPs in Europe. Despite the biogas availability is a challenge to guarantee continuity of operation, the results show that the WWTP is a promising market to pave the way for SOFC cost reduction and further technological learning.

The industrial sector will have to undergo major changes in order to reduce its emissions with the goal of climate change mitigation. In this context, the iron and steel subsector accounts for the highest CO 2 emissions share. This work uses a simulation model of the global energy system and quantifies the impacts of different measures for CO 2 reduction (such as fuel switching, electrification and Carbon Capture and Storage - CCS) on investment and operation decisions. The reported scenarios consider the implementation of a CO 2 price as a policy instrument to decarbonise the industrial sector. The selected case study covers steel production in the USA up to the year 2050. The results show that single measures such as fuel switching, electrification and CCS adoption alone have a limited impact on the decarbonisation of the iron and steel sector and should be rather implemented all together in an integrated approach towards climate change mitigation.

Methane and CO2 emissions from the natural gas supply chain have been shown to vary widely but there is little understanding about the distribution of emissions across supply chain routes, processes, regions and operational practises. This study defines the distribution of total methane and CO2 emissions from the natural gas supply chain, identifying the contribution from each stage and quantifying the effect of key parameters on emissions. The study uses recent high-resolution emissions measurements with estimates of parameter distributions to build a probabilistic emissions model for a variety of technological supply chain scenarios. The distribution of emissions resembles a log-log-logistic distribution for most supply chain scenarios, indicating an extremely heavy tailed skew: median estimates which represent typical facilities are modest at 18–24 g CO2 eq./MJ HHV, but mean estimates which account for the heavy tail are 22–107 g CO2 eq./MJ HHV. To place these values into context, emissions associated with natural gas combustion (e.g. for heat) are approximately 55 g CO2/MJ HHV. Thus, some supply chain scenarios are major contributors to total greenhouse gas emissions from natural gas. For methane-only emissions, median estimates are 0.8–2.2% of total methane production, with mean emissions of 1.6–5.5%. The heavy tail distribution is the signature of the disproportionately large emitting equipment known as super-emitters, which appear at all stages of the supply chain. The study analyses the impact of different technological options and identifies a set of best technological option (BTO) scenarios. This suggests that emissions-minimising technology can reduce supply chain emissions significantly, with this study estimating median emissions of 0.9% of production. However, even with the emissions-minimising technologies, evidence suggests that the influence of the super-emitters remains. Therefore, emissions-minimising technology is only part of the solution: reducing the impact of super emitters requires more effective detection and rectification, as well as pre-emptive maintenance processes.

Existing studies that consider the techno-economics of residential heating systems typically focus on their performance within present-day energy systems. However, the energy system within which these technologies operate will need to change radically if climate change mitigation is to be achieved. This article addresses this problem by modelling small-scale heating techno-economics in the context of significant electricity system decarbonisation. The current electricity market price regime based on short run marginal costs is seen to provide a very weak investment signal for electricity system investors, so an electricity price regime based on long run marginal energy costs is also considered, using a case study of the UK in 2035. The economic case for conventional boilers remains stronger in most dwelling types. The exception to this is for dwellings with high annual heat demand. Sensitivity studies demonstrate the impact of factors such as price of natural gas, carbon intensity of the central grid and thermodynamic performance. Fuel cell micro combined heat and power shows most potential under the long run electricity price regime, and heat pumps under the short run electricity price regime. This difference highlights the importance of future electricity market structure on consumer choice of heating systems in the future.

Electrical energy storage could play a pivotal role in future low-carbon electricity systems, balancing inflexible or intermittentsupply with demand. Cost projections are important for understanding this role, but data are scarce and uncertain.Here, we construct experience curves to project future prices for 11 electrical energy storage technologies. We find that,regardless of technology, capital costs are on a trajectory towards US$340 ± 60 kWh−1for installed stationary systems andUS$175 ± 25 kWh−1for battery packs once 1 TWh of capacity is installed for each technology. Bottom-up assessment ofmaterial and production costs indicates this price range is not infeasible. Cumulative investments of US$175–510 billion wouldbe needed for any technology to reach 1 TWh deployment, which could be achieved by 2027–2040 based on market growthprojections. Finally, we explore how the derived rates of future cost reduction influence when storage becomes economicallycompetitive in transport and residential applications. Thus, our experience-curve data set removes a barrier for further studyby industry, policymakers and academics.

There exists considerable uncertainty over both shale and conventional gas resource availability and extraction costs, as well as the fugitive methane emissions asso ciated with shale gas extraction and its possible role in mitigating climate change . This study uses a multi - region energy system model, TIAM (TIMES Integrated Assessment Model) , to consider the impact of a range of conventional and shale gas cost and avai lability assessments on mitigation scenarios aimed at achieving a limit to global warming of below 2 °C in 2100, with a 50% likelihood. When adding shale gas to the global energy mix, the reduction to the global energy system cost is relatively small (up to 0.4%), and the mitigation cost increases by 1 - 3% under all cost assumptions. The impact of a “dash for shale gas”, of unavailability of carbon capture and storage, of increased barriers to investment in low carbon technologies, and of higher than expected leakage rates, are also considered; and are each found to have the potential to increase the cost and reduce feasibility of meeting global temperature goals. We conclude that the extraction of shale gas is not likely to significantly reduce the effort req uired to mitigate climate change under global ly coordinated action, but could increase required mitigation effort if not handled sufficiently carefully.

