arXiv subjects

Felix Müsgens

Publications and source records attributed to Felix Müsgens.

13 recordsLinked to original sources

arXiv2026

Direct Air Capture in Europe's 2050 Energy System: Integration, Storage and Cost Drivers

Direct Air Carbon Capture and Storage (DACCS) can mitigate hard-to-abate emissions, e.g. from transport or industry. However, there is a wide variety of cost estimates for DACCS, driven, to a significant extent, by differences in electricity cost. At the same time, there is a notable gap in research that integrates direct air capturing systems into long-term energy system models. We separate direct air capturing, carbon transport, and carbon storage and integrate them into a European capacity expansion model for a fully decarbonised electricity system in 2050. We explore how two dimensions affect the total system costs of DACCS. The first dimension is the availability of CO2 storage locations: In one analysis, storage locations are restricted to offshore storage locations in the North Sea only, i.e. depleted natural gas fields. The alternative analysis comprises suitable storage locations distributed across Europe, including onshore. We find that limiting CO2 storage to North Sea sites increases overall capture costs by approximately 10 %. The second dimension is whether DACCS is analysed as stand-alone or integrated into the electricity system. We differentiate between three alternatives: fully isolated, fully integrated, and retrospectively added to an existing system. We find that neglecting system integration - i.e. treating direct air capture system as a stand-alone technology - increases capture costs by up to 30 %.

physics.soc-ph↗

arXiv2026

Planning resilient hydrogen supply chains under disruption risk

Despite growing concerns over energy security, infrastructure planning and modelling for emerging green fuel supply chains often neglect risks from supply disruptions. Using a stochastic optimisation model of EU hydrogen imports, we show that 'naive' infrastructure planning results in welfare losses of 12 % (24 billion EUR) compared to risk-aware planning that anticipates supply disruptions. Despite requiring higher upfront investments, anticipatory planning achieves welfare levels close to those of an idealised system without disruptions, but entails a markedly different infrastructure configuration. Two complementary resilience strategies emerge: diversification across import corridors and strategic over-investment. This leads to increased intra-European transport capacity, a broader set of import pipelines, and investments in costly shipping terminals for hydrogen carriers. Our results show that incorporating supply risk considerations into infrastructure planning helps prevent the structural vulnerabilities seen in fossil fuel systems when designing future hydrogen supply chains.

econ.GN↗

arXiv2026

The Competitiveness of Renewables: An Analysis of Magnitude, Geography, and Drivers

While renewable energy sources are the fastest-growing electricity generation technology globally, their competitiveness is still the subject of controversy. This paper presents an electricity system model for investment and dispatch to determine the cost-optimal shares of renewable energy sources. We compute and analyse renewable generation shares in market equilibrium for Germany and Texas, using annual data for 2015 to 2024, and in five-year intervals for 2030 to 2050. Furthermore, we identify the key drivers of the renewable competitiveness and quantify their contribution through parameter variations. Our results show that renewable generation achieves considerable market shares even without subsidies. In Germany, the increase in renewable generation is primarily driven by CO2 pricing, complemented by declining investment costs for renewable technologies. In Texas, solar PV is part of the cost-optimal system, even in the absence of CO2 pricing and despite low natural gas prices.

physics.soc-ph↗

arXiv2026

Adaptive Robust Optimization for European Electricity System Planning Considering Regional Dunkelflaute Events

The expansion of wind and solar power is driving the European energy system transformation, thereby also driving our reliance on this weather-dependent resources. Integrating renewable scarcity events into long-term planning has therefore become essential. This study demonstrates how worst-case regional renewable scarcity events - such as the Dunkelflaute, prolonged periods of low wind and solar availability - can be incorporated endogenously into the planning of a weather-robust, interconnected energy system. We develop a capacity expansion model for a fully decarbonized European electricity system using an adaptive robust optimization framework which incorporates multiple extreme weather realizations within a single optimization run. Results show that system costs rise nonlinearly with the geographic extent of these events: a single worst-case regional disruption increases costs by 9%, but broader disruptions across multiple regions lead to much sharper increases, up to 51%. As Dunkelflaute conditions extend across most of Europe, additional cost impacts level off, with a maximum increase of 71%. The optimal technology mix evolves with the severity of weather stress: while renewables, batteries, and interregional transmission are sufficient to manage localized events, large-scale disruptions require long-term hydrogen storage and load shedding to maintain system resilience. Central European regions, especially Germany and France, emerge as systemic bottlenecks, while peripheral regions bear the cost of compensatory overbuilding. These findings underscore the need for a coordinated European policy strategy that goes beyond national planning to support cross-border infrastructure investment, scale up flexible technologies such as long-duration storage, and promote a geographically balanced deployment of renewables to mitigate systemic risks associated with Dunkelflaute events.

