arXiv subjects

Jochen Linßen

Publications and source records attributed to Jochen Linßen.

17 recordsLinked to original sources

arXiv2026

Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies

Energy system models guide societally important decisions, but their credibility rests on quantitative assumptions that are difficult to source and audit. Meta-analyses can improve transparency and modeling practices, but the rapid growth of publications makes manual information extraction increasingly impractical. Consequently, databases are updated infrequently and efforts are often duplicated across research groups. Here, we demonstrate the highly accurate automated extraction of quantitative information from 76,000 energy system studies published since 2010. We compile 3.2 million structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.

cs.CL↗

arXiv2026

Stable and Renewable: Assessing the Reliability of a Fully Renewable European Energy System

The transformation of the energy system has raised concerns about the reliability of fully renewable energy systems. We address this question for a 2050 European energy system using an economically optimal adequacy assessment. Our results show that a cost-optimal, fully renewable European system can be as reliable as a fossil-based one, with an average loss of load of only 0.03% due to variability in renewable generation. Outages primarily affect industrial and service sectors, while household supply remains largely uninterrupted. Regional differences in supply security emerge, with outages concentrated in countries with a low Value of Lost Load (VoLL). We demonstrate that system reliability can be fully ensured at negligible additional cost (+0.17%) by modestly increasing hydrogen turbine (+10%) and battery capacities (+15%) beyond the cost-optimal levels. We conclude that well-designed renewable energy systems are stable, with hydrogen-based backup being a key enabler of reliability.

math.OC↗

arXiv2025

Impacts of Stratospheric Aerosol Injection on Renewable Energy Systems

Climate change is one of the 21st centurys major challenges. However, the progress in reducing greenhouse gas emissions is perceived as being too slow. Hence, more radical technologies such as stratospheric aerosol injection are entering discussions to limit climate change. This study presents a methodology for evaluating the effects of injecting 20Mt of SO$_2$ into the atmosphere annually on the global radiative balance, photovoltaic potentials, and renewable energy systems under a targeted temperature reduction of 2{\deg}C. Results show that the average annual reduction of PV potentials ranges from 0.25% to 4% up to 12% in Northern Europe during summer. The modeled renewable energy systems largely absorb these reductions resulting in minor capacity shifts with larger changes confined to a few systems. The results show that the inherent flexibility of large scale renewable energy systems helps mitigating changes in cost, but understanding this flexibility is crucial to avoid errors in design.

physics.comp-ph↗

arXiv2025

Towards Hydrogen Autarky? Evaluating Import Costs and Domestic Competitiveness in European Energy Strategies

The design of the future European energy system depends heavily on how Europe balances its domestic hydrogen production against its reliance on imports. This study reveals that neither-extreme full self-sufficiency nor complete reliance on imports-is economically optimal through 2050. Using a high-resolution energy system model accounting for interannual weather variability, we find import cost thresholds favoring domestic production decrease from 3.0 EUR/kg (2030) to 2.5 EUR/kg (2050). However, the impact of weather is significant and can shift the optimal import share by up to 60 percentage points at constant prices. Strategies with a high import share minimize the need for domestic renewable energy and electrolyzers but require significant investment in long-distance transportation, backup electricity capacity, and storage. Conversely, achieving full self-sufficiency demands massive domestic infrastructure, including up to 1,315 GW of electrolysis by 2050. These findings highlight the critical need for diversified hydrogen strategies that balance cost, resilience, and energy sovereignty. Policy must prioritize flexible infrastructure accommodating both imports and scalable domestic production to navigate evolving market and climatic conditions.

physics.soc-ph↗

arXiv2025

Automated Building Heritage Assessment Using Street-Level Imagery

Registration of heritage values in buildings is important to safeguard heritage values that can be lost in renovation and energy efficiency projects. However, registering heritage values is a cumbersome process. Novel artificial intelligence tools may improve efficiency in identifying heritage values in buildings compared to costly and time-consuming traditional inventories. In this study, OpenAI's large language model GPT was used to detect various aspects of cultural heritage value in facade images. Using GPT derived data and building register data, machine learning models were trained to classify multi-family and non-residential buildings in Stockholm, Sweden. Validation against a heritage expert-created inventory shows a macro F1-score of 0.71 using a combination of register data and features retrieved from GPT, and a score of 0.60 using only GPT-derived data. The methods presented can contribute to higher-quality datasets and support decision making.

cs.CV↗

arXiv2025

Large-Scale Linear Energy System Optimization: A Systematic Review on Parallelization Strategies via Decomposition

As renewable energy integration, sector coupling, and spatiotemporal detail increase, energy system optimization models grow in size and complexity, often pushing solvers to their performance limits. This systematic review explores parallelization strategies that can address these challenges. We first propose a classification scheme for linear energy system optimization models, covering their analytical focus, mathematical structure, and scope. We then review parallel decomposition methods, finding that while many offer performance benefits, no single approach is universally superior. The lack of standardized benchmark suites further complicates comparison. To address this, we recommend essential criteria for future benchmarks and minimum reporting standards. We also survey available software tools for parallel decomposition, including modular frameworks and algorithmic abstractions. Though centered on energy system models, our insights extend to the broader operations research field.

