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Demand-side response (DSR), the incentivised time-shifting of energy use by consumers away from peak times, is regarded as a potentially effective measure to balance electricity supply and demand. This will be even more important in the low-carbon energy system of the future, with a high share of non-dispatchable power, such as variable renewable energy and nuclear power. Most DSR programmes require consumers’ active engagement in shifting end-use activities. Previous studies have, however, rarely revealed socio-demographic factors influential for consumers’ willingness-to-shift specific end-use activities. This study thus aims to fill this research gap and, using a multinomial logistic model to analyse a nationwide survey, identify factors influential for DSR-related decisions. The nationwide survey for 1004 respondents was carried out to collect data about consumers’ willingness-to-shift their daily activities. We focused on the activities that constitute the major part of domestic energy consumption, i.e. cooking, dish-washing, entertainment, heating, laundry and showering. According to the results, consumers’ original timing of the end-use activities, socio-demographic factors, ownership of specific appliances and level of concern for energy-saving are influential for their willingness-to-shift activities. These findings can not only help policymakers make more targeted DSR promotion plans but also help to improve broader modelling tools to better consider consumers’ willingness-to-shift their demand.

Publisher Copyright: © 2022 The Authors The impacts of climate change such as water scarcity and air temperature increase affect the development of the electricity supply system. In countries like Iran, where cooling demand in the residential sector is high and water availability is fairly low, this can create significant challenges. In this study, an econometric approach was used to project the electricity demand for four sectors in Iran, considering the effect of changing air temperature. Also, the impact of water scarcity on the optimal expansion of the power system was investigated using TIMES, a bottom-up energy system optimization modeling platform, to build a model for the Iranian electricity system with each power plant unit modeled separately. The option of switching cooling technology was also included in the modeling. The results show that a fuel price increase alters the optimal capacity and cooling technology portfolio substantially. Renewables become more desirable under the scenarios in which the fuels and water prices, as well as the carbon taxes, are high. The emissions increase in all scenarios, although the carbon tax moderates this increase. The water withdrawal declines in the high water price scenarios. The sensitivity analysis of water price revealed the effect of this parameter on water withdrawal.

The role of different technologies in a future low carbon energy system is determined by numerous factors, many of which are highly uncertain. Their deployment may be a function of dependency on other technologies, or competition, or wider system effects. In this paper, using a UK example, we explore patterns of interdependency between technologies using a hierarchical clustering approach across multiple scenarios. We find that technologies compete in some instances, often on costs, cluster because they co-depend on each other, or emerge under all conditions, as robust options. Crucially, the broader scenario framing around carbon capture and storage (CCS) availability and climate policy stringency strongly influences these interdependencies. Keywords: Energy system models, Scenario clustering, Technology interdependency, Low carbon pathways

Swift and deep decarbonisation of electricity generation is central to enabling a timely transition to net-zero emission energy systems. While future power systems will likely be dominated by variable renewables (VRE), studies have identified a need for low-carbon dispatchable power such as nuclear. We use a cost-optimising power system model to examine the technoeconomic case for investment in new nuclear capacity in the UK's net-zero emissions energy system and consider four sensitivity dimensions: the capital cost of new nuclear, the availability of competing technologies, the expansion of interconnection and weather conditions. We conclude that new nuclear capacity is only cost-effective if ambitious cost and construction times are assumed, competing technologies are unavailable and interconnector expansion is not permitted. We find that BECCS and long-term storage could reduce electricity system costs by 5-21% and that synchronous condensers can provide cost-effective inertia in highly renewable systems with low amounts of synchronous generation. We show that a nearly 100% variable renewable system with very little fossil fuels, no new build nuclear and facilitated by long-term storage is the most cost-effective system design. This suggests that the current favourable UK Government policy towards nuclear is becoming increasingly difficult to justify., Comment: 26 pages, includes supplementary materials, submitted to Energy. Overnight capital costs now correctly converted to full costs including interest during construction, results largely unchanged

