Novel wind technologies, in particular airborne wind energy (AWE) and floating offshore wind turbines, have the potential to unlock untapped wind resources and contribute to power system stability in unique ways. So far, the techno-economic potential of both technologies has only been investigated at a small scale, whereas the most significant benefits will likely play out on a system scale. Given the urgency of the energy transition, the possible contribution of these novel technologies should be addressed. Therefore, we investigate the main system-level trade-offs in integrating AWE systems and floating wind turbines into a highly renewable future energy system. To do so, we develop a modelling workflow that integrates wind resource assessment and future cost and performance estimations into a large-scale energy system model, which finds cost-optimal system designs that are operationally feasible with hourly temporal resolution across ten countries in the North Sea region. Acknowledging the uncertainty on AWE systems' future costs and performance and floating wind turbines, we examine a broad range of cost and technology development scenarios and identify which insights are consistent across different possible futures. We find that onshore AWE outperforms conventional onshore wind regarding system-wide benefits due to higher wind resource availability and distinctive hourly generation profiles, which are sometimes complementary to conventional onshore turbines. The achievable power density per ground surface area is the main limiting factor in large-scale onshore AWE deployment. Offshore AWE, in contrast, provides system benefits similar to those of offshore wind alternatives. Therefore, deployment is primarily driven by cost competitiveness. Floating wind turbines achieve higher performance than conventional wind turbines, so they can cost more and remain competitive. AWE, in particular, might be able to play a significant role in a climate-neutral European energy supply and thus warrants further study.@en

Indonesia has large renewable energy resources that are not always located in regions where they are needed. Sub-sea power transmission cables, or island links, could connect Indonesia's high-demand islands, like Java, to large-resource islands. However, the role of island links in Indonesia's energy transition has been explored in a limited fashion. Considering Indonesia's current fossil fuel dependency, this is a critical knowledge gap. Here we assess the role of island links in Indonesia's full power sector decarbonisation via energy system optimisation modelling and an extensive scenario and sensitivity analysis. We find that island links could be crucial by providing access to the most cost-effective resources across the country, like onshore photovoltaics (PV) and hydropower from Kalimantan and geothermal from Sumatera. In 2050, 43 GW of inter-island transmission lines enable 410 GWp of PV providing half of total generation, coupled with 100 GW of storage, at levelised system costs of 60 US$(2021)/MWh. Without island links, Java could still be supplied locally, but at 15% higher costs due to larger offshore floating PV and storage capacity requirements. Regardless of the degree of interconnection, biomass, large hydro, and geothermal remain important dispatchable generators with at least 62 GW and 23% of total generation throughout all tested scenarios. Full decarbonisation by 2040 mitigates an additional 464 MtCO2e compared to decarbonisation by 2050, but poses more challenges for renewables upscaling and fossil capacity retirement.@en

Energy transition policies can be translated into narratives about how energy systems should change (e.g., towards a centralised or decentralised system). These narratives tend to reflect expectations, priorities, and perceptions on feasibility and the social acceptability of different policy options, as well as long-term goals and trade-offs, all of which influence policy criteria. Taking as its case study Portugal and the implementation of European directives there, this study aims to characterise energy transition narratives (e.g. a swift transformation to renewables) and interrelated policy criteria (e.g., participation of local communities), focusing on expectations for a socially engaging and democratic energy transition. The analysis builds on the results of a Delphi survey with 10 expert stakeholders, a citizens’ survey (n=500), and a workshop with 19 participants. It identifies the most relevant criteria to stakeholders, as well as the importance of different underlying expectations, meanings, and attitudes shaping narratives about energy system futures. The findings indicate that criteria interrelated to narratives which highlight a promise of democratic energy governance may be less important for energy transition policies, and therefore undermine energy democracy goals. The conclusion highlights suggestions for policy and future research more likely to foster sociopolitical acceptance.

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Background: The transition to a climate neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESOMs have rarely been compared or linked with one another. Methods: This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables. We define harmonised regions and a subset of shared result variables that enable the comparison of scenario results across the models. Results: The results highlight how power generation and demand development are related and driven by regional and sectoral drivers. They also show that demand developments like for hydrogen can be linked with power generation potentials such as onshore wind power. Lastly, the results show that the role of nuclear power is related to the availability of wind resources. Conclusions: This comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESOM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind. The comparison shows that regional aggregations might conceal differences within regions that are potentially of interest for national policy makers thereby indicating a need for national-level analysis.

