TY - GEN A1 - Genge, Lucien A1 - Scheller, Fabian A1 - Müsgens, Felix T1 - Supply costs of green chemical energy carriers at the European border: A meta-analysis T2 - International Journal of Hydrogen Energy N2 - Importing green chemical energy carriers is crucial for meeting European climate targets. However, estimating the costs of supplying these energy carriers to Europe remains challenging, leading to a wide range of reported supply-cost estimates. This study analyzes the estimated supply costs of green chemical energy carriers at the European border using a dataset of 1050 data points from 30 studies. The results reveal significant variations in supply costs, with a projected four-fold difference in 2030 and a five-fold difference in 2050 across all energy carriers. The main drivers of cost differences are varying production costs, particularly influenced by the weighted average costs of capital and capital expenditures of renewable energy sources, electrolyzers, and carrier-specific conversion processes. Transport costs also contribute to variations, mainly influenced by the choice of energy carrier and the weighted average costs of capital. To optimize cost-efficiency and sustainability in the chemical energy carrier sector, this paper recommends prioritizing transparency and sensitivity analyses of key input parameters, classifying energy carriers based on technological and economic status, and encouraging research and development to reduce production costs. KW - Green chemical energy carriers KW - Hydrogen derivates KW - Hydrogen supply costs KW - Hydrogen production costs KW - Hydrogen transportation costs KW - Meta-analysis Y1 - 2023 U6 - https://doi.org/10.1016/j.ijhydene.2023.06.180 SN - 0360-3199 VL - 48 IS - 98 SP - 38766 EP - 38781 ER - TY - GEN A1 - Watermeyer, Mira A1 - Möbius, Thomas A1 - Grothe, Oliver A1 - Müsgens, Felix T1 - A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling T2 - arXiv N2 - 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. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2304.09336 SP - 1 EP - 38 ER - TY - GEN A1 - Möbius, Thomas A1 - Watermeyer, Mira A1 - Grothe, Oliver A1 - Müsgens, Felix T1 - Enhancing energy system models using better load forecasts T2 - Energy Systems N2 - Since energy system models require a large amount of technical and economic data, their quality significantly affects the reliability of the results. However, some publicly available data sets, such as the transmission system operators’ day-ahead load forecasts, are known to be biased and inaccurate, leading to lower energy system model performance. We propose a time series model that enhances the accuracy of transmission system operators’ load forecast data in real-time, using only the load forecast error’s history as input. We further present an energy system model developed specifically for price forecasts of the short-term day-ahead market. We demonstrate the effectiveness of the improved load data as input by applying it to this model, which shows a strong reduction in pricing errors, particularly during periods of high prices and tight markets. Our results highlight the potential of our method the enhance the accuracy of energy system models using improved input data. KW - Data pre-processing KW - Day-ahead electricity prices KW - Energy system modelling Y1 - 2023 U6 - https://doi.org/10.1007/s12667-023-00590-3 SN - 1868-3975 SP - 1 EP - 30 ER - TY - GEN A1 - Batz Liñeiro, Taimyra A1 - Müsgens, Felix T1 - Evaluating the German onshore wind auction programme: An analysis based on individual bids T2 - Energy Policy N2 - Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals, but create incentives for large actors to game the system. KW - Renewable energy KW - Auction KW - Wind KW - Onshore KW - Community energy KW - companies KW - Germany Y1 - 2023 U6 - https://doi.org/10.1016/j.enpol.2022.113317 SN - 1873-6777 SN - 0301-4215 VL - 172 ER - TY - GEN A1 - Möbius, Thomas A1 - Riepin, Iegor A1 - Müsgens, Felix A1 - van der Weijde, Adriaan H. T1 - Risk aversion and flexibility options in electricity markets T2 - Energy Economics N2 - Investments in electricity transmission and generation capacity must be made despite significant uncertainty about the future developments. The sources of this uncertainty include, among others, the future levels and spatiotemporal distribution of electricity demand, fuel costs and future energy policy. In recent years, these uncertainties have increased due to the ongoing evolution of supply- and demand-side technologies and rapid policy changes designed to encourage a transition to low-carbon energy systems. Because transmission and generation investments have long lead times and are difficult to reverse, they are subject to a considerable – and arguably growing – amount of risk. KW - Flexibility KW - Storage KW - Demand response KW - Generation and transmission KW - expansion KW - Investment KW - Risk aversion KW - Stochastic programming Y1 - 2023 SN - 0140-9883 SN - 1873-6181 VL - 126 ER - TY - GEN A1 - Hoffmann, Christin A1 - Ziemann, Niklas A1 - Penske, Franziska A1 - Müsgens, Felix T1 - The value of secure