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

With an application on the UK, this paper shows that myopic planning might result in delayed strategic investments and in considerably higher costs for achieving decarbonisation targets compared to estimates done with perfect foresight optimisation energy models. It also suggests that carbon prices obtained from perfect foresight energy models might be under-estimated. The study was performed using a combination of the standard UK Times Model (UKTM), a perfect foresight, bottom-up, technology-rich cost optimisation energy model, and its myopic foresight version: My-UKTM. This also demonstrates that using perfect foresight optimisation models in tandem with their myopic equivalents can provide valuable indications for policy design. Keywords: Energy systems decarbonisation, Optimisation energy models, Perfect foresight, Myopic models, TIMES model, Technology diffusion, United Kingdom

In this study we describe a novel formulation of the so-called modelling to generate alternatives (MGA) methodology and use it to explore the near cost optimal solution space of the global energy-environment-economy model TIAM-UCL. Our implementation specifically aims to find maximally different global energy system transition pathways and assess the extent of their diversity in the near optimal region. From this we can determine the stability of the results implied by the least cost pathway which in turn allows us to both identify whether there are any consistent insights that emerge across MGA iterations while at the same time highlighting that energy systems that are very similar in cost can look very different. It is critical that the results of such an uncertainty analysis are communicated to policy makers to aid in robust decision making. To demonstrate the technique we apply it to two scenarios, a business as usual (BAU) case and a climate policy run. For the former we find significant variability in primary energy carrier consumption across the MGA iterations which then projects further into the energy system leading to, for example, large differences in the portfolio of fuels used in and emissions from the electricity sector. When imposing a global emissions constraint we find, in general, less variability than the BAU case. Consistent insights do emerge with oil use in transport being a robust finding across all MGA iterations for both scenarios and, in the mitigation case, the electricity sector is seen to reliably decarbonise before transport and industry as total system cost is permitted to increase. Finally, we compare our implementation of MGA to the so-called Hop-Skip-Jump formulation, which also seeks to obtain maximally different solutions, and find that, when applied in the same way, the former identifies more diverse transition pathways than the latter.

Energy system optimization models (ESOMs) are widely used to generate insight that informs energy and environmental policy. Using ESOMs to produce policy-relevant insight requires significant modeler judgement, yet little formal guidance exists on how to conduct analysis with ESOMs. To address this shortcoming, we draw on our collective modelling experience and conduct an extensive literature review to formalize best practice for energy system optimization modelling. We begin by articulating a set of overarching principles that can be used to guide ESOM-based analysis. To help operationalize the guiding principles, we outline and explain critical steps in the modelling process, including how to formulate research questions, set spatio-temporal boundaries, consider appropriate model features, conduct and refine the analysis, quantify uncertainty, and communicate insights. We highlight the need to develop and refine formal guidance on ESOM application, which comes at a critical time as ESOMs are being used to inform national climate targets.

International audience

Since the 1980s, there has been a shift in energy research. It has shifted from approaches that forecast or project the future to approaches which make more tentative claims and which explore several plausible scenarios. Due to multiple uncertainties in energy systems, there is an infinite amount of plausible scenarios that could be constructed and scenario developers therefore choose smaller, more tangible sets of scenarios to analyse. Yet, it is often unclear how and why this scenario choice is made and how such choices might be improved. This paper presents a retrospective analysis of twelve UK energy scenarios developed between 1978 and 2002. It investigates how specific scenarios were chosen and whether these choices captured the actual UK energy system transition. It finds that scenario choice reflected contemporary debates, leading to a focus on certain issues and limiting the insights gleaned from these exercises. The paper argues for multi-organisation, multi-method and multi-scenario approaches to the development of energy scenarios to capture the wide range of insights on offer. Rather than focus on uncertainty in model parameters, greater reflection on structural uncertainties, such as shifts in energy governance, is also required.

Long-term decarbonisation pathways to achieve ambitious low-carbon targets involve a range of uncertainties. Different energy system modelling approaches can be used to systematically evaluate the influence of the uncertainties, but this often leads to an unmanageable number of pathways. Summarising the large ensemble through a more limited number of representative pathways, to inform stakeholders, can be challenging. This study thus explores how to identify representative decarbonisation pathways using clustering algorithms, which can assist in grouping similar data points in uncategorised datasets, such as pathway ensembles. However, the suitability of clustering algorithms for pathway characterisation has not been investigated to date. Hence, k-means, hierarchical clustering, Gaussian mixture model, spectral clustering, and density-based clustering are adopted for comparisons. An illustrative pathway ensemble for the United Kingdom is applied to evaluate their performance based on cluster validity indices. Three metric transformations, including power, standardisation and sectoral standardisation, are also applied to create three additional sets of pathways for testing. The k-means algorithm is found to outperform others consistently, although hierarchical clustering might also be applicable if the distribution of pathway proximity is uneven. The results also highlight the utility of the approach in revealing distinctive trade-offs between technologies among the identified representative pathways. For instance, the electrification of heating can be replaced by district heating in the residential sector. The described, novel approach can be applied to characterise other sets of pathways, with greater technological details generated by any energy system models, to reveal insights for long-term decarbonisation.