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Indonesia has large renewable energy resources that are not always located in regions where they are needed. Sub-sea power transmission cables, or island links, could connect Indonesia's high-demand islands, like Java, to large-resource islands. However, the role of island links in Indonesia's energy transition has been explored in a limited fashion. Considering Indonesia's current fossil fuel dependency, this is a critical knowledge gap. Here we assess the role of island links in Indonesia's full power sector decarbonisation via energy system optimisation modelling and an extensive scenario and sensitivity analysis. We find that island links could be crucial by providing access to the most cost-effective resources across the country, like onshore photovoltaics (PV) and hydropower from Kalimantan and geothermal from Sumatera. In 2050, 43 GW of inter-island transmission lines enable 410 GWp of PV providing half of total generation, coupled with 100 GW of storage, at levelised system costs of 60 US$(2021)/MWh. Without island links, Java could still be supplied locally, but at 15% higher costs due to larger offshore floating PV and storage capacity requirements. Regardless of the degree of interconnection, biomass, large hydro, and geothermal remain important dispatchable generators with at least 62 GW and 23% of total generation throughout all tested scenarios. Full decarbonisation by 2040 mitigates an additional 464 MtCO2e compared to decarbonisation by 2050, but poses more challenges for renewables upscaling and fossil capacity retirement.@en

Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study's central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.@en

Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.@en

A notable inclusion in the most recent mitigation assessment from the IPCC was the need for a broader societal transformation to achieve the Paris Agreement temperature goals in a fair and equitable manner. Energy system models are becoming increasingly important in politics when it comes to informing the energy transition towards climate neutrality. Even though these tools have become very powerful, their ability to map real-world behavioural, social and political developments is limited. This can lead to neglecting important aspects of fairness and inclusion as well as behavioural change measures in politics and policies, making it (almost) impossible to keep up with the goal of leaving no one and no region behind in the transition. In this talk, we argue that it is time to rethink energy system models through the lenses of a just transition. An integrated and complementary energy modelling and social science and humanities research approach is crucial to enable fair and equitable climate neutrality pathways. Political decisions would benefit from insights not only based on modelled techno-economic pathways and scenarios of the energy transition, but also on the findings of discussions and debates with the many stakeholders involved or impacted. This is particularly important as cost optimisation ignores existing injustices, there will be geographically dispersed winners and losers in the energy transition, and the transition process can offer different socio-economic benefits for broader regional transitions. In this talk, we will discuss limitation of current modelling approaches and provide policy recommendations to broaden the evidence base to include inter- and transdisciplinary research as well as the learnings from implementation projects. The policy recommendations are based on findings and experiences from stakeholder engagement in modelling from different European research projects, including SENTINEL, SONNET, SEEDS and JustWind4All.@en

The mass-scale integration of electric vehicles into the power system is a key pillar of the European energy transition agenda. Yet, the extent to which such integration would represent a burden for the power system of each member country is still an unanswered question. This is mainly due to a lack of accurate and context-specific representations of aggregate mobility and charging patterns for large electric vehicle fleets. Here, we develop and validate against empirical data an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data. We hence apply the model – which we name RAMP-mobility – to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socio-economic factors. We hence quantify the impact that fully-electric car fleets would have on the demand to be met by each country's power system: an uncontrolled deployment of electric vehicles would increase peak demand in the range 35–51%, whilst a plausible share of adoption of smart charging strategies could limit the increase to 30–41%. On the contrary, plausible technology (battery density) and infrastructure (charging power) developments would not provide substantial benefits. Efforts for electric vehicles integration should hence primarily focus on mechanisms to support smart vehicle-to-grid interaction. The approach is applicable generally beyond Europe and can provide policy makers with quantitatively reliable insights about electric vehicles impact on the power system.@en

The decarbonisation of residential heat through integration with the power system and deployment of refurbishment policies is at the core of European energy policies. Yet, heat-electricity integration may be challenged, in practice, by the large variability of heat demand across weather years. Current approaches for residential heat demand simulation fail to provide insights about the extent of such variability across many weather years and about the benefits potentially brought about by nearly zero-energy buildings. To fill this gap, this work develops an open-source space-heating demand simulation workflow that is applicable to any country's building stock. The workflow, based on a well-established lumped-parameter thermodynamic model, allows capturing sub-national weather-year variability and the mitigation effects of refurbishment. For Italy, different weather years lead to variations in heat demand up to 2 TWh/day, lasting for several days. Moreover, some weather regimes produce spatial asymmetries that may further complicate heat-electricity integration. The refurbishment of about 55% of buildings constructed before 1975 could substantially mitigate such oscillations, leading to a 31–37% reduction of yearly heat demand, primarily in colder regions. Intra-day heat demand variations, driven by user behaviour, are not substantially impacted by refurbishment, calling for the simultaneous deployment of flexible heat generating technologies.

