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Technology-rich integrated assessment models (IAMs) provide high resolution of the energy-related climate impacts, when exploring prospective climate change mitigation strategies. However, energy system models (ESMs), the core of integrated assessment models, usually ignore industrial linkages other than those related to energy. Furthermore, these models focus on climate change and neglect other environmental pressures. In this manuscript we present an approach for coupling ESMs with multiregional input–output models (MRIOs) based on the Make and Use framework. The main advantage of using the Make and Use framework can be seen in improving both models simultaneously. An exemplary case study is carried out for the energy system model of MESSAGEix and the multi-regional input–output database of EXIOBASE. We further elaborate on the methodology to illustrate the usefulness but also challenges of this approach. Finally, we identify further steps to be carried out toward systematic prospective analyzes. [ABSTRACT FROM AUTHOR]

Abstract Background Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue. Results We review the mathematical structure of a typical energy system model, and discuss issues of sparsity, degeneracy and large numerical range. We introduce and test a method to automatically scale models to improve numerical range. We test this method as well as tweaks to model formulation and solver preferences, finding that adjustments can have a substantial impact on runtime. In particular, the barrier method without crossover can be very fast, but affects the structure of the resulting optimal solution. Conclusions We conclude with a range of recommendations for energy system modellers: first, on large and difficult models, manually select the barrier method or barrier+crossover method. Second, use appropriate units that minimize the model’s numerical range or apply an automatic scaling procedure like the one we introduce here to derive them automatically. Third, be wary of model formulations with cost-free technologies and dummy costs, as those can dramatically worsen the numerical properties of the model. Finally, as a last resort, know the basic solver tolerance settings for your chosen solver and adjust them if necessary.

Background: Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue. Results: We review the mathematical structure of a typical energy system model, and discuss issues of sparsity, degeneracy and large numerical range. We introduce and test a method to automatically scale models to improve numerical range. We test this method as well as tweaks to model formulation and solver preferences, finding that adjustments can have a substantial impact on runtime. In particular, the barrier method without crossover can be very fast, but affects the structure of the resulting optimal solution. Conclusions: We conclude with a range of recommendations for energy system modellers: first, on large and difficult models, manually select the barrier method or barrier+crossover method. Second, use appropriate units that minimize the model's numerical range or apply an automatic scaling procedure like the one we introduce here to derive them automatically. Third, be wary of model formulations with cost-free technologies and dummy costs, as those can dramatically worsen the numerical properties of the model. Finally, as a last resort, know the basic solver tolerance settings for your chosen solver and adjust them if necessary. [ABSTRACT FROM AUTHOR]

The EU energy and climate policy revolves around enhancing energy security and affordability, while reducing the environmental impacts of energy use. The European energy transition has been at the centre of debate following the post-pandemic surge in power prices in 2021 and the energy crisis following the 2022 Russia-Ukraine war. Understanding the extent to which electricity prices depend on fossil fuel prices (specifically natural gas) is key to guiding the future of energy policy in Europe. To this end, we quantify the role of fossil-fuelled vs. low-carbon electricity generation in setting wholesale electricity prices in each EU-27 country plus Great Britain (GB) and Norway during 2015-2021. We apply econometric analysis and use sub/hourly power system data to estimate the marginal share of each electricity generation type. The results show that fossil fuel-based power plants set electricity prices in Europe at approximately 58% of the time (natural gas 39%) while generating only 34% of electricity (natural gas 18%) a year. The energy transition has made natural gas the main electricity price setter in Europe, with gas determining electricity prices for more than 80% of the hours in 2021 in several countries such as Belgium, GB, Greece, Italy, and the Netherlands. Hence, Europe’s electricity markets are highly exposed to the geopolitical risk of gas supply and natural gas price volatility, and the economic risk of currency exchange.

