Stefan Pfenninger
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High-resolution energy system models, as powerful tools to represent energy systems in detail and assist energy transition planning, rarely account for economic disparity, unlike broader-scale tools such as integrated assessment models. In this study, by analysing net-zero European energy system designs through the lens of national gross domestic product (GDP) and household average income, we find that disparity-unaware high-resolution energy system models can produce results that are technically feasible but largely incompatible with economic realities. The investment in household heating technology may be disproportional to the income level of lower-income countries. Explicitly acknowledging economic disparity in such models reduces the danger of them proposing solutions which burden economically disadvantaged actors. Therefore, here, we explicitly include national GDP and household income disparity in a model for net-zero European energy system designs. We find that disparity-compatible systems are possible with a 1.1% total system cost increase compared to the least-cost ones. Unlike in disparity-unaware system designs, where energy infrastructure investments often reach over 20% of national GDP for some countries, we develop disparity-compatible designs which limit investments to below 5% in each country. Our results show that less affluent European countries may need substantial household-level financing to support their heating transition and to diversify their net-zero energy technology choices.
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High-resolution energy system models, as powerful tools to represent energy systems in detail and assist energy transition planning, rarely account for economic disparity, unlike broader-scale tools such as integrated assessment models. In this study, by analysing net-zero European energy system designs through the lens of national gross domestic product (GDP) and household average income, we find that disparity-unaware high-resolution energy system models can produce results that are technically feasible but largely incompatible with economic realities. The investment in household heating technology may be disproportional to the income level of lower-income countries. Explicitly acknowledging economic disparity in such models reduces the danger of them proposing solutions which burden economically disadvantaged actors. Therefore, here, we explicitly include national GDP and household income disparity in a model for net-zero European energy system designs. We find that disparity-compatible systems are possible with a 1.1% total system cost increase compared to the least-cost ones. Unlike in disparity-unaware system designs, where energy infrastructure investments often reach over 20% of national GDP for some countries, we develop disparity-compatible designs which limit investments to below 5% in each country. Our results show that less affluent European countries may need substantial household-level financing to support their heating transition and to diversify their net-zero energy technology choices.
Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above the national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
...
Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above the national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
Journal article
(2025)
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Bob van der Zwaan, Amir Fattahi, Stefan Pfenninger, Francesco Lombardi, Panagiotis Fragkos, Maria Kannavou, Theofano Fotiou, Giannis Tolios, Will Usher, Francesco Dalla Longa, Mark Dekker, Detlef van Vuuren, Robert Pietzcker, Renato Rodrigues, Felix Schreyer, Daniel Huppmann, Johannes Emmerling
Electricity- and hydrogen-based sector coupling contributes to realizing the transition towards greenhouse gas neutrality in the European energy system. Energy system and integrated assessment models show that, to follow pathways compatible with the European policy target of net-zero greenhouse gas emissions by 2050, large amounts of renewable electricity and H2 need to be generated, mostly by scaling-up wind and solar energy production capacity. With a set of such models, under jointly adopted deep decarbonisation scenario assumptions, we here show that the ensuing direct penetration of electricity and H2 in final energy consumption may rise to average shares of around 60% and 6%, respectively, by 2050. We demonstrate that electrification proves the most cost-efficient decarbonisation route in all economic sectors, while the direct use of H2 in final energy consumption provides a relatively small, though essential, contribution to deep decarbonisation. We conclude that the variance observed across results from different models reflects the uncertainties that abound in the shape of deep decarbonisation pathways, in particular with regard to the role of H2.