Wastewater treatment is an energy and greenhouse gas intensive process. An important opportunity to reduce both of these quantities is via the use of biogas in co-generation systems. Solid-oxide fuel cells (SOFCs) are the generator types studied in this work. The feasibility of the retrofitting of a wastewater treatment facility fitted with a SOFC combined heat and power energy provision system is assessed including effects of uncertainties in biogas availability on cost and energy performance. A two-stage stochastic optimization framework is proposed to provide feedback on the energy co-generation system design. Results quantify standard deviations in the biogas rate beyond which the SOFC capacity factor might drop below 80 % and change the optimal size of the modules to install. Keywords: solid-oxide fuel cells, stochastic optimization, wastewater treatment, biogas.

Currently, natural gas in Brazil represents around 12.9% of the primary energy supply, with consistent annual growth during the last decade. However, Brazil is entering a time of uncertainty regarding future gas supply, mainly as import from Bolivia is being renegotiated. As such, diversification of gas supply sources and routes need to be considered. Energy systems and infrastructure models are essential tools in assisting energy planning decisions and policy programmes at regional and international levels. In this study, a novel combination of a simulation-based integrated assessment model (MUSE-South_Brazil) and the recently-developed Gas INfrastructure Optimisation model (GINO) is presented. The Brazilian region represented by the five southern states served by the Bolivian gas pipeline (GASBOL) has been investigated. Modelled projections suggest that regional gas demand would increase from 38.8 mcm/day in 2015 to 104.3 mcm/day by 2050, mainly driven by the increasing demand in the industry and power sectors. Therefore existing regional gas infrastructure would be insufficient to cover future demands. Three different renegotiation scenarios between Brazil and Bolivia were modelled, obtaining distinct cost-optimal infrastructure expansion pathways. Depending on the scenario, the model expects gas demand to be covered by other supply options, such as an increase in pre-salt production, LNG imports and imports from a new Argentinian pipeline.

The evaluation of capital and operational expenditures for deepwater oil and gas projects is a critical issue, given the uncertainty to estimate a project's net present value (NPV). To obtain the dependence structure between these variables, the copula methodology was adopted in order to avoid linear-dependence assumptions and inadequate estimation. This methodology was applied to Brazilian upstream deepwater projects, considering concession and production sharing contract (PSC) agreements. The findings indicate that oil price is the main impacting variable for the economic results. The best results for the concession and PSC agreements are the 500 million barrel and the 5000 million barrels cases, respectively. For 500 million barrels in concession contract the results were 93.0% of positive NPV for high oil prices (US$120/bbl) and 35% of positive NPV for low oil prices (US$68/bbl). For 5000 million barrel cases in PSC the results were 88% of positive NPV for high oil prices and 23% for low oil prices. It seems that the smaller fields take benefit compared to the bigger ones; because the windfall profit rent from concession agreements and profit oil from PSC agreements have less impact on cash flows. The remaining cases with uneconomic results impede booking oil reserves. The upstream deepwater projects are long-term developments and therefore there is a lag between oil prices and capex between the final investment decision and the first oil. On top of that, learning curves and contracts renegotiation must be taken into account for more comprehensive future analyses. Keywords: Upstream deepwater projects, Copulas methodology, Fiscal agreements, Oil reserves

The concept of ‘unburnable carbon’ emerged in 2011, and stems from the observation that if all known fossil fuel reserves are extracted and converted to CO2 (unabated), it would exceed the carbon budget and have a very significant effect on the climate. Therefore, if global warming is to be limited to the COP21 target, some of the known fossil fuel reserves should remain unburnt. Several recent reports have highlighted the scale of the challenge, drawing on scenarios of climate change mitigation and their implications for the projected consumption of fossil fuels. Carbon Capture and Storage (CCS) is a critical and available mitigation opportunity and its contribution to timely and cost-effective decarbonisation of the energy system is widely recognised. However, while some studies have considered the role of CCS in enabling access to more fossil fuels, no detailed analysis on this issue has been undertaken. This paper presents a critical review focusing on the technologies that can be applied to enable access to, or ‘unlock’, fossil fuel reserves in a way that will meet climate targets and mitigate climate change. It also quantifies the impact of CCS in unlocking unburnable carbon in the first and in the second half of the century.

Natural gas is expected to play a significant role in the support of the global market growth. The consequent transformation of the energy portfolio mix will be accompanied by dramatic changes in the natural gas supply and important questions will arise about the relative accessibility of reserves as well as the need of long distance transportation infrastructures. This paper proposes a cost minimisation model to study the evolution of the natural gas transportation system. A case study was developed where the interregional links of the natural gas supply were studied on a global scale. Results show potential underutilization of assets in the gas transmission industry.

Wastewater treatment plants (WWTP) are currently very energy and greenhouse gas intensive processes. An important opportunity to reduce both of these quantities is via the use of biogas produced within the treatment process to generate energy. This paper studies the optimal energy and economic performance of a wastewater treatment facility fitted with a solid oxide fuel cell (SOFC) based combined heat and power (CHP) plant. An optimisation framework is formulated and then applied to determine cost, energy and emissions performance of the retrofitted system when compared with conventional alternatives. Results show that present-day capital costs of SOFC technology mean that it does not quite compete with the conventional alternatives. But, it could become interesting if implemented in thermally-optimised WWTP systems. This would increase the SOFC manufacturing volumes and drive a reduction of capital and fixed operating costs.