While storytelling and visualization have always been recognized as invaluable techniques for imparting knowledge across generations, their importance has become even more evident in the present information age as the abundance of complex data grows exponentially. These techniques can simplify convoluted concepts and communicate them in a way to be intelligible for diverse audiences, bringing together heterogeneous stakeholders and fostering collaboration. In the field of energy and climate research, there is an increasing demand to make sophisticated models and their outcomes explainable and comprehensible for an audience of laypersons. Unfortunately, traditional tools and methods may be inefficient to provide meaning for input and output values; therefore, in this study, we employ a storytelling tool, the so-called Academic Presenter, to digest various datasets and visualize the extended BioENergy OPTimization model (BENOPTex) outcomes in different online and offline formats. The developed tool facilitates communications among collaborators with a broad spectrum of backgrounds by transforming outcomes into visually appealing stories. Although this study focuses on designing an ideal user interface for BENOPTex, the developed features and the learned lessons can be replicated for other energy system models. [ABSTRACT FROM AUTHOR]

The UK government's plans to decarbonise residential heating will mean major changes to the energy system whatever the specific technology pathway chosen, driving a range of impacts on users and suppliers. We use an energy system model (UK TIMES) to identify the potential energy system impacts of alternative pathways to low or zero carbon heating. We find that the speed of transitioning can affect the network investment requirements, the overall energy use and emissions generated, while the primary heating fuel shift will determine which sectors and networks require most investment. Crucially, we identify that retail price differences between heating fuels in the UK, particularly gas and electricity, could erode or eliminate bill savings from switching to more efficient heating systems.

Bottom-up energy system models have been used extensively to analyse future energy scenarios, addressing a wide variety of policy questions. This paper focuses on energy efficiency, a key energy, climate, and economic policy area where several examples of energy system model applications can be found in the literature. This paper analyses how different studies implement energy efficiency scenarios in energy system models and explains how the approach taken can affect the results significantly, potentially affecting policy decisions. This analysis contributes to understanding how this type of modelling framework considers energy efficiency policy issues and the extent of insight provided, or not, on different dimensions. With the aim of identifying 'best practice' in using energy system models to inform effective analysis of energy efficiency policy, the UK TIMES energy system model is used to implement five different energy efficiency scenarios for residential heating following different modelling approaches and replicating scenarios available in the literature. The study concludes that energy efficiency scenarios, implementing the same target, produce significantly different results in terms of technology mix, energy use, emissions and costs. Additionally, the outcomes show that there is no overall best energy efficiency scenario, as each impacts on different policy targets, which could come into conflict with each other. [ABSTRACT FROM AUTHOR]

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.

Industrial decarbonization models are used to evaluate technological and policy pathways to achieve net zero greenhouse gas emissions. In this paper, 81 industrial modeling papers are analyzed to identify modeling gaps and recommend future research directions. First, we identified a list of criteria for modeling capabilities used to inform policymakers. Modeling parameters were also compiled that assess feasibility of technologies for energy system modelers. Second, assessment of literature was undertaken using an evaluation template. The results of this study showed that research papers lack modeling capabilities to inform policymakers in various areas including cost, industrial, technology, and geographical coverages. Energy system modelers need to consider pertinent modeling capabilities to effectively inform policymakers. In addition, most models neither consider nor disclose modeling parameters such as cost, demand, technology options, and technology resolution. This paper recommends energy system modelers to promote transparency and accessibility of their models and input data. [ABSTRACT FROM AUTHOR]