electricity supply for increasing acceptance of green hydrogen - First experimental evidence from the virtual reality lab T2 - 19th International Conference on the European Energy Market (EEM), 06-08 June 2023, Lappeenranta, Finland N2 - To unlock the high potential of green hydrogen in reaching the ambitious 1.5 °C goal declared by the Paris Agreement, enormous (public) investments are needed. Social acceptance of these investments is required in order to implement hydrogen technologies as fast and efficiently as possible. This study investigates which benefits associated with green hydrogen foster its social acceptance. Using a between-subject design, we implement two different treatments. Both treatments have in common that the participants experience the transformation into a hydrogen economy in a virtual reality scenario. In "Info Security of Supply", we provide the participants information about the benefits of green hydrogen regarding the security of energy supply and climate protection. In Control, we inform them only about the benefits of climate protection. Subsequently, the participants decide about the financial support of a hydrogen project. Our preliminary results show a higher support if the focus is solely on the positive impact of green hydrogen for climate protection. Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10161844 SN - 979-8-3503-1258-4 SN - 979-8-3503-2452-5 U6 - https://doi.org/10.1109/EEM58374.2023.10161844 SN - 2165-4093 ER - TY - GEN A1 - Riepin, Iegor A1 - Sgarciu, Smaranda A1 - Bernecker, Maximilian A1 - Möbius, Thomas A1 - Müsgens, Felix T1 - Grok It and Use It: Teaching Energy Systems Modeling T2 - SSRN eLibrary N2 - This article details our experience developing and teaching an “Energy Systems Modeling” course, which sought to introduce graduate-level students to operations research, energy economics, and system modeling using the General Algebraic Modeling System (GAMS). In this paper, we focus on (i) the mathematical problems discussed in the course, (ii) the energy-related empirical interpretations of these mathematical problems, and (iii) the best teaching practices (i.e., our experiences regarding how to make the content interesting and accessible for students). KW - Energy Systems KW - Mathematical Programming KW - Optimization KW - Teaching Y1 - 2023 U6 - https://doi.org/10.2139/ssrn.4320978 SN - 1556-5068 ER - TY - GEN A1 - Hoffmann, Christin A1 - Byrukuri Gangadhar, Shanmukha Srinivas A1 - Müsgens, Felix T1 - Smells like green energy the impact of bioenergy production on residential property values in Germany T2 - Science Direkt N2 - One requirement for the fast and efficient development of bioenergy is a clear understanding of the negative externalities bioenergy facilities cause. At this point, discussions in practice and research as to which externalities have an impact and how strong this potential impact actually is remain inconclusive. We utilize bioenergy plant construction data from Germany between 2007 and 2022 and match it with information about real estate in the plants’ vicinity. Applying improved difference-in-difference estimation procedures to analyze heterogeneous treatment effects, we interpret a causal impact of bioenergy plant commissioning on housing prices in the vicinity in terms of the average net external effects affecting the vicinity. Overall, we find a minor negative impact of -0,5 % on home prices within 1 km of bioenergy plants but no statistically significant effect beyond this distance. This suggests that visual pollution has a limited role as an externality of bioenergy plants. However, if we restrict the sample to homes downwind of bioenergy plants, we find a significant yet small negative impact on home prices, ranging from -1,0 % to - 1,3 %. Additionally, homes near bioenergy plants that use gaseous inputs - which emit stronger odors - experience price reductions between -0,4 % to -0,7 %. This lets us conclude that, besides visual pollution, odor emission may play a more significant role in affecting nearby home prices. KW - Bioenergy KW - Oder emission KW - Difference-in-differences KW - Hedonic pricing Y1 - 2025 UR - https://www.sciencedirect.com/science/article/pii/S0140988325002282 U6 - https://doi.org/10.1016/j.eneco.2025.108404 VL - 145 SP - 1 EP - 16 PB - Elsevier B.V. CY - Amsterdam ER - TY - GEN A1 - Hoffmann, Christin A1 - Jalbout, Eddy A1 - Villanueva, Monica A1 - Batz Liñeiro, Taimyra A1 - Müsgens, Felix T1 - Positive and Negative Externalities from Renewable and Conventional Power Plants in the Backyard: The Value of Participation T2 - SSRN eLibrary N2 - We quantify the net external effects of conventional and renewable electricity generators by analyzing housing prices in their vicinity. Using a Differences-in-Differences approach, we find that (1) wind turbines reduce prices significantly, (2) solar fields have no significant impact, and (3) conventional plants over 1 km away show positive net effects. We set out to explain this result by disentangling the positive local external effects of energy generation, which we measure in terms of local purchasing power and tax revenues. Our