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Electricity- and hydrogen-based sector coupling contributes to realizing the transition towards greenhouse gas neutrality in the European energy system. Energy system and integrated assessment models show that, to follow pathways compatible with the European policy target of net-zero greenhouse gas emissions by 2050, large amounts of renewable electricity and H2 need to be generated, mostly by scaling-up wind and solar energy production capacity. With a set of such models, under jointly adopted deep decarbonisation scenario assumptions, we here show that the ensuing direct penetration of electricity and H2 in final energy consumption may rise to average shares of around 60% and 6%, respectively, by 2050. We demonstrate that electrification proves the most cost-efficient decarbonisation route in all economic sectors, while the direct use of H2 in final energy consumption provides a relatively small, though essential, contribution to deep decarbonisation. We conclude that the variance observed across results from different models reflects the uncertainties that abound in the shape of deep decarbonisation pathways, in particular with regard to the role of H2.
The common use of cost minimisation to support energy system design decisions hides from view many economically comparable design options that stakeholders may prefer. Modelling to generate alternatives (MGA) is increasingly popular as a way to go beyond least-cost designs, providing stakeholders with diverse portfolios to appraise. However, generating all the feasible designs is not computationally viable; modellers must choose what design features to generate diversity around, despite not knowing which tradeoffs matter the most in practice. Therefore, MGA alone cannot ensure the generation of design options that match stakeholder needs. To address this shortcoming, we propose a human-in-the-loop (HITL) approach that automatically integrates stakeholder preferences into MGA. We elicit preferences by letting stakeholders interact with a tentative MGA design space. Hence, we decode those preferences to feed them back to the MGA algorithm and perform a guided search. This search produces a human-trained design space with more designs that mirror the elicited preferences. A synthetic experiment for the Portuguese energy system shows that HITL-MGA may facilitate consensus formation, promising to accelerate technically and socially feasible energy transition decisions.
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The common use of cost minimisation to support energy system design decisions hides from view many economically comparable design options that stakeholders may prefer. Modelling to generate alternatives (MGA) is increasingly popular as a way to go beyond least-cost designs, providing stakeholders with diverse portfolios to appraise. However, generating all the feasible designs is not computationally viable; modellers must choose what design features to generate diversity around, despite not knowing which tradeoffs matter the most in practice. Therefore, MGA alone cannot ensure the generation of design options that match stakeholder needs. To address this shortcoming, we propose a human-in-the-loop (HITL) approach that automatically integrates stakeholder preferences into MGA. We elicit preferences by letting stakeholders interact with a tentative MGA design space. Hence, we decode those preferences to feed them back to the MGA algorithm and perform a guided search. This search produces a human-trained design space with more designs that mirror the elicited preferences. A synthetic experiment for the Portuguese energy system shows that HITL-MGA may facilitate consensus formation, promising to accelerate technically and socially feasible energy transition decisions.
Model-based scenarios are widely used to guide energy planning and climate policy decisions. While the mathematical and physical foundations of many techno-economic models assume universal truth and objectivity, their application to explore a yet unwritten future demands a more nuanced understanding of these concepts. Although modellers' beliefs about the certainty and universality of knowledge may influence how they present their findings to decision-makers, the matter has received little empirical attention to date. Here, we address this gap and investigate modellers' epistemic beliefs concerning energy modelling and scenarios, as well as their perspectives on the communication of model outputs and expert authority. To that end, we conducted a survey with over 160 experts from a broad range of geographical regions and disciplines. Our results show significant polarisation in the participants' beliefs, revealing the two stylised profiles of a Positivist and a Postpositivist Modeller. While there are few differences in the respondents' attitudes based on educational level and background or model usage, we find significant variation particularly based on geographic location. In an effort to overcome this polarisation, we consider our study a call for diversity in modelling teams and argue for fostering the discussion of epistemic beliefs within the broader modelling community. Finally, we recommend incorporating key topics beyond technical aspects into the training and education of future modellers.