With the European Green Deal, the European Union (EU27) aims to achieve an ambitious decarbonization of its electricity supply, while actively involving its citizens. Scenarios from energy and electricity sector models seek to inform this transition, although it is unclear to what extent these scenarios are aligned with the views of the citizens. In this study, four multi-organization, multi-model ensembles of existing electricity supply scenarios have been compiled for France, Germany, Poland, and the whole EU27 in 2035, leading to 612 scenarios in total. These scenarios were then compared with 601 preferred scenarios elicited from French, German, and Polish citizens in a survey with an interactive scenario tool. Results show that model-based and citizens’ preferred scenarios converged only on having moderate shares of onshore and offshore wind power and low shares of biomass and waste incineration. In contrast to the majority of model-based scenarios, most Polish and German citizens preferred a deeper decarbonization for their national electricity supply, while French citizens preferred a deeper denuclearization. Additionally, most citizens of all three countries used significant shares of solar photovoltaics and low shares of fossil fuels with carbon capture and storage, hence diverging from model-based scenarios. Similar patterns were found for the EU27 scenarios: many model-based scenarios included large shares of nuclear power, natural gas, and coal, while surveyed citizens preferred scenarios dominated by diverse renewable sources. European modelers should now quantify these missing scenarios so that the EU27 energy transition can be informed by modeling that is in line with citizens’ perspectives.

While storytelling and visualization have always been recognized as invaluable techniques for imparting knowledge across generations, their importance has become even more evident in the present information age as the abundance of complex data grows exponentially. These techniques can simplify convoluted concepts and communicate them in a way to be intelligible for diverse audiences, bringing together heterogeneous stakeholders and fostering collaboration. In the field of energy and climate research, there is an increasing demand to make sophisticated models and their outcomes explainable and comprehensible for an audience of laypersons. Unfortunately, traditional tools and methods may be inefficient to provide meaning for input and output values; therefore, in this study, we employ a storytelling tool, the so-called Academic Presenter, to digest various datasets and visualize the extended BioENergy OPTimization model (BENOPTex) outcomes in different online and offline formats. The developed tool facilitates communications among collaborators with a broad spectrum of backgrounds by transforming outcomes into visually appealing stories. Although this study focuses on designing an ideal user interface for BENOPTex, the developed features and the learned lessons can be replicated for other energy system models.

Summary: In order to cope with increasing complexity in energy systems due to rapid changes and uncertain future developments, the evaluation of multiple scenarios is essential for sound scientific system analyses. Hence, efficient modeling approaches and complexity reductions are urgently required. However, there is a lack of scientific analyses going beyond the scope of traditional energy system modeling. For this reason, we investigate the potential of metamodels to reduce the complexity of energy system modeling. In our explorative study, we investigate their potential and limits for applications in the fields of electricity dispatch and design optimization for heating systems. We first select a suitable metamodeling approach by conducting pre‐tests on a small scale. Based on this, we selected artificial neural networks due to their good performance compared to other approaches and the multiple possibilities of network topologies and hyperparameter settings. As for the dispatch model, we show that a high accuracy of price replication can be achieved while substantially reducing the runtimes per investigated scenario (from 2 hours on average down to less than 30 seconds). With the design optimization model, we find double‐edged results: while we also achieve a substantial reduction of runtime in this case (from ~0.8 hours to less than 30 seconds), the simultaneous forecasting of several interdependent variables proved to be problematic and the accuracy of the metamodel shows to be insufficient in many cases. Overall, we demonstrate that metamodeling is a suitable approach to complemement traditional energy system modeling rather than to replace them: the loss of traceability in (black‐box) metamodels indicates the importance of hybrid solutions that combine fundamental models with metamodels. [ABSTRACT FROM AUTHOR]

Bottom-up energy system models have been used extensively to analyse future energy scenarios, addressing a wide variety of policy questions. This paper focuses on energy efficiency, a key energy, climate, and economic policy area where several examples of energy system model applications can be found in the literature. This paper analyses how different studies implement energy efficiency scenarios in energy system models and explains how the approach taken can affect the results significantly, potentially affecting policy decisions. This analysis contributes to understanding how this type of modelling framework considers energy efficiency policy issues and the extent of insight provided, or not, on different dimensions. With the aim of identifying ‘best practice’ in using energy system models to inform effective analysis of energy efficiency policy, the UK TIMES energy system model is used to implement five different energy efficiency scenarios for residential heating following different modelling approaches and replicating scenarios available in the literature. The study concludes that energy efficiency scenarios, implementing the same target, produce significantly different results in terms of technology mix, energy use, emissions and costs. Additionally, the outcomes show that there is no overall best energy efficiency scenario, as each impacts on different policy targets, which could come into conflict with each other.