results show that the commissioning of conventional power stations results in a significant increase in both purchasing power and business tax income in the vicinity. We thus conclude that significant financial participation of the local public in the development of renewable energy projects, especially wind turbines, could be key to increasing their acceptance and accelerating their expansion. KW - Renewable energy KW - Conventional power plants KW - Hedonic valuation KW - Difference-in-Differences KW - Wind turbines KW - Solar fields KW - Financial participation KW - Acceptance Y1 - 2022 U6 - https://doi.org/10.2139/ssrn.4203184 SN - 1556-5068 SP - 1 EP - 33 ER - TY - GEN A1 - Bernecker, Maximilian A1 - Gebhardt, Marc A1 - Ben Amor, Souhir A1 - Wolter, Martin A1 - Müsgens, Felix T1 - Quantifying the impact of load forecasting accuracy on congestion management in distribution grids T2 - International journal of electrical power & energy systems N2 - Digitalization is a global trend in energy systems and beyond. However, it is often unclear what digitalization means exactly in the context of energy systems and how the benefits of digitalization can be quantified. Providing additional information, e.g., through sensors and metering equipment, is one concrete angle where digitalization contributes. This paper provides a framework to quantify the value of such additional information in distribution grids. We analyze to what extent smart meters improve the accuracy of day-ahead load forecasts and quantify the savings in congestion management costs resulting from the improved accuracy. To quantify the cost reduction, we conduct a case study employing a simplified IEEE test system. Historical electricity load data from over 6,000 smart meters was used to improve day-ahead load forecasts. We assessed and compared the forecasting performance to estimates based on standard load profiles with multiple load forecast simulations in the network based on uncertainty parameterizations from forecasts with and without smart meter data. Calculating redispatch cost in the distribution grid, we find that the forecast based on smart meter data reduces key redispatch parameters such as the share of expected voltage violations, the amount of rescheduled generation by more than 90%. These improvements translate into a reduction in congestion management costs by around 97%. Furthermore, we shed light on whether the gains increase linearly with the number of smart meters and available data in the grid. When smart meter shares are increased uniformly throughout the grid, savings are concave, i.e., the first 10% of smart meters reduces congestion management costs by around 20% while the last 10% reduces these costs only marginally. Focusing smart meter installation on the most congested nodes reduces congestion management costs by around 60% with just 10% smart meter coverage, significantly outperforming a uniform rollout. However, savings in congestion management alone are not likely to recover the installation and operation costs of the installed smart meter. KW - Distribution Grid KW - Congestion Management KW - Uncertainty KW - Forecasting KW - Smart Meter Y1 - 2025 U6 - https://doi.org/10.1016/j.ijepes.2025.110713 SN - 0142-0615 VL - 168 SP - 1 EP - 27 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Batz Liñeiro, Taimyra A1 - Müsgens, Felix T1 - Pay-back time : increasing electricity prices and decreasing costs make renewable energy competitive T2 - Energy policy N2 - The global energy transition needs a large-scale rollout of electricity generation from renewable energy sources (RES). Leading nations such as Spain, Japan, and Germany have invested early and substantially in RES. This leadership has been associated with high expenditures, but the trend is reversing as RES become more competitive. First, levelized costs of electricity for RES have decreased significantly and second, wholesale prices for electricity have increased, due to more ambitious climate protection and rising fuel prices. Despite favorable developments indicating a decline in the financial support needed for renewable deployment—and the fact that many countries still need to significantly increase their renewable capacities to meet climate objectives—renewable support has once again come under criticism. This paper demonstrates that cost-related criticism and concern is often unwarranted. By quantifying the aggregated subsidies of all RES units in Germany, which arguably are among the highest in the world, our findings reveal that: i) the net support costs of RES have been high in the past, ii) most net subsidies have already been paid and iii) newer installations of wind offshore, wind onshore and ground mounted PV are economically profitable. In addition, we show that wind onshore has been the most cost-efficient technology over time and explore the remarkable evolution of solar technologies, transitioning from one of the costliest to one of the most cost-effective options. KW - Energy transition KW - Renewable energy KW - RES act KW - Germany Y1 - 2025 U6 - https://doi.org/10.1016/j.enpol.2025.114523 SN - 1873-6777 VL - 199 SP - 1 EP - 14 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Antweiler, Werner A1 - Müsgens, Felix T1 - The new merit order : the viability of energy-only electricity markets with only intermittent renewable energy sources and grid-scale storage T2 - Energy economics N2 - What happens to the merit order of electricity markets when all electricity is supplied by intermittent renewable energy sources coupled with large-scale electricity storage? With near-zero marginal cost of production, will there still be a role for an energy-only electricity market? We answer these questions both analytically and empirically for electricity markets in Texas and Germany. What emerges in market equilibrium is the ‘new merit order’. Curtailment at zero prices is a necessary feature of the new merit order, complementing peak prices to cover fixed costs of storage. Storage cannot ‘solve’ curtailment, because curtailment provides essential zero-price periods. Our work demonstrates that as long as free entry and competition ensure effective price setting, an efficient new merit order emerges in electricity markets even when the grid is completely powered by intermittent sources with near-zero marginal costs. We find that energy-only markets remain viable and functional. KW - Electricity market KW - Equilibrium100% renewables and storage KW - System prices KW - Merit order Y1 - 2025 U6 - https://doi.org/10.1016/j.eneco.2025.108439 SN - 1873-6181 VL - 145 SP - 1 EP - 28 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Antweiler, Werner A1 - Müsgens, Felix T1 - The new merit order : the viability of energy-only electricity markets with only intermittent renewable energy sources and grid-scale storage T2 - USAEE working paper N2 - What happens to the merit order of electricity markets when all electricity is supplied by intermittent renewable energy sources coupled with large-scale electricity storage? With near-zero marginal cost of production, will there still be a role for an energy-only electricity market? We answer these questions both analytically and empirically for electricity markets in Texas and Germany. What emerges in market equilibrium is the ‘new merit order’. Our work demonstrates that as long as free entry and competition ensure effective price setting, an efficient new merit order emerges in electricity markets even when the grid is completely powered by intermittent sources with near-zero marginal costs. We find that energy only markets remain viable and functional. Y1 - 2024 UR - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4702939 U6 - https://doi.org/10.2139/ssrn.4702939 VL - 24-614 SP - 1 EP - 49 ER - TY - GEN A1 - Ben Amor, Souhir A1 - Sgarciu, Smaranda A1 - Batz Lineiro, Taimyra BatzLineiro A1 - Müsgens, Felix T1 - Advanced models for hourly marginal CO2 emission factor estimation : a synergy between fundamental and statistical approaches T2 - Applied energy N2 - Global warming is caused by increasing concentrations of greenhouse gases, particularly carbon dioxide (CO2). The reduction of carbon dioxide emissions is thus an energy policy priority. A metric to quantify the change in CO2 emissions is the marginal emission factor. Marginal emission factors are needed for various purposes, for example to analyze the emission impact of electric vehicle charging. This paper presents two methodologies to estimate the marginal emission factor in electricity systems with high temporal resolution. The first is an energy systems model that incrementally calculates the marginal emission factors. This calculation is computationally intensive when the time resolution is high, but is very accurate because it considers relevant market factors on both the supply and demand sides and emulates the electricity market dynamics. The second is a Markov Switching Dynamic Regression model, a statistical model designed to estimate marginal emission factors faster, and it is benchmarked against the dynamic linear regression model widely used in the marginal emission factor estimation literature. For the German electricity market, we estimate the marginal emission factor time series both historically (2019, 2020) using Agora Energiewende and for the future (2025, 2030, and 2040) using estimated energy system data. The results indicate that the Markov Switching Dynamic Regression model outperforms benchmark models. Hence, the Markov Switching Dynamic Regression model is a simpler alternative to the computationally intensive incremental marginal emission factor, especially when short-term marginal emission factor estimation is needed. The results of the marginal emission factor estimation are applied to an exemplary low-emission vehicle charging scenario to estimate CO2 savings by shifting the charge hours to those corresponding to the lower marginal emission factor. We implemented the emission-minimized charging approach using both marginal emission factors. Over a 5-year period, the Markov Switching Dynamic Regression model appears to save 47.9 % of emissions on average, compared to 6.5 % real-world saving. The maximal value possible with incremental MEFs would be 37.6 %. KW - Marginal