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Disagreements persist on how to design a self-sufficient, carbon-neutral European energy system. To explore the diversity of design options, we develop a high-resolution model of the entire European energy system and produce 441 technically feasible system designs that are within 10% of the optimal economic cost. We show that a wide range of systems based on renewable energy are feasible, with no need to import energy from outside Europe. Model solutions reveal considerable flexibility in the choice and geographical distribution of new infrastructure across the continent. Balanced renewable energy supply can be achieved either with or without mechanisms such as biofuel use, curtailment, and expansion of the electricity network. Trade-offs emerge once specific preferences are imposed. Low biofuel use, for example, requires heat electrification and controlled vehicle charging. This exploration of the impact of preferences on system design options is vital to inform urgent, politically difficult decisions for eliminating fossil fuel imports and achieving European carbon neutrality.@en

Operations research is used to support large-scale capacity expansion planning in the context of the energy transition, most typically in the form of cost-minimising linear programming models. However, cost-optimality is often not the goal, or only one of many competing goals, in real-world planning. In recent work, we developed the SPORES method to generate spatially explicit near-optimal solutions. Previous near-optimal solution methods were unable to generate spatially diverse model results, while our method focuses explicitly on siting generation capacity on the sub-national scale, which is also where many competing objectives matter most, such as mitigating local landscape impacts. However, applying our method at the spatial resolution required by real-life decision-making structures means modelling hundreds of sub-national locations, leading to a trade-off with respect to computational tractability. Here we develop and empirically test several improved mathematical formulations that maximise the diversity of our near-optimal results, including formulations adapted from methods proposed in the literature. We compare and critically discuss the benefits and limitations of each, thereby providing generally applicable insights around how to best use limited computational capacity to generate the most diverse set of near-optimal alternatives without redundant computation.@en

Thanks to their modularity and their capacity to adapt to different contexts, hybrid microgrids are a promising solution to decrease greenhouse gas emissions worldwide. To properly assess their impact in different settings at country or cross-country level, microgrids must be designed for each particular situation, which leads to computationally intractable problems. To tackle this issue, a methodology is proposed to create surrogate models using machine learning techniques and a database of microgrids. The selected regression model is based on Gaussian Processes and allows to drastically decrease the computation time relative to the optimal deployment of the technology. The results indicate that the proposed methodology can accurately predict key optimization variables for the design of the microgrid system. The regression models are especially well suited to estimate the net present cost and the levelized cost of electricity (R² = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R² = 0.92 and 0.86). A least-cost path towards 100% electrification coverage for the Bolivian lowlands mid-size communities is finally computed, demonstrating the usability and computational efficiency of the proposed framework.

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For decades, electrification planning in the developing world has often focused on extending the national grid to increase electricity access. This article draws attention to the potential complementary role of decentralized alternatives – primarily micro-grids – to address universal electricity access targets. To this aim, we propose a methodology consisting of three steps to estimate the LCOE and to size micro-grids for large-scale geo-spatial electrification modelling. In the first step, stochastic load demand profiles are generated for a wide range of settlement archetypes using the open-source RAMP model. In the second step, stochastic optimization is carried by the open-source MicroGridsPy model for combinations of settlement size, load demand profiles and other important techno-economic parameters influencing the LCOE. In the third step, surrogate models are generated to automatically evaluate the LCOE using a multivariate regression of micro-grid optimization results as a function of influencing parameters defining each scenario instance. Our developments coupled to the OnSSET electrification tool reveal an important increase in the cost-competitiveness of micro-grids compared to previous analyses.@en

With pressing priorities in the development agenda, policy makers in developing countries are in the difficult situation of prioritizing policy actions. Limited government and utility budgets need cost effective solutions to bring the desired development benefits of electrification, health, education and food security among others. Energy access is a prerequisite for economic activity and for human development as interacts in synergy with other development needs. As rural electrification models usually focus on the supply of electricity solely, thermal energy needs, such as cooking and water heating remain unattended and satisfied by non-renewable energy fuels. To this aim, we explore optimal electrification solutions addressing two types of energy demands, electricity and thermal energy demands for cooking. Our model builds on a 3-step electrification methodology proposed by Peña et al. including electricity as a modern source of clean energy for cooking in rural communities. The total investments needed to build and operate the microgrids, including distribution costs, is 332 million USD. This is equivalent to 1129 USD/per inhabitant. This amount does not account however the health and environment benefits that e-cooking can bring to inhabitants in Bolivian low-lands.