Energy scenarios should ideally account for societal feasibility and hence they could be informed by surveys on citizens’ preferences. This study surveyed consensus among citizens of France (N = 202), Germany (N = 199), and Poland (N = 200) on their national and EU electricity supply for 2035, considering different demographics, political orientations, and attitudes towards the transition and its governance. Results showed a broad consensus among surveyed citizens who positively viewed hydro, rooftop and open field solar, onshore and offshore wind power, biomass, and electricity savings, and who negatively viewed Carbon Capture and Storage (CCS) and net electricity imports. Citizens on average preferred diversified national and EU scenarios with rapid decarbonization and denuclearization, limited imports from neighboring countries, and small shares of CCS. Such scenarios should be now evaluated by modelers.

With less than 6 years left to achieve the global sustainable development goals (SDGs) by 2030, there is a growing urgency to increase the effectiveness of policy actions by targeting multiple SDGs. Mathematical modeling tools facilitate sustainability assessment in support of integrated policymaking. This study examines the interlinkages between the SDGs with commonly used and broadly applicable energy system models (ESMs) using the 248 indicators defined by the United Nations. The SDGs are classified under the environment (SDGs 6, 7, 12, 13, 14, 15), economy (SDGs 8, 9, 10, 11, 17), and society (SDGs 1, 2, 3, 4, 5, 16) domains governing the sustainability framework. The results ascertain that current ESMs are conceived to prioritize energy-related environmental indicators related to SDG7 (Affordable and Clean Energy), SDG12 (Responsible Consumption and Production) and SDG13 (Climate Action) with limited focus on non-energy indicators of the environmental domain, along with little to no coverage of SDGs-indicators allocated within the economic and social domains, respectively. The findings call for expanding the frontiers of ESMs to incorporate emerging systemic interdependencies that extend beyond the traditional "energy-environmental" nexus considering the multi-dimensional facets of sustainable development. For this purpose, a conceptual integrated framework was developed underscoring procedural protocols and technical directives to advance the ESMs-SDGs modeling paradigm through the application of methodological improvement opportunities. Theoretical and practical implications driven by policy and managerial perspectives were also discussed. In closure, this study can inform stakeholders about the gaps (sustainability issues and corresponding targets-indicators) that are imperative to address in modeling the SDGs and how to leverage existing ESMs into integrating a broader range of SDGs beyond traditional considerations. The improved outcomes are expected to facilitate integrated sustainable policy planning and formulation in pursuit of operationalizing the SDGs for the timely attainment of the 2030 agenda. [Display omitted] • Shortlisted ten commonly used and broadly applicable energy system models (ESMs). • Systematic interlinkages mapping among ESMs and SDGs using 248 indicators. • Ranked SDGs-indicators coverage within ESMs using a customized scoring system. • ESMs focus on energy-environmental indicators followed by economic and then social. • Proposed conceptual integrated framework for advancing ESMs-SDGs modeling paradigm. [ABSTRACT FROM AUTHOR]

