@misc{EngelhornMuesgens, author = {Engelhorn, Thorsten and M{\"u}sgens, Felix}, title = {Why is Germany's energy transition so expensive? Quantifying costs from wind energy decentralisation}, series = {Resource and Energy Economics}, volume = {65}, journal = {Resource and Energy Economics}, issn = {1873-0221}, doi = {10.1016/j.reseneeco.2021.101241}, pages = {18}, abstract = {This paper discusses the efficiency of promotion schemes for renewable energy sources using the example of onshore wind energy in Germany. We analyse whether the scheme incentivised a cost-minimal capacity build-up by developing a model to derive two cost-minimal benchmark scenarios, which are then compared to the historical capacity build-up between 1995 and 2015. The costs of the two cost-minimising benchmark scenarios are significantly lower than those of the historical build-up. The benchmark cost reduction largely stems from greater efficiency—fewer turbines are being constructed overall but they are being placed at better wind sites so the annual production of wind energy remains unchanged. Hence, aggregated turbine land use is also significantly down in these scenarios. Furthermore, we compare costs for consumers, as protecting consumers from price discrimination of producers is sometimes used to justify higher payments for low-wind sites. However, our results show that the efficiency gain from building at high wind sites outweighs the distributional effect, even from a consumer's perspective.}, language = {en} } @misc{AntweilerMuesgens, author = {Antweiler, Werner and M{\"u}sgens, Felix}, title = {On the long-term merit order effect of renewable energies}, series = {Energy Economics}, volume = {99}, journal = {Energy Economics}, issn = {0140-9883}, doi = {10.1016/j.eneco.2021.105275}, pages = {19}, abstract = {The merit order effect describes the lowering of the average wholesale electricity price due to increased capacity of renewable energies. This effect has been observed in many places. While it is beneficial for consumers, it has triggered concerns about the economic viability of legacy plants. Is this merit order effect a permanent feature of high shares of renewable energy, or merely a transitory phase? This article develops a simple theoretical model to shed light on this question. It introduces intermittent renewable energies into a conventional mix of base load and peak load and investigates the effect when base and peak load remain unchanged in the short-term, and when they fully adjust in the long-term. We find that the merit order effect is a temporary phenomenon that is caused by the slow pace of capacity adjustments. We also find that the introduction of renewables has a mild beneficial long-term effect when base load is provided monopolistically, as renewables compete with base load to a limited extent. Consequently, concerns about the adverse consequences for conventional power plants resulting from renewable capacity are only partially justified. While in the short-term the generation portfolio suffers from "stranded assets", market forces will gradually reduce overcapacity. We also confirm that renewable expansion can be complemented by energy-only markets to incentivize efficient investment in conventional capacity.}, language = {en} } @misc{RiepinMuesgens, author = {Riepin, Iegor and M{\"u}sgens, Felix}, title = {Seasonal flexibility in the European natural gas market}, series = {The Energy Journal}, volume = {43}, journal = {The Energy Journal}, number = {1}, issn = {1944-9089}, doi = {10.5547/01956574.43.1.irie}, pages = {117 -- 138}, abstract = {This paper focuses on seasonal demand swings in the European natural gas market. We quantify and compare the role of different flexibility options (domestic production, gas storage, and pipeline and LNG imports) to assess European demand fluctuations in monthly resolution. We contribute to the existing literature on seasonal flexibility by addressing the problem with a mathematical gas market optimization model. Our paper provides valuable empirical insights into the decline of gas production in northwestern Europe. Furthermore, we focus on how specific flexibility features differ between pipeline supplies and LNG supplies and between gas imports and storage dispatch. In terms of methodology, we construct a bottom-up market optimization model and publish the complete source code (which is uncommon for gas market models). Furthermore, we propose a new metric—the scaled coefficient of variation—to quantify the importance of supply sources for seasonal flexibility provision.}, language = {en} } @misc{NikodinoskaKaesoMuesgens, author = {Nikodinoska, Dragana and K{\"a}so, Mathias and M{\"u}sgens, Felix}, title = {Solar and wind power generation forecasts using elastic net in time-varying forecast combinations}, series = {Applied Energy}, volume = {306, Part A}, journal = {Applied Energy}, issn = {1872-9118}, doi = {10.1016/j.apenergy.2021.117983}, pages = {17}, abstract = {Precise renewable energy feed-in forecasts are essential for an effective and efficient integration of renewables into energy systems, and research contributions that help to reduce the uncertainty related to renewables are in high demand. This importance will increase in the future, as renewable energies are the world's fastest growing electricity generation capacities. Forecast combinations have been empirically proven to outperform individual forecasting models in many disciplines. Our work uses an elastic net method, with cross-validation and rolling window estimation, in the context of renewable energy forecasts. Namely, the forecast combinations are obtained using regional data from Germany for both solar photovoltaic and wind feed-in during the period 2010-2018, with quarter-hourly frequency. The dynamic elastic net estimation, preceded by dynamic data pre-processing, improves forecasting accuracy for both photovoltaic and wind power feed-in forecasts. Moreover, our forecasting framework outperforms benchmarks such as simple average and individual forecasts. Our forecasting framework can be applied widely to estimate renewable power in other countries, systems, or individual power plants.