econ.GN↗

arXiv2025

Green Ammonia: A Techno-Economic Supply Chain Optimization

Green ammonia is emerging as a strategic intermediary within green energy supply chains, serving effectively as both an industrial commodity and hydrogen carrier. This study provides a techno-economic analysis of green ammonia supply chains, comparing cost-effective pathways from global production to European consumers, and evaluates ammonia alongside alternative hydrogen carriers. Gaseous hydrogen consistently remains the most economical import option for Europe, though ammonia holds a narrowing cost advantage over liquid hydrogen (from 16 % in 2030 to 10 % by 2040). Competitive ammonia suppliers, notably Morocco, the United States, and the United Arab Emirates, benefit from low renewable energy costs, with significant price reductions expected by 2040, driven by falling costs for electricity, electrolysers, and conversion technologies. Optimal transport modes vary by consumer demand and distance: trucks are ideal for low demands at all distances, rail for medium ranges, and pipelines for high-demand scenarios. By 2040, ammonia will primarily serve direct-use applications, as hydrogen consumers increasingly shift to direct hydrogen supplies. Policymakers should prioritize pipeline infrastructure for hydrogen distribution, cautiously invest in ammonia's short- to medium-term infrastructure advantages, and limit long-term reliance on ammonia as a hydrogen carrier to mitigate stranded asset risks.

econ.GN↗

arXiv2024

Bridging an energy system model with an ensemble deep-learning approach for electricity price forecasting

This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper also benchmarks the results against several econometric alternatives. Lastly, we demonstrate the economic value of improved price estimators maximising the revenue from an electric storage resource. The results demonstrate that our integrated model improves overall forecasting accuracy by 18 %, compared to available literature benchmarks. Furthermore, our robustness checks reveal that a) the Ensemble Deep Neural Network model performs best in our dataset and b) adding output from the techno-economic energy systems model as econometric model input improves the performance of all econometric models. The empirical relevance of the forecast improvement is confirmed by the results of the exemplary storage optimisation, in which the integration of the techno-economic energy system model leads to a revenue increase of up to 10 %.

econ.GN↗

arXiv2023

A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling

The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently.

econ.EM↗

arXiv2023

A Meta-Analysis of Solar Forecasting Based on Skill Score

We conduct the first comprehensive meta-analysis of deterministic solar forecasting based on skill score, screening 1,447 papers from Google Scholar and reviewing the full texts of 320 papers for data extraction. A database of 4,687 points was built and analyzed with multivariate adaptive regression spline modelling, partial dependence plots, and linear regression. The marginal impacts on skill score of ten factors were quantified. The analysis shows the non-linearity and complex interaction between variables in the database. Forecast horizon has a central impact and dominates other factors' impacts. Therefore, the analysis of solar forecasts should be done separately for each horizon. Climate zone variables have statistically significant correlation with skill score. Regarding inputs, historical data and spatial temporal information are highly helpful. For intra-day, sky and satellite images show the most importance. For day-ahead, numerical weather predictions and locally measured meteorological data are very efficient. All forecast models were compared. Ensemble-hybrid models achieve the most accurate forecasts for all horizons. Hybrid models show superiority for intra-hour while image-based methods are the most efficient for intra-day forecasts. More training data can enhance skill score. However, over-fitting is observed when there is too much training data (longer than 2000 days). There has been a substantial improvement in solar forecast accuracy, especially in recent years. More improvement is observed for intra-hour and intra-day than day-ahead forecasts. By controlling for the key differences between forecasts, including location variables, our findings can be applied globally.