Simulating energy systems is vital for energy planning to understand the effects of fluctuating renewable energy sources and integration of multiple energy sectors. Capacity expansion is a powerful tool for energy analysts and consists of simulating energy systems with the option of investing in new energy sources. In this paper, we apply clustering based aggregation techniques from the literature to very different real-life sector coupled energy systems. The purpose is to provide a robust comparison of methods to complement the literature, in which methods are either not compared or compared on very similar energy systems. We systematically compare the aggregation techniques with respect to solution quality and simulation time. Furthermore, we propose two new clustering approaches with promising results. We show that the aggregation techniques result in solution time savings between 75% and 90% with generally very good solution quality. To the best of our knowledge, we are the first to analyze and conclude that a weighted representation of clusters is beneficial. Furthermore, to the best of our knowledge, we are the first to recommend a clustering technique with good performance across very different energy systems: the k-means with Euclidean distance measure, clustering days and with weighted selection, where the median, maximum and minimum elements from clusters are selected. A deeper analysis of the results reveals that the aggregation techniques excel when the investment decisions correlate well with the overall behavior of the energy system. We propose future research directions to remedy when this is not the case. Finally, we believe that to further strengthen the research area, a library of benchmarks instances should be developed. [ABSTRACT FROM AUTHOR]

Electrifying transport to meet local pollution and overall net zero carbon ambitions is now a key UK policy focus, but this will have important impacts on the energy system, the economy, and the environment. Understanding the changes that the electrification of transport will bring is crucial for developing sustainable policies for net zero goals and a just transition. A literature is emerging to analyse the impact of a large-scale penetration of electric vehicles (EVs), but generally limiting focus to the implications for the electricity network. In this paper, we aim to provide insight on the wider energy system impacts of the expected EV rollout in the UK, in terms of fuel changes, energy costs, CO2 emission reduction and network investments; and how different EV charging strategies increase or mitigate the impacts of the expected large-scale penetration of EVs. Results show that non-smart and/or decentralised charging will require considerably larger investments on the network to accommodate new EV demand. Network reinforcement costs are passed to the consumer via increased electricity prices and, albeit reduced, emissions shift from the transport to the power sector. These results show the importance of considering the whole energy system and the wider economy, to avoid carbon leakage and to maximise the effectiveness of policies.

• Energy and climate papers that link models and socio-technical transitions theories or theoretical frameworks are reviewed. • Three aims are identified: Solutions to energy and climate challenges , increasing realism , and interdisciplinary learning. • Most studies demonstrate substantial benefits of interdisciplinary learning. • Few studies demonstrate concrete findings on how to meet climate and energy targets or to improve realism of models. • We suggest redirecting such integrative research to provide more practical outcomes to meet energy and climate targets. In the last decade, a new strand of energy and climate research emerged that links quantitative models and socio-technical transitions theories or frameworks. Linking the two enables capturing the co-evolution of society, technology, the economy and the environment. We systematically review this literature (N = 44) and describe the papers' trends, scope, temporal and spatial foci, and methodological strategies. The reviewed literature aspires to find solutions to the energy and climate challenges, to increase realism in models and theories, and to enable interdisciplinary learning between the two scholarly communities. The outcomes in this literature show benefits of interdisciplinary learning between modellers and transitions theorists. However, the literature rarely identified practical insights for energy and climate solutions or for improving realism in models and theories. We conclude by suggesting that integrative research should be continued, but redirected to provide more practical outcomes to meet energy and climate targets. [ABSTRACT FROM AUTHOR]

The management and integration of intermittent renewable energy sources, such as wind and solar power, require precise capacity planning due to their variable nature. This study investigated the efficacy of using hourly time resolution in energy system models, a common practice in capacity planning. Concerns have been raised about the ability of hourly data to accurately represent rapid fluctuations in energy production and demand since it inherently constantly under- or overestimates actual real-time conditions. This research compared the outputs of energy models using 60-min resolution data with those utilizing a 1-min resolution benchmark across various dimensions: stability of outputs, temporal performance, geographical performance, impact of starting time shifts in data sampling, and trend effects. Results indicate that models using 60-min resolution data maintain a high level of accuracy, with output deviations of less than 2 % from the benchmark. This finding provides strong support that the current significant number of research studies, based on 60-min resolution data, do not carry potentially biased results due to their time resolution and are suitable for capacity planning decisions, thereby aiding in policy formulation. • Investigated the accuracy of 60-min resolution in energy system models. • Compared model outputs using 60-min and 1-min resolution data. • Analyzed stability, temporal and geographical performance, and trend effects. • Found less than 2 % deviation from the benchmark in 60-min resolution models. • Assessed suitability of hourly data for renewable energy capacity planning. [ABSTRACT FROM AUTHOR]