Between 2009 and 2017 the share of wind and solar energy sources in the GB electricity generation mix increased from 2.5% to 17%. Due to the variable nature of these renewable sources, large thermal power stations designed for constant base-load operation have been required to operate more flexibly to compensate for fluctuations in renewable generation. This flexible operation results in increased thermal stress and reduced efficiency causing increased operation, maintenance and fuel costs for these assets. In this paper we present the results of what is, to the best of our knowledge, the first empirical study on the impact of renewables generation on startups, ramping and part-loading (collectively, ‘cycling’) of base-load generators. We develop regression models using half-hourly generation data from 2009 to 2017 that capture the impact of increased renewable penetration while taking into account confounding factors including seasonality and demand. We find that with 2009-levels of renewable generation, cycling in 2017 would have been less severe, with 20% fewer startups. We also present estimates for cycling under National Grid Future Energy Scenarios to 2030 with implications for investment in generation assets. Additionally, the dataset derived in this research is made available and comprises the first open-access dataset on cycling.

United Kingdom and the Nordic power market have plans to interlink directly through a sub-sea power transmission line in The North Sea. Such power market couplings have complicated implications for the interconnected energy systems and for different agents in the common power market. We analyse this case by modelling the hourly operation of the Nordic-UK power market coupling, considering the local district heating (DH) system in each country as well. According to the results, after the operation of the new interconnection between Norway and the UK (North Sea Link), the overall socio-economic benefits (social welfare) in the region will likely improve by 220–230 million euro per year, without considering the cost of the interconnector itself. The UK-Nordic market coupling enhances the flexibility of the UK power system in wind integration, irrespective of the share of wind in the Nordic countries. However, increasing wind capacity in the UK will diminish the expected economic benefits of the link. The merit order effect of wind integration in the UK will reduce the price gap between UK and Norway, and so the congestion income of the link in many hours a year when the link is congested from Norway towards the UK.

Hopes are high that removing fossil fuel subsidies could help to mitigate climate change by discouraging inefficient energy consumption and levelling the playing field for renewable energy. In September 2016, the G20 countries re-affirmed their 2009 commitment (at the G20 Leaders' Summit) to phase out fossil fuel subsidies and many national governments are using today's low oil prices as an opportunity to do so. In practical terms, this means abandoning policies that decrease the price of fossil fuels and electricity generated from fossil fuels to below normal market prices. However, whether the removal of subsidies, even if implemented worldwide, would have a large impact on climate change mitigation has not been systematically explored. Here we show that removing fossil fuel subsidies would have an unexpectedly small impact on global energy demand and carbon dioxide emissions and would not increase renewable energy use by 2030. Subsidy removal would reduce the carbon price necessary to stabilize greenhouse gas concentration at 550 parts per million by only 2-12 per cent under low oil prices. Removing subsidies in most regions would deliver smaller emission reductions than the Paris Agreement (2015) climate pledges and in some regions global subsidy removal may actually lead to an increase in emissions, owing to either coal replacing subsidized oil and natural gas or natural-gas use shifting from subsidizing, energy-exporting regions to non-subsidizing, importing regions. Our results show that subsidy removal would result in the largest CO 2 emission reductions in high-income oil- and gas-exporting regions, where the reductions would exceed the climate pledges of these regions and where subsidy removal would affect fewer people living below the poverty line than in lower-income regions.