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Model-based scenarios are widely used to guide energy planning and climate policy decisions. While the mathematical and physical foundations of many techno-economic models assume universal truth and objectivity, their application to explore a yet unwritten future demands a more nuanced understanding of these concepts. Although modellers' beliefs about the certainty and universality of knowledge may influence how they present their findings to decision-makers, the matter has received little empirical attention to date. Here, we address this gap and investigate modellers' epistemic beliefs concerning energy modelling and scenarios, as well as their perspectives on the communication of model outputs and expert authority. To that end, we conducted a survey with over 160 experts from a broad range of geographical regions and disciplines. Our results show significant polarisation in the participants' beliefs, revealing the two stylised profiles of a Positivist and a Postpositivist Modeller. While there are few differences in the respondents' attitudes based on educational level and background or model usage, we find significant variation particularly based on geographic location. In an effort to overcome this polarisation, we consider our study a call for diversity in modelling teams and argue for fostering the discussion of epistemic beliefs within the broader modelling community. Finally, we recommend incorporating key topics beyond technical aspects into the training and education of future modellers.
The optimal is not always the best
Life cycle impacts of near-optimal energy systems
Journal article
(2025)
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Alexander de Tomás-Pascual, Laura Pérez-Sánchez, Miquel Sierra-Montoya, Francesco Lombardi, Stefan Pfenninger-Lee, Inês Campos, Cristina Madrid-López
Energy system optimization models (ESOMs) can be used to guide long-term energy transitions but often overlook environmental impacts and the diversity of solutions close to the cost-optimal one. Here, we combine an ESOM using Modelling to Generate Alternatives (MGA) with Life Cycle Assessment (LCA) to evaluate 260 near-optimal and technologically diverse carbon-neutral energy system designs for Portugal in 2050 across five environmental indicators: climate change, land use, water use, ecotoxicity, and materials. Using the Calliope energy modelling framework and ENBIOS for environmental assessment, we find that system designs whose cost is within 10 % of the minimum feasible cost provide up to 50 % lower environmental impacts. Our results reveal a trade-off between technological diversity and environmental performance, showing that while diversity enhances resilience, this may come with a significant increase in environmental drawbacks. Solar photovoltaic and battery technologies dominate the environmental impacts, particularly in water consumption and critical material use. This study shows that traditional cost-optimal energy system designs may not be environmentally optimal. Exploring near-optimal alternatives reveals lower-impact solutions and supports more inclusive planning for energy transitions.
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Energy system optimization models (ESOMs) can be used to guide long-term energy transitions but often overlook environmental impacts and the diversity of solutions close to the cost-optimal one. Here, we combine an ESOM using Modelling to Generate Alternatives (MGA) with Life Cycle Assessment (LCA) to evaluate 260 near-optimal and technologically diverse carbon-neutral energy system designs for Portugal in 2050 across five environmental indicators: climate change, land use, water use, ecotoxicity, and materials. Using the Calliope energy modelling framework and ENBIOS for environmental assessment, we find that system designs whose cost is within 10 % of the minimum feasible cost provide up to 50 % lower environmental impacts. Our results reveal a trade-off between technological diversity and environmental performance, showing that while diversity enhances resilience, this may come with a significant increase in environmental drawbacks. Solar photovoltaic and battery technologies dominate the environmental impacts, particularly in water consumption and critical material use. This study shows that traditional cost-optimal energy system designs may not be environmentally optimal. Exploring near-optimal alternatives reveals lower-impact solutions and supports more inclusive planning for energy transitions.
Examining pathways for a climate neutral Europe by 2050
A model comparison analysis including integrated assessment models and energy system models
Journal article
(2025)
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Efstratios Mikropoulos, Mark Roelfsema, Hsing Hsuan Chen, Iain Staffell, Gabriel Oreggioni, Dan Hdidouan, Jakob Zinck Thellufsen, Miguel Antonio Chang, Stefan Pfenninger, More Authors...
