TY - GEN A1 - Nguyen, Thi Ngoc A1 - Müsgens, Felix T1 - What drives the accuracy of PV output forecasts? T2 - Applied Energy N2 - 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. KW - Solar energy forecasting KW - Photovoltaics forecasting KW - Evaluating forecasts KW - Forecasting practice KW - Model selection KW - Statistical analysis Y1 - 2022 U6 - https://doi.org/10.1016/j.apenergy.2022.119603 SN - 1872-9118 SN - 0306-2619 VL - 323 ER - TY - GEN A1 - Riepin, Iegor A1 - Schmidt, Matthew A1 - Baringo, Luis A1 - Müsgens, Felix T1 - Adaptive robust optimization for European strategic gas infrastructure planning T2 - Applied Energy N2 - 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. KW - Adaptive robust optimization KW - Capacity planning KW - European gas market KW - Uncertainty Y1 - 2022 U6 - https://doi.org/10.1016/j.apenergy.2022.119686 SN - 1872-9118 SN - 0306-2619 VL - 324 ER - TY - GEN A1 - Jalbout, Eddy A1 - Genge, Lucien A1 - Riepin, Iegor A1 - Müsgens, Felix T1 - What do we know about green hydrogen supply costs? T2 - 18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia N2 - 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. KW - Industries KW - Costs KW - Uncertainty KW - Hydrogen KW - Supply chains KW - Europe KW - IEEE Fellows Y1 - 2022 SN - 978-1-6654-0896-7 SN - 978-1-6654-0897-4 U6 - https://doi.org/10.1109/EEM54602.2022.9921127 SN - 2165-4093 SN - 2165-4077 SP - 1 EP - 7 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Bernecker, Maximilian A1 - Riepin, Iegor A1 - Müsgens, Felix T1 - Modeling of Extreme Weather Events—Towards Resilient Transmission Expansion Planning T2 - 18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia N2 - 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. KW - Adaptation models KW - Renewable energy sources KW - Wind KW - Uncertainty KW - Mathematical models KW - Power systems KW - Planning Y1 - 2022 SN - 978-1-6654-0896-7 SN - 978-1-6654-0897-4 U6 - https://doi.org/10.1109/EEM54602.2022.9921145 SN - 2165-4093 SN - 2165-4077 SP - 1 EP - 7 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Nguyen, Thi Ngoc A1 - Müsgens, Felix T1 - A Meta-Analysis of Solar Forecasting Based on Skill Score T2 - arXiv N2 - 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. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2208.10536 SP - 1 EP - 55 ER - TY - GEN A1 - Jalbout, Eddy A1 - Genge, Lucien A1 - Müsgens, Felix T1 - H2Europe: an analysis of long-term hydrogen import-potentials from the MENA region T2 - 18th International Conference on the European Energy Market (EEM), 13-15 September 2022, Ljubljana, Slovenia N2 - Our paper assesses the economic viability of green hydrogen exports from the middle east and north Africa to the EU in the long-term (for the year 2050). For this purpose, we simulate a techno-economic hydrogen supply chain based on generation from renewable energy sources. The chain of production, conversion, and transportation costs, in combination with potentials, yields a merit order of hydrogen supply. The paper focuses on one low-cost option for imports from the middle east and north Africa region. In addition, it provides a classification of additional options. We find that due to proximity, retrofitting and potentially even installing new pipelines from Morocco, Algeria and Tunisia could provide nearly half the expected European demand of 60 million tons p.a. by 2050. Based on our assumptions, the costs would be around 2 €/kgH 2 . KW - Renewable energy sources KW - Costs KW - Pipelines KW - Supply chains KW - Green products KW - Europe KW - Hydrogen Y1 - 2022 SN - 978-1-6654-0896-7 SN - 978-1-6654-0897-4 U6 - https://doi.org/10.1109/EEM54602.2022.9921055 SN - 2165-4093 SN - 2165-4077 SP - 1 EP - 7 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Müsgens, Felix A1 - Radke, Silvian T1 - Warmer Lichtsturm - Umgang mit Erzeugungsspitzen aus PV und Wind : Arbeitspaket 1 T2 - Warmer Lichtsturm - Umgang mit Erzeugungsspitzen aus PV und Wind N2 - Über das Scientific Advisory & Project Board Die vorliegende Kurzstudie wurde von einem Konsortium entwickelt, das sich aus Mitgliedern des 50Hertz Scientific Advisory and Project Boards (SAPB) zusammensetzt. Das SAPB ist ein Kreis von Professorinnen und Professoren, die 50Hertz darin unterstützen, neue Denk- und Lösungsansätze zu entwickeln und Erkenntnisse aus der Forschung – wo angemessen – in die Praxis von 50Hertz zu übernehmen. Disclaimer Im Kontext der durch 50Hertz beauftragten SAPB-Studien forschen die beteiligten Wissenschaftlerinnen und Wissenschaftler frei und ergebnisoffen. Alle durch das Konsortium entwickelten Handlungsempfehlungen werden von 50Hertz eingehend geprüft und diskutiert, letztendlich entsprechen aber nicht alle Positionen denen von 50Hertz. KW - Wind-Einspeisungsspitzen KW - PV-Zubau Y1 - 2024 UR - https://www.50hertz.com/xspProxy/api/staticfiles/50hertz-client/dokumente/unternehmen/partnerschaften/scientific%20advisory%20project%20board/berichte%20warmer%20lichtsturm/sapb_warmer-lichtsturm_langfassung.pdf VL - 2024 SP - 13 EP - 30 ER - TY - GEN A1 - Müsgens, Felix A1 - Genge, Lucien T1 - European hydrogen infrastructure planning : insights from the TransHyDE project system analysis T2 - Flagship Project TransHyDE N2 - The white paper was developed by a selected authorship of the TransHyDE Project System Analysis. The contents of the TransHyDE publications are produced in the project independently of the Federal Ministry of Education and Research. Y1 - 2024 UR - https://www-docs.b-tu.de/fg-energiewirtschaft/public/Veroeffentlichungen/European%20Hydrogen%20Infrastructure%20Planning.pdf VL - 2024 ER - TY - BOOK A1 - Müsgens, Felix A1 - Bade, Alexander T1 - Energy trading and risk management N2 - This book offers important insights into the intricacies of energy trading and risk management to students and professionals in the liberalized electricity and natural gas markets. In its opening chapter, the book delves into fundamental concepts, including price formation on wholesale markets. The various market places and trading products are presented, and the distinguishing characteristics of electricity and natural gas compared to other commodities are emphasised. Moving forward, the next chapter concentrates on portfolio management, offering insights into the trading process through the perspectives of various agents. It begins with proprietary traders and progresses to encompass the portfolio management activities of integrated companies responsible for both generation assets and a retail client base. The book then looks at risk management, explaining the diverse risks that impact the value of energy portfolios inthe market. It places emphasis on credit risk, price risk, quantity risk, and product liquidity risk as the most influential factors and presents effective practices for their management. Furthermore, readers will gain a comprehensive understanding of how to manage quantity risk and its interconnectedness with price risk. Additionally, the book addresses the measurement of product liquidity and its disparities across different energy market products. The book then looks at risk management, explaining the diverse risks that impact the value of energy portfolios inthe market. It places emphasis on credit risk, price risk, quantity risk, and product liquidity risk as the most influential factors and presents effective practices for their management. Furthermore, readers will gain a comprehensive understanding of how to manage quantity risk and its interconnectedness with price risk. Additionally, the book addresses the measurement of product liquidity and its disparities across different energy market products. In summary, this book is an accessible introduction and a concise reference for students and professionals across all fields of energy. KW - Energy Trading KW - Risk Management KW - Electricity KW - Portfolio Management KW - Commodity Trading KW - Options, Futures and Derivatives KW - Power Plant Real Optiononality KW - Power Trading Y1 - 2024 UR - https://link.springer.com/book/10.1007/978-3-031-57238-8 SN - 978-3-031-57237-1 SN - 978-3-031-57238-8 U6 - https://doi.org/10.1007/978-3-031-57238-8 PB - Springer Link CY - Schweiz ET - 1. Auflage ER - TY - GEN A1 - Ben Amor, Souhir A1 - Akkal Devi, Prashanth A1 - Müsgens, Felix T1 - Meta-forecasting for solar power generation: algorithm-based swarm intelligence T2 - 20th International Conference on the European Energy Market (EEM) N2 - Our paper introduces a dynamic meta-forecasting approach that optimizes the weights of several available forecasts using a swarm intelligence algorithm, named Particle Swarm Optimization. This algorithm was chosen for its efficiency and convergence performance in solving optimization problems. We apply the methodology to solar photovoltaic day-ahead forecasts utilizing regional data from Germany. Our sample covers the period between 2019 and 2022 at a quarter-hourly frequency. We provide day-ahead forecasts using a rolling estimation window. Alongside benchmark models, Particle Swarm Optimization predictive accuracy is comparable to state-of-the-art models, particularly a dynamic elastic net benchmark model. However, there are slight differences. The investor can choose between Particle Swarm Optimization for its simple implementation and computational efficiency, or dynamic elastic net models for its parameters explainability. KW - Dynamic KW - Meta-forecast KW - Solar feed-in KW - Swarm intelligence optimization Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10608959/keywords#keywords U6 - https://doi.org/10.1109/EEM60825.2024.10608959 SN - 2165-4093 SN - 2165-4077 PB - IEEE ER - TY - GEN A1 - Grübler, Lene Marie A1 - Müsgens, Felix T1 - Applying spatial decomposition in energy system models T2 - 20th International Conference on the European Energy Market (EEM) N2 - The European energy transition requires the expansion of renewable generators and consequently, the expansion of grid infrastructure and flexibility options. When modelling complex energy systems, the application of decomposition techniques is reasonable to keep models computationally tractable. Our paper focuses on the efficient optimization of energy systems covering large geographic areas. To accelerate the solving process, we apply a variant of Benders decomposition, which does not only apply temporal but also spatial decomposition. We show that by applying our decomposition approach, the solving time decreases up to 75% compared to the runtime of the equivalent monolithic model solved by a commercial solver and the barrier algorithm. Furthermore, we show that additionally applying spatial decomposition results in time savings compared to an only temporally decomposed approach. KW - Benders decomposition KW - Complexity reduction energy systems modeling KW - Grid expansion planning KW - Spatial decomposition Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10608869 U6 - https://doi.org/10.1109/EEM60825.2024.10608869 SN - 2165-4093 SN - 2165-4077 VL - 2024 ER -