Hydrogen is considered one of the key pillars of an effective decarbonization strategy of the energy sector; however, the potential of hydrogen as an electricity storage medium is debated. This paper investigates the role of hydrogen as an electricity storage medium in an electricity system with large hydropower resources, focusing on the Swiss electricity sector. Several techno-economic and climate scenarios are considered. Findings suggest that hydrogen storage plays no major role under most conditions, because of the large hydropower resources. More specifically, no hydrogen storage is installed in Switzerland if today's values of net-transfer capacities and low load-shedding costs are assumed. This applies even to hydrogen-favorable climate scenarios (dry years with low precipitation and dam inflows) and economic assumptions (high learning rates for hydrogen technologies). In contrast, hydrogen storage is installed when net-transfer capacities between countries are reduced below 30% of current values and load-shedding costs are above 1,000 EUR/MWh. When installed, hydrogen is deployed in a few large-scale installations near the national borders. [Display omitted] • High-fidelity models of Swiss electricity sector and hydrogen storage technologies. • No significant hydrogen storage is required in Switzerland under most scenarios. • Large hydropower resources reduce the need for hydrogen storage significantly. • Hydrogen storage becomes relevant with reduced electricity net transfer capacities. • Hydrogen storage is used for high self-sufficiency ratios to avoid load-shedding. [ABSTRACT FROM AUTHOR]

Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.

The demand for energy in Sub-Saharan African (SSA) countries is rapidly increasing. This can be attributed to the increase in human population and standard of living in the region. Owing to the increasing energy demand, there is a need for proper energy planning. Energy system models are used in exploring the energy futures of countries and regions. Use of the wrong models has resulted in unrealistic projections and consequently poor energy policies. It is against this backdrop that this study was conceived. This paper aims to identify what class of energy system models that are most suitable for energy system analysis in SSA countries. Literature was initially reviewed to understand the basic characteristics of the energy systems of SSA countries. The paper then heads on to systematically identify what types of energy system models have been used in SSA countries, using the Scopus database search platform. The result of this extensive review suggests that the bottom-up optimization and the accounting framework class of energy models are best suited for SSA countries. However, the paper further opine that the existing energy system models are not capable of characterizing the energy system of SSA countries fully. Hence, there is a need to develop new energy system models or modify the already existing ones in order to capture all the features of SSA countries. Thus, this study will be of importance to energy analysts, researchers and policy makers when selecting energy system models.

The recent development of renewable energy sources (RES) challenges energy systems and opens many new research questions. Energy System Models (ESM) are important tools to study these problems. However, including RES into ESM strongly increases the model complexity, because one needs to model the fluctuant, weather-dependent electricity production from RES with a high level of granularity. This leads to long execution times. To deal with this issue, our objective is to reduce the input time series of ESM without losing their energy-related key characteristics, such as weather-dependent fluctuations in production or peak demands. This task is challenging, because of the variety and high-dimensionality of the data. We describe a carefully engineered data-processing pipeline to reduce energy time series. We use Self-Organizing Maps, a specific kind of neural network, to select "representative days". We show that our approach outperforms the existing ones with respect to the quality of ESM results, and leads to a significant reduction of ESM execution times. [ABSTRACT FROM AUTHOR]

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. • Hierarchical clustering is applied to determine low carbon technology interdependency. • Interdependency concerns how deployment of one technology impacts another. • Technologies are identified that cluster, and which compete with other clusters. • Insights on why clusters emerge, including broader scenario framing such as policy ambition. [ABSTRACT FROM AUTHOR]