emission factor KW - Energy system model KW - CO2 emissions KW - Electricity generation KW - Markov switching dynamic regression model KW - Emission-minimized vehicle charging Y1 - 2025 U6 - https://doi.org/10.1016/j.apenergy.2025.126265 SN - 1872-9118 VL - 397 SP - 1 EP - 25 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Muhammad, Sulaman A1 - Hoffmann, Christin A1 - Müsgens, Felix T1 - Assessing energy security risks : implications for household electricity prices in the EU T2 - Energy N2 - Energy security has emerged as a critical issue, especially for Europe, driven by escalating geopolitical tensions and the transition toward sustainable energy sources. Beyond affecting national energy supply, energy security also significantly influences the affordability of electricity for consumers. We examine the impact of energy security on household electricity prices, focusing on three key indicators: energy dependency, energy diversity, and geopolitical risk. Using panel data of 27 EU countries from 2007 to 2022, the analysis reveals that both energy diversity and dependency contribute to lowering electricity prices, while geopolitical risk shows no significant direct effect. However, the interaction between energy dependency and geopolitical risk reveals that during times of heightened geopolitical risks, a heavy reliance on foreign energy can lead to significantly higher electricity prices. Further 2SLS estimation and additional analysis with extended controls confirm the robustness of these findings. These findings offer valuable insights for policymakers focused on enhancing energy security. KW - Energy diversity KW - Energy dependency KW - Energy geopolitical risk KW - Electricity prices Y1 - 2025 U6 - https://doi.org/10.1016/j.energy.2025.136201 SN - 1873-6785 VL - 327 SP - 1 EP - 11 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Genge, Lucien A1 - Neuwirth, Marius A1 - Al-Dabbas, Khaled A1 - Fleiter, Tobias A1 - Müsgens, Felix T1 - Optimising green value chains for the chemical industry in Europe T2 - International journal of hydrogen energy N2 - Transforming Europe's basic chemical industries for climate neutrality necessitates strategic decisions about sourcing green ammonia and methanol. Using a spatially detailed, techno-economic optimisation model for 72 industrial sites, we compare three distinct value chain setups: domestic production, hydrogen imports, and direct commodity imports. Direct commodity imports consistently emerge as the most cost-effective strategy for most countries, with average savings of 14 % for ammonia and 18 % for methanol in 2040. However, the picture is more diverse across the individual countries. Domestic ammonia production remains competitive in regions with abundant renewables like Southern Europe and Norway, while hydrogen imports offer strategic value for the largest industrial sites in Germany, the Netherlands, and hubs near the MENA region. On average, a fully domestic production of green ammonia would result in 15 % higher costs at European level equal to about 1.4 billion €/year - compared to commodity imports. At site level, the cost premium ranges between −13 % (domestic production is cheaper than imports) and +38 %. Our findings provide policymakers with a foundation to develop industrial transition strategies that balance cost efficiency and sovereignty in the ammonia/fertiliser and methanol/chemicals value chains. They underline the importance of European cooperation by deploying best wind and solar potentials and establishing European energy transport infrastructure as backbone of a competitive net-zero industry. KW - Green value chains KW - Green ammonia KW - Green methanol KW - Sourcing strategies KW - Energy sovereignty KW - Techno-economic optimisation KW - Industrial decarbonisation Y1 - 2025 U6 - https://doi.org/10.1016/j.ijhydene.2025.152689 SN - 1879-3487 VL - 199 SP - 1 EP - 3 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Radke, Silvian M. A1 - Müsgens, Felix T1 - Mitigating negative electricity prices - batteries or flexibility in wind and solar? T2 - 2025 21st International Conference on the European Energy Market (EEM) N2 - This paper analyses the influence of battery storage systems and high shares of price-inelastic generation on electricity prices. Within the last years, negative wholesale electricity prices have become a relevant topic in many electricity systems around the world. We develop a linear cost minimization model to analyze the relationship between price-inelastic generation and battery storage. We parameterize the model with three different scenarios which describe the German electricity market in the year 2030. Our model shows that both additional storage capacities and more flexible renewable generation are necessary to avoid high volatility in electricity prices and thereby reduce the number of hours with negative electricity prices. KW - Flexibility KW - Intermittent energy KW - Renewable energy KW - Surplus energy Y1 - 2025 SN - 979-8-3315-1278-1 U6 - https://doi.org/10.1109/EEM64765.2025.11050208 SN - 2165-4093 SN - 2165-4077 SP - 1 EP - 6 PB - IEEE CY - Piscataway, NJ ER -