The European Union's goal of achieving climate neutrality by 2050, outlined in the European Green Deal, is supported by numerous studies providing insights into pathways and emission reduction strategies in the energy sectors. However, model comparisons of such pathways are less common due to the complex nature of climate and energy modelling. Our study brings together integrated assessment models and energy system models under a common framework to develop EU policy scenarios: a Current Trends scenario reflecting existing policies and trends and a Climate Neutrality scenario aligned with the EU's emission reduction target. Both scenarios project reduced final energy consumption by 2050, driven by increased electrification and decreased fossil fuel usage. Electricity consumption increases driven by electrification despite the improved efficiency of electrified technologies. Models align on a shift toward renewables but diverge in technology and fuel choices, reflecting various approaches to reach net-zero energy systems. Furthermore, trade-offs between energy demand and supply mitigation strategies, as well as between renewable energy, e-fuels, and CCS technologies are identified. Considering these model variations, our study highlights the importance of consistent model comparison to offer reliable recommendations to policymakers and stakeholders. We conclude that model diversity is a valuable asset when used sensibly.
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The European Union's goal of achieving climate neutrality by 2050, outlined in the European Green Deal, is supported by numerous studies providing insights into pathways and emission reduction strategies in the energy sectors. However, model comparisons of such pathways are less common due to the complex nature of climate and energy modelling. Our study brings together integrated assessment models and energy system models under a common framework to develop EU policy scenarios: a Current Trends scenario reflecting existing policies and trends and a Climate Neutrality scenario aligned with the EU's emission reduction target. Both scenarios project reduced final energy consumption by 2050, driven by increased electrification and decreased fossil fuel usage. Electricity consumption increases driven by electrification despite the improved efficiency of electrified technologies. Models align on a shift toward renewables but diverge in technology and fuel choices, reflecting various approaches to reach net-zero energy systems. Furthermore, trade-offs between energy demand and supply mitigation strategies, as well as between renewable energy, e-fuels, and CCS technologies are identified. Considering these model variations, our study highlights the importance of consistent model comparison to offer reliable recommendations to policymakers and stakeholders. We conclude that model diversity is a valuable asset when used sensibly.
Brazil is especially relevant for tackling climate change while halting biodiversity loss due to its extensive areas of ecological significance, such as the Amazon rainforest. Addressing the issue between land-use demand for renewable energy development and protection of conservation land is key to aligning climate and conservation goals. However, the country’s potential to achieve deep decarbonization through rapid renewable energy expansion while preserving conservation land remains underexplored. Here, we leverage a spatially explicit model through integrated, high-resolution sector coupling of Brazil’s energy systems and find that doubling biofuel use by 2050 demands substantial land, primarily from degraded pastures. Strategic coordination of wind, solar, and biofuels can achieve deep decarbonization, cutting CO2emissions by 40%–91% while minimizing land competition and increasing system costs by less than 4%. Protecting these lands also facilitates reforestation, potentially sequestering an additional 15.43 Gt of carbon, demonstrating a viable synergy between climate mitigation and ecological integrity.
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Brazil is especially relevant for tackling climate change while halting biodiversity loss due to its extensive areas of ecological significance, such as the Amazon rainforest. Addressing the issue between land-use demand for renewable energy development and protection of conservation land is key to aligning climate and conservation goals. However, the country’s potential to achieve deep decarbonization through rapid renewable energy expansion while preserving conservation land remains underexplored. Here, we leverage a spatially explicit model through integrated, high-resolution sector coupling of Brazil’s energy systems and find that doubling biofuel use by 2050 demands substantial land, primarily from degraded pastures. Strategic coordination of wind, solar, and biofuels can achieve deep decarbonization, cutting CO2emissions by 40%–91% while minimizing land competition and increasing system costs by less than 4%. Protecting these lands also facilitates reforestation, potentially sequestering an additional 15.43 Gt of carbon, demonstrating a viable synergy between climate mitigation and ecological integrity.