Abstract Power system operational uncertainties of load fluctuation, and unplanned outage of generating units, or lines are normally handled by reserve generation or transmission capacity. The output of conventional generators is quite controllable, and load variation is predictable within a certain confidence limit. On the other hand, output of variable renewable generators is random and uncontrollable. Large-scale integration of these resources introduces additional uncertainty to an existing system. This drastic change in generation paradigm underscores the need for additional flexible capacity to maintain reliable system operation. Conventional long-term planning studies tend to overestimate system's ability to integrate renewable energy and underestimate flexibility requirement due to inherent modeling limitations. Mathematical models that are used in these planning exercises adopt simplified spatial and temporal resolution and often neglect real system operation. Therefore, there is an urgent need to develop new planning strategies to reflect short-term renewable energy resource intermittency in long-term decision making. In this article, the implication of RE intermittency in system operation and also their impact on long-term planning is discussed. Recent planning approaches from literature which aim to address these issues, have been highlighted. A critical discussion regarding their usefulness is also presented. Highlights • Consideration of short-term renewable energy resource variation is crucial for long-term system portfolio planning. • Long-term system models have limited ability to quantify optimum flexible capacity to support large scale system variation. • Methodological enhancement in planning paradigm can be undertaken endogenously or by hybrid methods using separate models. • Hybrid modeling approach utilizes best features of individual models. • Selection of suitable approach varies with system's nature, data availability, computational requirement, and manpower. [ABSTRACT FROM AUTHOR]

To model a future power system with high shares of variable renewables, it is essential to capture the flexibility of dispatchable technologies such as hydropower. However, the representation of hydropower is often oversimplified in energy system investment models, such that the flexibility of hydropower is significantly exaggerated. This suggests the need for improved representations of hydropower that capture physical river dynamics but are computationally efficient to maintain the tractability of large models. Here, we develop a series of hydropower optimization models for a single river with various levels of techno-physical detail to evaluate options for hydropower representations in energy system investment models. All models operate hourly over a full year with perfect foresight. We explore trade-offs between accuracy and computational time involved in including features such as the river network, head-dependent power production, and discharge-dependent turbine efficiencies. We find that the level of detail significantly affects the optimal production and confirm that a simplistic hydropower representation similar to those often used in investment models significantly overestimates the flexibility of hydropower. The most detailed nonconvex model includes a full river network, head-dependency, and turbine efficiencies and is solved in just one hour on a modern desktop computer. Furthermore, we linearize this detailed model, thereby reducing computation time to one minute while featuring production dynamics substantially more similar to the full nonconvex model than a naive linear network model. These contributions pave the way for improving hydropower representations in investment models to avoid overestimating the flexibility that hydropower may provide. [Display omitted] • Aggregating hydropower plants in models overestimates the flexibility of hydropower. • We study the accuracy and computational time for 7 different ways to model hydropower. • Physical constraints on hydropower affect optimal power production significantly. • We solve a nonconvex hourly model for a full year in 1 h on a desktop computer. • We develop a novel linear model with very similar dynamics that solves in 1 min. [ABSTRACT FROM AUTHOR]

This study contributes to a better understanding of where to place different energy modelling tools and support better decision-making related to the sustainable development of energy systems. It is argued that through the connection of the energy field and the field of sustainable development, the current energy paradigm—encompassing economic, environmental and social aspects—has emerged. This paper provides an analysis of different categories of existing energy system models and their ability to provide answers to questions arising from the current energy paradigm formulated within this study. The current energy paradigm and the relevant questions were defined by conducting conceptual framework analysis. The overarching question of the current paradigm asks how different energy pathways impact on the (sustainable) development of the energy system and overall (sustainable) development globally and nationally. A review of energy system models was conducted to analyse what questions of the current energy paradigm are addressed by which models. The results show that most models address aspects of the current energy paradigm but often in a simplified way. To answer some of the questions of the current energy paradigm in more depth and to get novel insights on sustainable energy system development, it might be necessary use complementary methods in addition to traditional energy modelling methodological approaches.