In this article we analyze how passenger car transportation in Europe may change this century under permanent high oil prices and stringent climate control policy. We focus on electricity and hydrogen as principal candidate energy carriers, because these two options are increasingly believed to become the long-term competitors in the transport sector. We complement a concise stylistic analysis with an in-depth investigation performed with the energy system optimization model TIAM-ECN, which we ran only for the European regions for this study. This bottom-up model, belonging to the TIMES family, has been adapted for the purpose of researching – amongst others – the transport sector. We particularly inspect the use of passenger cars and find that, if oil prices amount to 100–150 $/bl during the remainder of the century, the transport sector could be little affected in the sense that it may continue to rely predominantly on (liquid or gaseous) fossil fuels: our model suggests that it could be optimal to start replacing gasoline and diesel by natural gas around the middle of the century if sufficient oil and gas reserves are available within this price range. If the European Commission achieves implementing its ambitious carbon mitigation plan, however, a massive restructuring of the transport sector away from fossil fuels could take place, which in three decades would transform it to broadly rely on hydrogen as main energy carrier according to our model runs. Under a broad set of sensitivity scenarios with varying assumptions regarding our most important modeling parameters, we find that if battery costs are reduced by at least 60% in comparison to our reference cost decline path, the passenger car sector could predominantly run on electricity from around 2050.

There is clear consensus on the value of diversity as applied to energy systems, with the concept being a central reference for governments, industry and civil society organizations. Given its importance in policy debates, we have sought to explore the specification and measurement of diversity. We show that although conceptualisation of diversity has developed over recent years, along with increasingly elegant mathematical representations, the concept is, at core, subjective and irreducibly context specific. Subjectivity derives from determination of boundaries and the placement of objects into categories, the acts that make assessment of diversity possible. We illustrate this point with an empirical analysis of the diversity of renewable energy generation in the UK over the past century. By applying a range of different indices and classifications to this dataset, we demonstrate that the ‘diversity story’ told is different in each case. As such we argue that the analysis of diversity must be produced and consumed critically. Attempts to expand, manage, measure or comment upon the diversity of a system, be it an ecosystem, an organization, an economy, or an energy portfolio, demand rigor, reflexivity and, most importantly, transparency.

This article investigates possible evolution pathways for the transport sector during the 21st century, globally and in Europe, under a climate change control scenario. We attempt to shed light on the question how the transport sector should best be decarbonized. We perform our study with the global bottom-up energy systems model TIAM-ECN, a version of the TIAM model that is broadly used for the purpose of developing energy technology and climate policy scenarios, which we adapted for analyzing in particular the transport sector. Given the global aggregated perspective of TIAM-ECN, that in its current version yields at every point in time a single CO2 price for different forms of energy use across geographic regions and economic sectors, it generates a decarbonization process that for the transport sector occurs later in time than for the power sector. This merely reflects that emission reductions are generally cheaper for electricity production than for transportation, and that it is thus cost-minimizing to spend limited financial resources available for CO2 emissions abatement in the power sector first. In our scenarios the use of hydrogen in internal combustion engines and fuel cells, rather than electricity as energy carrier and batteries to store it, gradually becomes the dominant transport technology. This outcome is in agreement with some recent publications but is at loggerheads with the current popularity of the electric car. Based on sensitivity analysis we conclude that even if the establishment of a hydrogen infrastructure proves about an order of magnitude more costly than modeled in our base case, electricity. based transportation only broadly emerges if simultaneously also the costs of electric cars go down by at least 40% with respect to our reference costs. One of the explanations for why the electric car is today, by e.g. entrepreneurs, often considered the supposed winner amongst multiple future transportation options is that the decision horizon of many analysts is no more than a few decades, instead of a full century. Electric cars fit better the current infrastructure than hydrogen fueled vehicles, so that from a short time perspective (covering the next decade or two) investments are not optimally spent by establishing an extensive hydrogen distribution network. Hence the path-dependency created by the present existence of a vast power transmission and distribution network can make electricity the most efficient choice for transportation, but only if the time frame considered is short. Electric transportation generally proves the more expensive alternative in our long-term perspective, except when electric car costs are assumed to drop substantially.

International audience Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation1. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm2 allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities.

This paper examines how changes in an international climate regime would affect the European decarbonization strategy and costs through the mechanisms of trade, technology, and innovation. We present the results from the Energy Modeling Forum (EMF) model comparison study on European climate policy to 2050. Moving from a no-policy scenario to an existing-policies case reduces all energy imports, on average. Introducing a more stringent climate policy target for the EU only leads to slightly greater global emission reductions. Consumers and producers in Europe bear most of the additional burden and inevitably face some economic losses. More ambitious mitigation action outside Europe, especially when paired with a well-operating global carbon market, could reduce the burden for Europe significantly. Because of global learning, the costs of wind and especially solar-PV in Europe would decline below the levels observed in the existing-policy case and increased R&D spending outside the EU would leverage EU R&D investments as well.