stat.AP↗

arXiv2023

Enhancing Energy System Models Using Better Load Forecasts

Energy system models require a large amount of technical and economic data, the quality of which significantly influences the reliability of the results. Some of the variables on the important data source ENTSO-E transparency platform, such as transmission system operators' day-ahead load forecasts, are known to be biased. These biases and high errors affect the quality of energy system models. We propose a simple time series model that does not require any input variables other than the load forecast history to significantly improve the transmission system operators' load forecast data on the ENTSO-E transparency platform in real-time, i.e., we successively improve each incoming data point. We further present an energy system model developed specifically for the short-term day-ahead market. We show that the improved load data as inputs reduce pricing errors of the model, with strong reductions particularly in times when prices are high and the market is tight.

econ.GN↗

arXiv2021

What drives the accuracy of PV output forecasts?

Due to the stochastic nature of photovoltaic (PV) power generation, there is high demand for forecasting PV output to better integrate PV generation into power grids. Systematic knowledge regarding the factors influencing forecast accuracy is crucially important, but still mostly unknown. In this paper, we review 180 papers on PV forecasts and extract a database of forecast errors for statistical analysis. We show that among the forecast models, hybrid models consistently outperform the others and will most likely be the future of PV output forecasting. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizon and out-of-sample test set have negative effects on the forecast accuracy. We also found that the inclusion of numerical weather prediction variables, data normalization, and data resampling are the most effective data processing techniques. Furthermore, we found some evidence for cherry picking in reporting errors and recommend that the test sets be at least one year to better assess model performance. The paper also takes the first step towards establishing a benchmark for assessing PV output forecasts.

stat.AP↗

arXiv2021

Risk aversion in flexible electricity markets

Flexibility options, such as demand response, energy storage and interconnection, have the potential to reduce variation in electricity prices between different future scenarios, therefore reducing investment risk. Moreover, investment in flexibility options can lower the need for generation capacity. However, there are complex interactions between different flexibility options. In this paper, we investigate the interactions between flexibility and investment risk in electricity markets. We employ a large-scale stochastic transmission and generation expansion model of the European electricity system. Using this model, we first investigate the effect of risk aversion on the investment decisions. We find that the interplay of parameters leads to (i) more investment in a less emission-intensive energy system if planners are risk averse (hedging against CO2 price uncertainty) and (ii) constant total installed capacity, regardless of the level of risk aversion (planners do not hedge against demand and RES deployment uncertainties). Second, we investigate the individual effects of three flexibility elements on optimal investment levels under different levels of risk aversion: demand response, investment in additional interconnection capacity and investment in additional energy storage. We find that that flexible technologies have a higher value for risk-averse decision-makers, although the effects are nonlinear. Finally, we investigate the interactions between the flexibility elements. We find that risk-averse decision-makers show a strong preference for transmission grid expansion once flexibility is available at low cost levels.

econ.GN↗

arXiv2021

Lessons Learned from Photovoltaic Auctions in Germany

Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and - using correlation and regression analysis - explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions.

econ.GN↗

arXiv2020

Modelling uncertainty in coupled electricity and gas systems -- is it worth the effort?

The interdependence of electricity and natural gas markets is becoming a major topic in energy research. Integrated energy models are used to assist decision-making for businesses and policymakers addressing challenges of energy transition and climate change. The analysis of complex energy systems requires large-scale models, which are based on extensive databases, intertemporal dynamics and a multitude of decision variables. Integrating such energy system models results in increased system complexity. This complexity poses a challenge for energy modellers to address multiple uncertainties that affect both markets. Stochastic optimisation approaches enable an adequate consideration of uncertainties in investment and operation planning; however, stochastic modelling of integrated large-scale energy systems further scales the level of complexity. In this paper, we combine integrated and stochastic optimisation problems and parametrise our model for European electricity and gas markets. We analyse and compare the impact of uncertain input parameters, such as gas and electricity demand, renewable energy capacities and fuel and CO2 prices, on the quality of the solution obtained in the integrated optimisation problem. Our results quantify the value of encoding uncertainty as a part of a model. While the methodological contribution should be of interest for energy modellers, our findings are relevant for industry experts and stakeholders with an empirical interest in the European energy system.

q-fin.GN↗