math.OC↗

arXiv2025

Risks of AI-driven product development and strategies for their mitigation

Humanity is progressing towards automated product development, a trend that promises faster creation of better products and thus the acceleration of technological progress. However, increasing reliance on non-human agents for this process introduces many risks. This perspective aims to initiate a discussion on these risks and appropriate mitigation strategies. To this end, we outline a set of principles for safer AI-driven product development which emphasize human oversight, accountability, and explainable design, among others. The risk assessment covers both technical risks which affect product quality and safety, and sociotechnical risks which affect society. While AI-driven product development is still in its early stages, this discussion will help balance its opportunities and risks without delaying essential progress in understanding, norm-setting, and regulation.

cs.CY↗

arXiv2025

Robust Capacity Expansion Modelling for Renewable Energy Systems

Future greenhouse gas neutral energy systems will be dominated by renewable energy technologies providing variable supply subject to uncertain weather conditions. For this setting, we propose an algorithm for capacity expansion planning: We evaluate solutions optimised on a single years' data under different input weather years, and iteratively modify solutions whenever supply gaps are detected. These modifications lead to solutions with sufficient capacities to overcome periods of cold dark lulls and seasonal demand/supply fluctuations. A computational study on a German energy system model for 40 operating years shows that preventing supply gaps, i.e. finding a robust system, increases the total annual cost by 1.6-2.9%. In comparison, non-robust systems display loss of load close to 50% of total demand during some periods. Results underline the importance of assessing the feasibility of energy system models using atypical time-series, combining dark lull and cold period effects.

math.OC↗

arXiv2025

The Striking Impact of Natural Hazard Risk on Global Green Hydrogen Cost

Due to climate change, natural hazards that affect energy infrastructure will become more frequent in the future. However, to incorporate natural hazard risk into infrastructure investment decisions, we develop an approach to translate this risk into discount rates. Thus, our newly developed discount rate approach incorporates both economic risk and natural hazard risk. To illustrate the impact of including the risk of natural hazards, we apply country-specific discount rates for hydrogen production costs. The country-specific relative difference in hydrogen generation cost ranges from a 96% surplus in the Philippines to a -63% cost reduction in Kyrgyzstan compared to a discount rate that only consists of economic risks. The inclusion of natural hazard risk changes the cost ranking of technologies as outcome of energy system models and thus policy recommendations. The derived discount rates for 254 countries worldwide are published in this publication for further use.

econ.GN↗

arXiv2025

Towards high resolution, validated and open global wind power assessments

Wind power is expected to play a crucial role in future net-zero energy systems, but wind power simulations to support deployment strategies vary drastically in their results, hindering reliable design decisions. Therefore, we present a transparent, open source, validated and evaluated, global wind power simulation tool ETHOS.RESKit.Wind with high spatial resolution and customizable designs for both onshore and offshore wind turbines. The tool provides a comprehensive validation and calibration procedure using over 16 million global measurements from metrerological masts and wind turbine sites. We achieve a global average capacity factor mean error of 0.006 and Pearson correlation of 0.865. In addition, we evaluate its performance against several aggregated and statistical sources of wind power generation. The release of ETHOS.RESKit.Wind is a step towards a fully open source and open data approach to accurate wind power modeling by incorporating the most comprehensive simulation advances in one model.

physics.soc-ph↗

arXiv2024

High-Resolution Rooftop-PV Potential Assessment for a Resilient Energy System in Ukraine

Rooftop photovoltaic (RTPV) systems are essential for building a decarbonized and, due to its decentralized structure, more resilient energy system, and are particularly important for Ukraine, where recent conflicts have damaged more than half of its electricity and heat supply capacity. Favorable solar irradiation conditions make Ukraine a strong candidate for large-scale PV deployment, but effective policy requires detailed data on spatial and temporal generation potential. This study fills the data gap by using open-source satellite building footprint data corrected with high-resolution data from eastern Germany. This approach allowed accurate estimates of rooftop area and PV capacity and generation across Ukraine, with simulations revealing a capacity potential of 238.8 GW and a generation potential of 290 TWh/a excluding north-facing. The majority of this potential is located in oblasts (provinces) across the country with large cities such as Donetsk, Dnipro or Kyiv and surroundings. These results, validated against previous studies and available as open data, confirm Ukraine's significant potential for RTPV, supporting both energy resilience and climate goals.

physics.soc-ph↗

arXiv2024

Automated and Connected Driving: State-of-the-Art and Implications for Future Scenario Analysis