Energy system models are increasingly used to support the urgent task of planning for the energy transition. Over the last years, they have experienced constant improvements in spatial and temporal resolution, technical detail and open disclosure of code and data. Nonetheless, disagreement persists about their usefulness and real-world relevance. This is because modellers make strong but implicit assumptions on what is sensible to consider and what is not, do not sufficiently involve stakeholders in the modelling process, and pretend that the open release of large amounts of code and data automatically translates into practical understandability. Model users increasingly perceive such shortcomings. Thus, it is urgent to provide new perspectives on how to advance the energy modelling community. We propose three practical advancements. First, expanding the open-source concept to include open assumptions communicated via an explicit statement in each study. Second, coherently combining multiple models into a cosmos of modular, interoperable models instead of using one-size-fits-all individual models too large to be understood. Third, incorporating stakeholder knowledge across all phases of the modelling process in a co-creation approach. Although challenging, we argue that such advancements are essential to achieve the quality and real-world usefulness needed for energy system models to accelerate the energy transition.
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Energy system models are increasingly used to support the urgent task of planning for the energy transition. Over the last years, they have experienced constant improvements in spatial and temporal resolution, technical detail and open disclosure of code and data. Nonetheless, disagreement persists about their usefulness and real-world relevance. This is because modellers make strong but implicit assumptions on what is sensible to consider and what is not, do not sufficiently involve stakeholders in the modelling process, and pretend that the open release of large amounts of code and data automatically translates into practical understandability. Model users increasingly perceive such shortcomings. Thus, it is urgent to provide new perspectives on how to advance the energy modelling community. We propose three practical advancements. First, expanding the open-source concept to include open assumptions communicated via an explicit statement in each study. Second, coherently combining multiple models into a cosmos of modular, interoperable models instead of using one-size-fits-all individual models too large to be understood. Third, incorporating stakeholder knowledge across all phases of the modelling process in a co-creation approach. Although challenging, we argue that such advancements are essential to achieve the quality and real-world usefulness needed for energy system models to accelerate the energy transition.
Climate change impacts the power system globally. It also creates a challenge for Indonesia's energy transition, which aims for net-zero emissions by 2060. Aside from decarbonization efforts, planning for this transition adds a challenge due to the deeply uncertain nature of climate change. This refers to a condition where planners cannot agree on models, probabilities, or even which variables to prioritize. That degree of climate uncertainty has not yet been addressed in Indonesia's current power systems planning approach. Failure to address these uncertainties could bring significant vulnerabilities to Indonesia's future power system. Furthermore, only a small number of studies on power systems planning in Indonesia have addressed these climate uncertainties, and even then, only in a limited way. This paper offers a conceptual recommendation of an adaptive planning approach as one potential method to address these uncertainties. The approach is based on Dynamic Adaptive Pathways Planning (DAPP), which comes from the decision-making under deep uncertainty (DMDU) taxonomy. It supports planners in exploring a range of possible futures, considering policies and uncertainties, and enabling more robust decision-making.
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Climate change impacts the power system globally. It also creates a challenge for Indonesia's energy transition, which aims for net-zero emissions by 2060. Aside from decarbonization efforts, planning for this transition adds a challenge due to the deeply uncertain nature of climate change. This refers to a condition where planners cannot agree on models, probabilities, or even which variables to prioritize. That degree of climate uncertainty has not yet been addressed in Indonesia's current power systems planning approach. Failure to address these uncertainties could bring significant vulnerabilities to Indonesia's future power system. Furthermore, only a small number of studies on power systems planning in Indonesia have addressed these climate uncertainties, and even then, only in a limited way. This paper offers a conceptual recommendation of an adaptive planning approach as one potential method to address these uncertainties. The approach is based on Dynamic Adaptive Pathways Planning (DAPP), which comes from the decision-making under deep uncertainty (DMDU) taxonomy. It supports planners in exploring a range of possible futures, considering policies and uncertainties, and enabling more robust decision-making.
Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
...
Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
Journal article
(2024)
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Javier López Prol, Fernando deLlano-Paz, Anxo Calvo-Silvosa, Stefan Pfenninger, Iain Staffell
Wind power has considerable potential to decarbonise electricity systems due to its low cost and wide availability. However, its variability is one factor limiting uptake. We propose a simple analytical framework to optimise the distribution of wind capacity across regions to achieve a maximally firm or load-following profile. We develop a novel dataset of simulated hourly wind capacity factors (CFs) with bias correction for 111 Chinese provinces, European countries and US states spanning ten years (∼10 million observations). This flexible framework allows for near-optimal analysis, integration of demand, and consideration of additional decision criteria without additional modelling. We find that spatial integration of wind resources optimising the distribution of capacities provides significant benefits in terms of higher CF or lower residual load and lower variability at sub-, quasi- and inter-continental levels. We employ the concept of firmness as achieving a reliable and certain generation profile and show that, in the best case, the intercontinental interconnection between China, Europe and the US could restrict wind CFs to within the range of 15%–40% for 99% of the time. Smaller configurations corresponding to existing electricity markets also provide more certain and reliable generation profiles than isolated individual regions.
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Wind power has considerable potential to decarbonise electricity systems due to its low cost and wide availability. However, its variability is one factor limiting uptake. We propose a simple analytical framework to optimise the distribution of wind capacity across regions to achieve a maximally firm or load-following profile. We develop a novel dataset of simulated hourly wind capacity factors (CFs) with bias correction for 111 Chinese provinces, European countries and US states spanning ten years (∼10 million observations). This flexible framework allows for near-optimal analysis, integration of demand, and consideration of additional decision criteria without additional modelling. We find that spatial integration of wind resources optimising the distribution of capacities provides significant benefits in terms of higher CF or lower residual load and lower variability at sub-, quasi- and inter-continental levels. We employ the concept of firmness as achieving a reliable and certain generation profile and show that, in the best case, the intercontinental interconnection between China, Europe and the US could restrict wind CFs to within the range of 15%–40% for 99% of the time. Smaller configurations corresponding to existing electricity markets also provide more certain and reliable generation profiles than isolated individual regions.
Wind-solar technological, spatial and temporal complementarities in Europe
A portfolio approach
Journal article
(2024)
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Javier López Prol, Fernando de Llano Paz, Anxo Calvo-Silvosa, Stefan Pfenninger, Iain Staffell
Climate change and geopolitical risks call for the rapid transformation of electricity systems worldwide, with Europe at the forefront. Wind and solar are the lowest cost, lowest risk, and cleanest energy sources, but their variability poses integration challenges. Combining both technologies and integrating regions with dissimilar generation patterns optimizes the trade-off between maximizing energy output and minimizing its variability, which respectively give the lowest levelized cost and lowest integration cost. We apply the Markowitz mean-variance framework to a rich multi-decade dataset of wind and solar productivity to quantify the potential benefits of spatially integration of renewables across European countries at hourly, daily and monthly timescales. We find that optimal cross-country coordination of wind and solar capacities across Europe's integrated electricity system increases capacity factor by 22% while reducing hourly variability by 26%. We show limited benefits to solar integration due to consistent output profiles across Europe. Greater wind integration yields larger benefits due to the diversity of regional weather patterns. This framework shows the importance of considering renewable projects not in isolation, but as interconnected parts of a pan-continental system. Our results can guide policymakers towards strategic energy plans that reduce system-wide costs of renewable electricity, accelerating the clean energy transition.