This paper proposes and discusses a soft-linking procedure between a Computable General Equilibrium (CGE) model and an energy system model with the aim to improve national energy policy decision-making. Significant positive and negative experiences are communicated. Specifically, the process of soft-linking the EMEC and TIMES-Sweden models is presented, and unlike previous work we rely on the use of multiple direction-specific connection points. Moreover, the proposed soft-linking methodology is applied in the context of a climate policy scenario for Sweden. The results display a partly new description of the Swedish economy, which when soft-linking, generates lower CO 2 -emissions in the reference scenario due to a decline in industrial energy demand. These findings point at the importance of linking bottom-up and top-down models when assessing national energy and climate policies. [ABSTRACT FROM AUTHOR]

Forest-derived biofuels have been on the agenda for several decades. Despite extensive research and development efforts, forest biofuel concepts have nevertheless not yet been realized on any significant scale. The discrepancy between the expectations from the research community and the lack of momentum regarding biofuel production raises the question of if and how research results can be used to achieve such goals. Here, we report results from an interview study with the aim of evaluating how energy system models can be used to illustrate barriers and drivers for forest biofuels, with focus on Swedish conditions, using the Be Where model as case. The study is framed as an example of expertise, and problematizes how energy system models are interpreted among expected users. While the interviews revealed some general scepticism regarding models, and what kinds of questions they can answer, the belief was also expressed that increased complexity might be an advantage in terms of being able to accommodate more barriers against forest biofuels. The study illustrates the complexity of this policy area, where an energy system model can answer some, but never all, 'what if . . . ?' questions. The results reveal a need for reformation in energy system modelling in order to more explicitly make society the subject of the work, and also illustrate that the belief in expertise as a tool for consensus-building in decision-making should be questioned. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Long-range energy scenarios are commonly used to inform national energy policy decisions. Although the scenario approach aims to expand the spectrum of futures considered, in the past energy scenarios have not been diverse enough to include less expected real-world developments. We use a bottom-up energy system model EXPANSE with Modeling to Generate Alternatives (MGA) to assess the diversity of the existing ensemble of multi-organization, multi-model Swiss electricity supply scenarios. We show that both for 2035 and 2050 existing scenarios cover well the various possibilities in deployment of individual electricity generation technologies in terms of installed capacity or produced electricity. When analyzing scenarios as a whole, we find that in 2035 there is a lack of scenarios with high electricity demand and greater deployment of renewable technologies occurring simultaneously, as well as scenarios with a negligible or negative share of net electricity import. In 2050, there is also a lack of scenarios that depict high electricity demand and conservative deployment of new renewable technologies, especially solar PV. We propose six additional scenarios to be included to increase the diversity of the ensemble of Swiss electricity supply scenarios and achieve better understanding of the future possibilities and uncertainties. [ABSTRACT FROM AUTHOR]

The UK government's plans to decarbonise residential heating will mean major changes to the energy system whatever the specific technology pathway chosen, driving a range of impacts on users and suppliers. We use an energy system model (UK TIMES) to identify the potential energy system impacts of alternative pathways to low or zero carbon heating. We find that the speed of transitioning can affect the network investment requirements, the overall energy use and emissions generated, while the primary heating fuel shift will determine which sectors and networks require most investment. Crucially, we identify that retail price differences between heating fuels in the UK, particularly gas and electricity, could erode or eliminate bill savings from switching to more efficient heating systems. • We provide insight on the energy system impacts of the low carbon heat transition in the UK. • We analyse impacts in terms of fuel use, energy costs, CO2 emissions and network investments. • Higher efficiency from heat pumps could reduce residential energy use by 40% by 2050. • Gas and electricity retail price differences could erode cost savings from efficiency gains. [ABSTRACT FROM AUTHOR]