Automated driving can have a huge impact on the transport system in passenger, as well as freight applications; however, market and technological development are difficult to foresee. Therefore, a systems analysis is called for to answer the question: What is the impact of automated driving on the techno-economic performance of transport systems? It is important to quantify the potential impacts not only on a local scale and for specific use cases but for entire transport systems at large. Here, we provide an overview of the current state of automated driving, including academic research in addition to industrial development. For industrial development, we find that it will take at least until 2030-2040 for automated vehicles to be widely available for passenger transport. For freight transport on the other hand, automated vehicles might already be used within the next years at least on motorways. For academic research, we find that most studies on passenger transport consider shared automated vehicles separated from other transport modes and consider specific regions only. For freight transport we find that operational strategies and usage potentials for level 4 and 5 trucks lack alignment with real-life use cases and driving profiles. Based on this, we develop an analytical framework for future research. This includes a mode choice model for passenger transport demand calculations, a total cost of ownership model for freight trucks, transport statistics for freight flows, a microscopic traffic simulation to assess the impact of automated vehicles on traffic flow, and a road network analysis.

physics.soc-ph↗

arXiv2024

Mapping Local Green Hydrogen Cost-Potentials by a Multidisciplinary Approach

For fast-tracking climate change response, green hydrogen is key for achieving greenhouse gas neutral energy systems. Especially Sub-Saharan Africa can benefit from it enabling an increased access to clean energy through utilizing its beneficial conditions for renewable energies. However, developing green hydrogen strategies for Sub-Saharan Africa requires highly detailed and consistent information ranging from technical, environmental, economic, and social dimensions, which is currently lacking in literature. Therefore, this paper provides a comprehensive novel approach embedding the required range of disciplines to analyze green hydrogen cost-potentials in Sub-Saharan Africa. This approach stretches from a dedicated land eligibility based on local preferences, a location specific renewable energy simulation, locally derived sustainable groundwater limitations under climate change, an optimization of local hydrogen energy systems, and a socio-economic indicator-based impact analysis. The capability of the approach is shown for case study regions in Sub-Saharan Africa highlighting the need for a unified, interdisciplinary approach.

econ.GN↗

arXiv2024

Global Shipyard Capacities Limiting the Ramp-Up of Global Hydrogen Transport

Decarbonizing the global energy system requires significant expansions of renewable energy technologies. Given that cost-effective renewable sources are not necessarily situated in proximity to the largest energy demand centers globally, the maritime transportation of low-carbon energy carriers, such as renewable-based hydrogen or ammonia, will be needed. However, whether existent shipyards possess the required capacity to provide the necessary global fleet has not yet been answered. Therefore, this study estimates global tanker demand based on projections for global hydrogen demand, while comparing these projections with historic shipyard production. Our findings reveal a potential bottleneck until 2033-2039 if relying on liquefied hydrogen exclusively. This bottleneck could be circumvented by increasing local hydrogen production, utilizing pipelines, or liquefied ammonia as an energy carrier for hydrogen. Furthermore, the regional concentration of shipyard locations raises concerns about diversification. Increasing demand for container vessels could substantially hinder the scale-up of maritime hydrogen transport.

econ.GN↗

arXiv2023

Green Hydrogen Cost-Potentials for Global Trade

Green hydrogen is expected to be traded globally in future greenhouse gas neutral energy systems. However, there is still a lack of temporally- and spatially-explicit cost-potentials for green hydrogen considering the full process chain, which are necessary for creating effective global strategies. Therefore, this study provides such detailed cost-potential-curves for 28 selected countries worldwide until 2050, using an optimizing energy systems approach based on open-field photovoltaics (PV) and onshore wind. The results reveal huge hydrogen potentials (>1,500 PWhLHV/a) and 79 PWhLHV/a at costs below 2.30 EUR/kg in 2050, dominated by solar-rich countries in Africa and the Middle East. Decentralized PV-based hydrogen production, even in wind-rich countries, is always preferred. Supplying sustainable water for hydrogen production is needed while having minor impact on hydrogen cost. Additional costs for imports from democratic regions are only total 7% higher. Hence, such regions could boost the geostrategic security of supply for greenhouse gas neutral energy systems.

econ.GN↗

arXiv2023

Low-carbon Lithium Extraction Makes Deep Geothermal Plants Cost-competitive in Energy Systems

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from energy systems. Our analysis indicates that if 10% of municipalities in the Upper Rhine Graben area in Germany constructed deep geothermal plants, they could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. This approach could offer significant environmental benefits and has high potential for mass application also in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.

econ.GN↗

arXiv2022

Global LCOEs of decentralized off-grid renewable energy systems

Recent global events emphasize the importance of a reliable energy supply. One way to increase energy supply security is through decentralized off-grid renewable energy systems, for which a growing number of case studies are researched. This review gives a global overview of the levelized cost of electricity (LCOE) for these autonomous energy systems, which range from 0.03 \$_{2021}/kWh to over 1.00 \$_{2021}/kWh worldwide. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from 0.54 \$_{2021}/kWh to 0.29 \$_{2021}/kWh, presumably due to cost reductions in renewable energy and storage technologies. Furthermore, we identify and discuss seven key reasons why LCOEs are frequently overestimated or underestimated in literature, and how this can be prevented in the future. Our overview can be employed to verify findings on off-grid systems, to assess where these systems might be deployed and how costs evolve.

econ.GN↗