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Climate change and geopolitical risks call for the rapid transformation of electricity systems worldwide, with Europe at the forefront. Wind and solar are the lowest cost, lowest risk, and cleanest energy sources, but their variability poses integration challenges. Combining both technologies and integrating regions with dissimilar generation patterns optimizes the trade-off between maximizing energy output and minimizing its variability, which respectively give the lowest levelized cost and lowest integration cost. We apply the Markowitz mean-variance framework to a rich multi-decade dataset of wind and solar productivity to quantify the potential benefits of spatially integration of renewables across European countries at hourly, daily and monthly timescales. We find that optimal cross-country coordination of wind and solar capacities across Europe's integrated electricity system increases capacity factor by 22% while reducing hourly variability by 26%. We show limited benefits to solar integration due to consistent output profiles across Europe. Greater wind integration yields larger benefits due to the diversity of regional weather patterns. This framework shows the importance of considering renewable projects not in isolation, but as interconnected parts of a pan-continental system. Our results can guide policymakers towards strategic energy plans that reduce system-wide costs of renewable electricity, accelerating the clean energy transition.
Open code and data are not enough
Understandability as design goal for energy system models
Energy system models do not represent natural processes but are assumption-laden representations of complex engineered systems, making validation practically impossible. Post-normal science argues that in such cases, it is important to communicate embedded values and uncertainties, rather than establishing whether a model is 'true' or 'correct'. Here, we examine how open energy modelling can achieve this aim by thinking about what 'a model' is and how it can be broken up into manageable parts. Collaboration on such building blocks—whether they are primarily code or primarily data—could become a bigger focus area for the energy modelling community. This collaboration may also include harmonisation and intercomparison of building blocks, rather than full models themselves. The aim is understandability, which will make life easier for modellers themselves (by making it easier to develop and apply problem-specific models) as well as for users far away from the modelling process (by making it easier to understand what is qualitatively happening in a model—without putting undue burden on the modellers to document every detail).
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Energy system models do not represent natural processes but are assumption-laden representations of complex engineered systems, making validation practically impossible. Post-normal science argues that in such cases, it is important to communicate embedded values and uncertainties, rather than establishing whether a model is 'true' or 'correct'. Here, we examine how open energy modelling can achieve this aim by thinking about what 'a model' is and how it can be broken up into manageable parts. Collaboration on such building blocks—whether they are primarily code or primarily data—could become a bigger focus area for the energy modelling community. This collaboration may also include harmonisation and intercomparison of building blocks, rather than full models themselves. The aim is understandability, which will make life easier for modellers themselves (by making it easier to develop and apply problem-specific models) as well as for users far away from the modelling process (by making it easier to understand what is qualitatively happening in a model—without putting undue burden on the modellers to document every detail).
Journal article
(2024)
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J.K.A. Langer, F. Lombardi, Stefan Pfenninger, Harkunti Pertiwi Rahayu, Muhammad Indra Al Irsyad, K. Blok
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$(2021)/MWh. Without island links, Java could still be supplied locally, but at 15% higher costs due to larger offshore floating PV and storage capacity requirements. Regardless of the degree of interconnection, biomass, large hydro, and geothermal remain important dispatchable generators with at least 62 GW and 23% of total generation throughout all tested scenarios. Full decarbonisation by 2040 mitigates an additional 464 MtCO2e compared to decarbonisation by 2050, but poses more challenges for renewables upscaling and fossil capacity retirement.
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Indonesia has large renewable energy resources that are not always located in regions where they are needed. Sub-sea power transmission cables, or island links, could connect Indonesia's high-demand islands, like Java, to large-resource islands. However, the role of island links in Indonesia's energy transition has been explored in a limited fashion. Considering Indonesia's current fossil fuel dependency, this is a critical knowledge gap. Here we assess the role of island links in Indonesia's full power sector decarbonisation via energy system optimisation modelling and an extensive scenario and sensitivity analysis. We find that island links could be crucial by providing access to the most cost-effective resources across the country, like onshore photovoltaics (PV) and hydropower from Kalimantan and geothermal from Sumatera. In 2050, 43 GW of inter-island transmission lines enable 410 GWp of PV providing half of total generation, coupled with 100 GW of storage, at levelised system costs of 60 US$(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.
