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 - 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 - 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 - Bernecker, Maximilian A1 - Genge, Lucien T1 - EU's hydrogen infrastructure planning : addressing the impact of demand uncertainty T2 - 2025 21st International Conference on the European Energy Market (EEM) N2 - Green hydrogen will play a key role to decarbonize the future European energy mix. However, the demand for green hydrogen is highly uncertain, influencing investment and policy implications. We perform a meta-study of future hydrogen demand scenarios for the year 2050 based on 32 empirical studies. With this foundation, we develop scenarios to examine how uncertainty in hydrogen demand affects the need for European infrastructure expansion using a linear optimization model covering the European electricity and hydrogen sectors. KW - Infrastructure Expansion Planning KW - Stochastic Programming KW - Uncertainty Y1 - 2025 SN - 979-8-3315-1278-1 U6 - https://doi.org/10.1109/EEM64765.2025.11050142 SN - 2165-4093 SN - 2165-4077 SP - 1 EP - 7 PB - IEEE CY - Piscataway, NJ ER -