The Energy Modeling Forum 28 (EMF28) performed a large-scale model comparison exercise to illustrate different technology pathways for cutting European greenhouse gas emissions by 80% by 2050. Focusing on selected countries (France, Germany, Italy, Sweden and UK), this paper first analyses climate and energy policy objectives and debates in the respective countries. It then compares EMF28 model results to the short-term projections of the National Renewable Energy Action Plans (NREAPs) and the long-term transformation pathway given in the European Commission’s “Energy Roadmap 2050”. It concludes that there is sufficient agreement with the NREAPs and national policies to accept the model results as conceivable scenarios. The scenarios suggest that in the future a variety of different national energy mixes will continue to reflect the different resource bases and preferences of individual Member States. In order to ensure a cost-efficient transformation, it is important to improve coordination between Member State policies and those at EU level.

http://www.worldscientific.com/doi/pdfplus/10.1142/S2010007813400046; International audience; Energy efficiency and decarbonization are important elements of climate change mitigation. We draw on European mitigation scenarios from the EMF28 modeling exercise to decompose economy-wide and sectoral emissions into their main components. We utilize the Logarithmic Mean Divisia Index (LMDI) to gain insights into five effects : affluence, energy intensity, carbon intensity, conversion efficiency, and structural change. Economy-wide analysis suggests that energy efficiency improvements (including end-use efficiency of production and structural change of the economy) determine emission reductions short to medium term while decarbonization becomes more important in the long term. Sectoral analysis suggests that electricity generation holds the largest potential for decarbonization. Mitigation in the transport and energy-intensive sectors is limited by technology availability, forcing output and energy inputs to decline to meet the given mitigation pathways. We conclude that energy efficiency improvements could bridge the time until carbon-free technologies mature, while their quick development remains essential.

No abstract received.

The Energy Modeling Forum 28 (EMF28) study systematically explores the energy system transition required to meet the European goal of reducing greenhouse gas (GHG) emissions by 80% by 2050. The 80% scenario is compared to a reference case that aims to achieve a 40% GHG reduction target. The paper investigates mitigation strategies beyond 2020 and the interplay between different decarbonization options. The models present different technology pathways for the decarbonization of Europe, but a common finding across the scenarios and models is the prominent role of energy efficiency and renewable energy sources. In particular, wind power and bioenergy increase considerably beyond current deployment levels. Up to 2030, the transformation strategies are similar across all models and for both levels of emission reduction. However, mitigation becomes more challenging after 2040. With some exceptions, our analysis agrees with the main findings of the “Energy Roadmap 2050” presented by the European Commission.

Energy and climate policy-making requires strong quantitative scientific evidence to devise robust and consistent long-term decarbonisation strategies. Energy system modelling can provide crucial insights into the inherent uncertainty in such strategies, which needs to be understood when designing appropriate policy measures.This study contributes to the growing research area of uncertainty analysis in energy system models. We combine consistent and realistic narratives on several technology dimensions with a global sensitivity analysis in a national, bottom-up, optimizing energy system model. This produces structured insights into the impact of low-carbon technology and resource availability on the long-term development of the UK energy system under ambitious decarbonisation pathways. We explore a variety of result metrics to present policy-relevant results in a useful and concise manner. The results provide valuable information on the variability of fuel and technology use across the uncertainty space (e.g. a strong variation in natural gas demand). We demonstrate the complementarities and substitutability of technologies (e.g. the dependency of hydrogen technologies on the availability of CCS). We highlight critical low-carbon options and hedging strategies (e.g. the early decarbonisation of the electricity sector or the stronger use of renewable sources as a hedging against failure in other technologies) and demonstrate timing and path dependencies (e.g. the importance of early decarbonisation action in the presence of multiple technology uncertainty). The results also show how the availability of a given technology can have wider impacts elsewhere in the energy system, thus complicating the management of a long-term energy transition. Keywords: Energy systems analysis, Sensitivity analysis, Uncertainty, Emission reduction, Low-carbon technologies