Journal article
(2024)
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Inês Campos, Miguel Brito, Stefan Pfenninger-Lee, Luís M. Fazendeiro, Guilherme Pontes Luz, Francesco Lombardi, Aías Lima, Cristina Madrid-López
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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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.
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.
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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.
Land-free bioenergy from circular agroecology
A diverse option space and trade-offs
Bioenergy from energy crops is a source of negative emissions and carbon-neutral fuels in many 1.5/2 ∘C IPCC pathways. This may compete with other land uses. In contrast, ancillary biomass like by-products and waste is not primarily grown for energy and thus without land/food/feed competition. Here, we examine the availability and environmental impacts of ancillary bioenergy from agricultural sources under 190 circular agroecological strategies using the global food-system model SOLm for the year 2050. We find that there is a diverse option space for the future food and energy system to meet both global warming targets (1.5 ∘C) and food system sustainability (medium to highly organic) – a similar range of ancillary bioenergy global potential (55–65 EJ)from very different food systems (50%–75% organic agriculture and various levels of waste and concentrate feeding reduction). We find three trade-offs between food system sustainability and ancillary bioenergy provision. First, there is a clear trade-off between nutrient recycling and negative emissions potential. 1.4–2.6 GTCO2eq of negative emissions supplied through ancillary bioenergy with carbon capture and storage comes at the cost of nutrient deficits and resulting incompatibility with even a medium degree of organic farming. Second, reducing feed from croplands increases the ancillary bioenergy production with low shares of organic agriculture and reduces it for high shares. Third, food waste reduction reduces ancillary bioenergy provision. Hence, the sustainable transformation of the food system towards a less animal-based diet and waste reduction may conflict with a higher ancillary bioenergy provision, especially when the organic share is high as well. The policy implication of our results is that ancillary bioenergy can provide a similar range of future bioenergy as foreseen in IPCC AR6 illustrative pathways (±10% ) without additional land use or compromising food availability. However, higher ancillary bioenergy provision or additional negative emissions compete with food system sustainability; hence, we recommend policymakers consider aligning energy system planning with the compatibility of sustainable food systems simultaneously.
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Bioenergy from energy crops is a source of negative emissions and carbon-neutral fuels in many 1.5/2 ∘C IPCC pathways. This may compete with other land uses. In contrast, ancillary biomass like by-products and waste is not primarily grown for energy and thus without land/food/feed competition. Here, we examine the availability and environmental impacts of ancillary bioenergy from agricultural sources under 190 circular agroecological strategies using the global food-system model SOLm for the year 2050. We find that there is a diverse option space for the future food and energy system to meet both global warming targets (1.5 ∘C) and food system sustainability (medium to highly organic) – a similar range of ancillary bioenergy global potential (55–65 EJ)from very different food systems (50%–75% organic agriculture and various levels of waste and concentrate feeding reduction). We find three trade-offs between food system sustainability and ancillary bioenergy provision. First, there is a clear trade-off between nutrient recycling and negative emissions potential. 1.4–2.6 GTCO2eq of negative emissions supplied through ancillary bioenergy with carbon capture and storage comes at the cost of nutrient deficits and resulting incompatibility with even a medium degree of organic farming. Second, reducing feed from croplands increases the ancillary bioenergy production with low shares of organic agriculture and reduces it for high shares. Third, food waste reduction reduces ancillary bioenergy provision. Hence, the sustainable transformation of the food system towards a less animal-based diet and waste reduction may conflict with a higher ancillary bioenergy provision, especially when the organic share is high as well. The policy implication of our results is that ancillary bioenergy can provide a similar range of future bioenergy as foreseen in IPCC AR6 illustrative pathways (±10% ) without additional land use or compromising food availability. However, higher ancillary bioenergy provision or additional negative emissions compete with food system sustainability; hence, we recommend policymakers consider aligning energy system planning with the compatibility of sustainable food systems simultaneously.