@inproceedings{NoltingSpiegelReichetal.2018, author = {Nolting, Lars and Spiegel, Thomas and Reich, Marius and Adam, Mario and Praktiknjo, Aaron}, title = {Can Energy System Modeling benefit from Artificial Neural Networks? Application of Two-Stage Metamodels to Reduce Computation of Security of Supply Assessments (Best Application Paper Award)}, series = {CIE - 48th International Conference on Computers \& Industrial Engineering, 02.-05.12.2018, Auckland / New Zealand}, booktitle = {CIE - 48th International Conference on Computers \& Industrial Engineering, 02.-05.12.2018, Auckland / New Zealand}, address = {Auckland}, year = {2018}, language = {en} } @inproceedings{ReichAdamGottschald2018, author = {Reich, Marius and Adam, Mario and Gottschald, Jonas}, title = {Robust Optimization of District Heating Networks Structure and Dimension combining Metamodels and Multi-Objective Optimization}, series = {ECOS - 31th International Conference on Efficiency, Cost, Optimization, Simulation and Enviromental Impact of Energy Systems, 17.-21.06.2018, Guimaraes / Portugal}, booktitle = {ECOS - 31th International Conference on Efficiency, Cost, Optimization, Simulation and Enviromental Impact of Energy Systems, 17.-21.06.2018, Guimaraes / Portugal}, address = {Guimaraes}, year = {2018}, language = {en} } @inproceedings{NeumannAdamBackesetal.2021, author = {Neumann, Hannah and Adam, Mario and Backes, Klaus and B{\"o}rner, Martin and Clees, Tanja and Doetsch, Christian and Glaeser, Susanne and Herrmann, Ulf and May, Johanna and Rosenthal, Florian and Sauer, Dirk Uwe and Stadler, Ingo}, title = {Development of open educational resources for renewable energy and the energy transition process}, series = {ISES SWC 2021: Solar World Congress Virtual Conference, 25.-29. Oktober 2021}, booktitle = {ISES SWC 2021: Solar World Congress Virtual Conference, 25.-29. Oktober 2021}, publisher = {International Solar Energy Society}, organization = {The International Solar Energy Society (ISES)}, doi = {10.18086/swc.2021.47.03}, year = {2021}, language = {en} } @unpublished{PriesmannMuenchRidhaetal.2021, author = {Priesmann, Jan and M{\"u}nch, Justin and Ridha, Elias and Spiegel, Thomas and Reich, Marius and Adam, Mario and Nolting, Lars and Praktiknjo, Aaron}, title = {Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook}, publisher = {arXiv}, doi = {https://doi.org/10.48550/arXiv.2112.04889}, year = {2021}, abstract = {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.}, language = {en} } @article{MuenchPriesmannReichetal.2024, author = {M{\"u}nch, Justin and Priesmann, Jan and Reich, Marius and Tillmanns, Marius and Praktiknjo, Aaron and Adam, Mario}, title = {Uplifting the Complexity of Analysis for Probabilistic Security of Electricity Supply Assessments using Artificial Neural Networks}, series = {Energy and AI}, journal = {Energy and AI}, publisher = {Elsevier}, issn = {2666-5468}, doi = {10.1016/j.egyai.2024.100401}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-45359}, year = {2024}, abstract = {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.}, language = {en} } @article{PriesmannMuenchTillmannsetal.2024, author = {Priesmann, Jan and M{\"u}nch, Justin and Tillmanns, Marius and Ridha, E. and Spiegel, Thomas and Reich, Marius and Adam, Mario and Nolting, L. and Praktiknjo, Aaron}, title = {Artificial intelligence and design of experiments for resource adequacy assessment in power systems}, series = {Energy Strategy Reviews}, volume = {53}, journal = {Energy Strategy Reviews}, publisher = {Elsevier}, issn = {2211-467X}, doi = {10.1016/j.esr.2024.101368}, year = {2024}, language = {en} }