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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

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.

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.

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.

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.

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.

Model-based scenarios have become the key method to explore uncertainties in the electricity supply transition of many countries. While retrospective scenario studies show that multi-organization, multi-model scenario ensembles increase the diversity of considered uncertainties, it remains unclear whether such ensembles align with the perspectives of transition stakeholders, including the wider public. This study compares a multi-organization, multi-model ensemble of 82 Swiss electricity supply scenarios for 2035 from a review of 19 studies between 2011 and 2018 with preferred scenarios from three samples of stakeholders: citizens from an online survey (N=61), informed citizens from participatory workshops (N=46), and energy experts from another online survey (N=60). The results show that most informed citizens and experts preferred an almost 100% domestic renewable electricity supply in Switzerland in 2035. On the contrary, most model-based scenarios relied significantly on fossil fuel-based generation and net electricity imports. Possible reasons for this misalignment include the lack of broad stakeholder participation in the development of such scenarios and the modeling choices such as cost-optimization models that are known to underrepresent renewable electricity. For both scenario developers and users, this study offers a word of caution that even a rich scenario ensemble could focus on alternatives that are not preferred by stakeholders and that diverse stakeholder and public perspectives can enrich scenario ensembles. For the Swiss electricity supply transition, the results indicate that a large-scale deployment of renewable electricity before 2035 is preferred by the expert and citizen samples and, therefore, such scenarios should be modeled more in the future. © 2020 Elsevier Ltd ISSN:1364-0321

Energy system models can contribute to the transition to low-carbon energy systems by helping devise pathways and calculate costs of energy policies and targets. As such, they are intended to provide support for energy planning, especially at the national level. Since energy system models are often developed outside urban contexts, their municipal policy relevance cannot be taken for granted. It is still unclear to what extent and how energy system models are applied to create municipal energy strategies and who uses them. This exploratory study aims to shed light on these aspects by examining the use of modelling tools and their outputs by municipal planners. We conducted interviews with practitioners from Danish municipalities and evaluated them using qualitative content analysis. This paper finds that the interviewees' use of software tools depends on how they perceive the functionality and complicatedness of the tools. The planners we interviewed prefer spreadsheet-based CO2 calculation or evaluation tools to energy system models, which in turn are used by heat supply companies, consultancies or universities. The practitioners we interviewed collaborate with model developers and users to further utilize model outputs. The incorporation of results from models or spreadsheet tools takes place mostly in the beginning of energy planning projects. This study suggests that models and the modelling practice can be improved with: open data, assumptions and models, collaboration across planning levels and improving links between technical modelling and practical implementation. Energy system models can contribute to the transition to low-carbon energy systems by helping devise pathways and calculate costs of energy policies and targets. As such, they are intended to provide support for energy planning, especially at the national level. Since energy system models are often developed outside urban contexts, their municipal policy relevance cannot be taken for granted. It is still unclear to what extent and how energy system models are applied to create municipal energy strategies and who uses them. This exploratory study aims to shed light on these aspects by examining the use of modelling tools and their outputs by municipal planners. We conducted interviews with practitioners from Danish municipalities and evaluated them using qualitative content analysis. This paper finds that the interviewees' use of software tools depends on how they perceive the functionality and complicatedness of the tools. The planners we interviewed prefer spreadsheet-based CO2 calculation or evaluation tools to energy system models, which in turn are used by heat supply companies, consultancies or universities. The practitioners we interviewed collaborate with model developers and users to further utilize model outputs. The incorporation of results from models or spreadsheet tools takes place mostly in the beginning of energy planning projects. This study suggests that models and the modelling practice can be improved with: open data, assumptions and models, collaboration across planning levels and improving links between technical modelling and practical implementation.

Transition towards renewable low carbon energy is a fundamental element of climate change mitigation, energy from biomass technologies are targeted within many country's decarbonisation strategies. Decision makers globally face many challenges developing strategies to drive this transition; models are increasingly used to road-test policy interventions before their implemented. A Bioenergy Literature Database was developed of 124,285 papers published 2000–2018. These document an exponential rise in research focusing on biomass and bioenergy. On average 35.4% of papers apply modelling analyses, 99.5% of these use bespoke models rather than high profile Integrated Assessment Models (IAMs) or Energy System Models – although it is these high profile models that are widely used in policy development. A review of the role of bioenergy within energy models is undertaken with a key objective of critiquing their performances in analysing bioenergy research questions. IAMs are found to be widely applied to investigate the impact of bioenergy within wider energy and environmental systems, e.g. for reducing emissions. Energy System Models focus on bioenergy processes, technologies and feedstocks, although don't capture wider environmental, economic and social themes. Specialist Bioenergy Models offer methods for bespoke analyses of all bioenergy issues, their narrow system boundaries generate targeted outputs but wider effects such as land-use change may not be captured. Caution is required when interpreting modelling outputs, particularly when used to inform policy. It's not feasible to develop all-encompassing bioenergy models covering all nuances between systems, but there is strong argument for using multiple models in parallel to build robust overall conclusions.

Fossil fuel thermal power plants constitute a large part of the Turkish electricity generation capacity. Turkish government has been developing several energy policy documents to evaluate how various renewable energy sources of the country can be utilized optimally in the generation of electricity within the next 30 years. This study considers three scenarios in the transition to renewable energy for Turkey; the business as usual (BAU), energy conservation (EC) and renewable energy (REN) scenarios. EC scenario considers the use of energy-efficient appliances and imposing a carbon tax, whereas REN scenario considers increasing the share of the renewable energy sources as much as possible in the power generation mix. These scenarios were evaluated in terms of cost and environmental impact. The LEAP (Long-range Energy Alternatives Planning Model) was used in the research. The REN scenario has been shown to be the optimal energy policy option for Turkey in terms of cost and environmental impact.

Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.

Hydrogen is a promising avenue for decarbonising energy systems and providing flexibility. In this paper, the JRC-EU-TIMES model – a bottom-up, technology-rich model of the EU28 energy system – is used to assess the role of hydrogen in a future decarbonised Europe under two climate scenarios, current policy initiative (CPI) and long-term decarbonisation (CAP). Our results indicate that hydrogen could become a viable option already in 2030 – however, a long-term CO2 cap is needed to sustain the transition. In the CAP scenario, the share of hydrogen in the final energy consumption of the transport and industry sectors reaches 5% and 6% by 2050. Low-carbon hydrogen production technologies dominate, and electrolysers provide flexibility by absorbing electricity at times of high availability of intermittent sources. Hydrogen could also play a significant role in the industrial and transport sectors, while the emergence of stationary hydrogen fuel cells for hydrogen-to-power would require significant cost improvements, over and above those projected by the experts.

Bottom-up energy system models rely on cost optimization to produce energy scenarios that inform policy analyses, debates and decisions. This paper reviews the rationale for the use of cost optimization and questions whether cost-optimal scenarios are adequate proxies of the real-world energy transition. Evidence from ex-post modeling shows that cost optimization does not approximate the real-world UK electricity system transition in 1990–2014. The deviation in cumulative total system costs from the cost-optimal scenario in 1990–2014 is equal to 9–23% under various technology, cost, demand, and discount rate assumptions. In fact, cost-optimal scenarios are shown to gloss over a large share of uncertainty that arises due to deviations from cost optimality. Exploration of large numbers of near-optimal scenarios under parametric uncertainty can give indication of the bounds or envelope of predictability of the real-world transition. Concrete suggestions are then made how to improve bottom-up energy system models to better deal with the vast uncertainty around the future energy transition. The paper closes with a reflective discussion on the tension between predictive and exploratory use of energy system models.