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Hybrid microgrids represent a cost-effective and viable option to ensure access to energy in rural areas located far from the main grid. Nonetheless, the sizing of rural microgrids is complicated by the lack of models capable of accounting for the evolution of the energy demand over time, which is likely to occur in such contexts as a result of the modification of users' lifestyles. To tackle this issue, the present study aims at developing a novel, long-term optimisation model formulation, capable of accounting for load evolution and performing suitable investment decisions for capacity expansion along the time horizon. Multiple scenarios of load evolution are considered to evaluate the beneficial effects of this novel approach, through the coupling of the model with a tool for stochastic load profiles generation. The results show how this implementation brings lower Net Present Cost to the project and improved correspondence between actual electricity demand and microgrid sizing. Finally, a sensitivity analysis evaluates the robustness of the approach with respect to input data variability and the Loss of Load parameter.

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The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines and resulting greenhouse gas emissions reduction. Nevertheless, allowing combinations of multiple flexibility options such as hydro for the long-term, electric vehicles and flexible thermal units for the short-term provides the best solution in terms of system adequacy, greenhouse gas emissions and operational costs.@en

Designing highly renewable power systems involves a number of contested decisions, such as where to locate generation and transmission capacity. Yet, it is common to use a single result from a cost-minimizing energy system model to inform planning. This neglects many more alternative results, which might, for example, avoid problematic concentrations of technology capacity in any one region. To explore such alternatives, we develop a method to generate spatially explicit, practically optimal results (SPORES). Applying SPORES to Italy, we find that only photovoltaic and storage technologies are vital components for decarbonizing the power system by 2050; other decisions, such as locating wind power, allow flexibility of choice. Most alternative configurations are insensitive to cost and demand uncertainty, while dealing with adverse weather requires excess renewable generation and storage capacities. For policymakers, the approach can provide spatially detailed power system transformation options that enable decisions that are socially and politically acceptable. The planning of highly renewable power systems at any scale involves compromise across diverse stakeholders. We develop a method that generates a variety of spatially explicit, alternative system configurations that can be used to balance techno-economic feasibility with social and political goals. The application of our method to Italy reveals flexibility of choice for decisions like where to locate wind power and whether to invest in particular technologies. Technology substitution and complementarity is evident: solar photovoltaic and battery capacities expand together, as do wind and synthetic gas turbine capacities, all of which must notably increase to replace bioenergy's firm capacity. We also see that highly renewable systems rely on regional interconnectivity but that gas infrastructure is only useful at a fraction of current capacity. Our approach can be similarly applied to examine trade-offs in other national systems, as well as those at district and continental scales. We develop a computational method, which shows that there is a flexibility of choice to manage contested decisions arising when planning highly renewable power systems, such as where to locate wind capacity. Within this decision space, problematic technologies, such as bioenergy, are difficult and costly to replace and are not absolute must-haves. Expansion of PV is a must-have, coupled with battery when designed to cope with low-wind conditions. Carbon-neutral gas turbines can contribute to balancing but with a minor role compared with today's use.@en

Coupling heat and electricity through power-to-heat (P2H) technologies is raising increasing attention. It allows, on the one hand, to substitute traditional heating technologies with highly-efficient heat pumps (HPs), while, on the other hand, exploiting cost-effective thermal storage options (TES) such as hot water tanks, ultimately increasing system flexibility and renewables penetration. Nonetheless, an accurate assessment of the benefits that may be ensured by a country-wide diffusion of P2H technologies is hindered by computational difficulties in representing large numbers of distributed HPs and TES systems within regionalor country-scale energy models. In this work, we simulate large numbers of individual HPs and TES systems and compute the realistic aggregate electricity consumption associated with those. Various relevant regulation logics are simulated, either thermostatically-controlled or aggregator-controlled. For the latter, we show that an equivalent virtual power plant (VPP) representation ensures sufficient accuracy for use in largescale energy models (NRMSE<10%). Finally, we evaluate the flexibility potential ensured by the different P2H configurations considered, by incorporating those into an open energy system optimisation model. We show that flexibility and decarbonisation benefits are achieved in all configurations, although they increase (up to -89.5 GWh/week of primary energy savings) with the degree of 'smartness' and PV-friendliness of P2H operation logics.

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