}, language = {en} } @misc{JansenBeiterRiepinetal., author = {Jansen, Malte and Beiter, Philipp and Riepin, Iegor and M{\"u}sgens, Felix and Guajardo-Fajardo, Victor Juarez and Staffell, Iain and Bulder, Bernard and Kitzing, Lena}, title = {Policy choices and outcomes for the competitive procurement of offshore wind energy globally}, series = {Energy Policy}, volume = {167}, journal = {Energy Policy}, issn = {0301-4215}, doi = {10.1016/j.enpol.2022.113000}, abstract = {Offshore wind energy is rapidly expanding, facilitated largely through auctions run by governments. We provide a detailed quantified overview of utilised auction schemes, including geographical spread, volumes, results, and design specifications. Our comprehensive global dataset reveals heterogeneous designs. Although most auction designs provide some form of revenue stabilisation, their specific instrument choices vary and include feed-in tariffs, one-sided and two-sided contracts for difference, mandated power purchase agreements, and mandated renewable energy certificates. We review the schemes used in all eight major offshore wind jurisdictions across Europe, Asia, and North America and evaluate bids in their jurisdictional context. We analyse cost competitiveness, likelihood of timely construction, occurrence of strategic bidding, and identify jurisdictional aspects that might have influenced auction results. We find that auctions are embedded within their respective regulatory and market design context, and are remarkably diverse, though with regional similarities. Auctions in each jurisdiction have evolved and tend to become more exposed to market price risks over time. Less mature markets are more prone to make use of lower-risk designs. Still, some form of revenue stabilisation is employed for all auctioned offshore wind energy farms analysed here, regardless of the specific policy choices. Our data confirm a coincidence of declining costs and growing diffusion of auction regimes.}, language = {en} } @misc{NguyenMuesgens, author = {Nguyen, Thi Ngoc and M{\"u}sgens, Felix}, title = {What drives the accuracy of PV output forecasts?}, series = {Applied Energy}, volume = {323}, journal = {Applied Energy}, issn = {1872-9118}, doi = {10.1016/j.apenergy.2022.119603}, abstract = {In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of "cherry picking" in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models.}, language = {en} } @misc{RiepinSchmidtBaringoetal., author = {Riepin, Iegor and Schmidt, Matthew and Baringo, Luis and M{\"u}sgens, Felix}, title = {Adaptive robust optimization for European strategic gas infrastructure planning}, series = {Applied Energy}, volume = {324}, journal = {Applied Energy}, issn = {1872-9118}, doi = {10.1016/j.apenergy.2022.119686}, abstract = {An adaptive robust optimization model for a gas transmission expansion problem. Analysis of Projects of Common Interests' value in maintaining gas system resilience. Endogenous realizations of cold-winter demand spikes and supply shortages. Publicly available source code for the entire project.}, language = {en} } @misc{JalboutGengeRiepinetal., author = {Jalbout, Eddy and Genge, Lucien and Riepin, Iegor and M{\"u}sgens, Felix}, title = {What do we know about green hydrogen supply costs?}, series = {18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia}, journal = {18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-6654-0896-7}, issn = {2165-4093}, doi = {10.1109/EEM54602.2022.9921127}, pages = {1 -- 7}, abstract = {This paper is set up to answer the question: What do we know about future hydrogen import costs on the European border? Our work focuses on breaking down, structuring and analysing the body of literature on hydrogen supply chains. We provide a comprehensive analysis on (i) What ranges of costs exist in the literature for specific steps of hydrogen supply chains? (ii) Which assumptions show the highest uncertainty? (iii) Related, what assumption seems to have the most substantial effect on the resulting EUR/kgH2 import cost estimate? While we narrow our discussion focus on North Africa and Australia to Europe in 2050, we hope policymakers, research fellows and energy-sector stakeholders around the globe might be interested in our results.}, language = {en} } @misc{BerneckerRiepinMuesgens, author = {Bernecker, Maximilian and Riepin, Iegor and M{\"u}sgens, Felix}, title = {Modeling of Extreme Weather Events—Towards Resilient Transmission Expansion Planning}, series = {18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia}, journal = {18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-6654-0896-7}, issn = {2165-4093}, doi = {10.1109/EEM54602.2022.9921145}, pages = {1 -- 7}, abstract = {In this paper, we endogenously compute worst-case weather events in a transmission system expansion planning problem using the robust optimization approach. Mathematically, we formulate a three-level mixed-integer optimization problem, which we convert to a bi-level problem via the strong duality concept. We solve the problem using a constraint-and-column generation algorithm. We use cardinality-constrained uncertainty sets to model the effects of extreme weather realizations on supply from renewable generators.}, language = {en} } @misc{NguyenMuesgens, author = {Nguyen, Thi Ngoc and M{\"u}sgens, Felix}, title = {A Meta-Analysis of Solar Forecasting Based on Skill Score}, series = {arXiv}, journal = {arXiv}, doi = {10.48550/arXiv.2208.10536}, pages = {1 -- 55}, abstract = {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.}, language = {en} }