TY - CHAP A1 - Neumann, Hannah A1 - Adam, Mario A1 - Backes, Klaus A1 - Börner, Martin A1 - Clees, Tanja A1 - Doetsch, Christian A1 - Glaeser, Susanne A1 - Herrmann, Ulf A1 - May, Johanna A1 - Rosenthal, Florian A1 - Sauer, Dirk Uwe A1 - Stadler, Ingo T1 - Development of open educational resources for renewable energy and the energy transition process T2 - ISES SWC 2021: Solar World Congress Virtual Conference, 25.-29. Oktober 2021 KW - OER4EE Y1 - 2021 U6 - https://doi.org/10.18086/swc.2021.47.03 PB - International Solar Energy Society ER - TY - INPR A1 - Priesmann, Jan A1 - Münch, Justin A1 - Ridha, Elias A1 - Spiegel, Thomas A1 - Reich, Marius A1 - Adam, Mario A1 - Nolting, Lars A1 - Praktiknjo, Aaron T1 - Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook N2 - Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.48550/arXiv.2112.04889 PB - arXiv ER - TY - JOUR A1 - Münch, Justin A1 - Priesmann, Jan A1 - Reich, Marius A1 - Tillmanns, Marius A1 - Praktiknjo, Aaron A1 - Adam, Mario T1 - Uplifting the Complexity of Analysis for Probabilistic Security of Electricity Supply Assessments using Artificial Neural Networks JF - Energy and AI N2 - The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 minutes to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time. KW - Security of electricity supply KW - Probabilistic simulation KW - Metamodeling KW - Artificial neural networks KW - Regression KW - HSD Publikationsfonds KW - DFG Publikationskosten KW - KIVi Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-45359 SN - 2666-5468 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences. PB - Elsevier ER - TY - JOUR A1 - Priesmann, Jan A1 - Münch, Justin A1 - Tillmanns, Marius A1 - Ridha, E. A1 - Spiegel, Thomas A1 - Reich, Marius A1 - Adam, Mario A1 - Nolting, L. A1 - Praktiknjo, Aaron T1 - Artificial intelligence and design of experiments for resource adequacy assessment in power systems JF - Energy Strategy Reviews KW - KiVi KW - Design of experiments KW - Resource adequacy KW - Security of supply KW - Artificial intelligence KW - KIVi Y1 - 2024 U6 - https://doi.org/10.1016/j.esr.2024.101368 SN - 2211-467X VL - 53 PB - Elsevier ER -