@unpublished{MuenchPriesmannReichetal.2024, author = {M{\"u}nch, Justin and Priesmann, Jan and Reich, Marius and Tillmanns, Marius and Praktiknjo, Aaron and Adam, Mario},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Artificial Neural Networks}, publisher = {Elsevier}, doi = {10.2139/ssrn.4770407}, year = {2024}, language = {en} } @unpublished{PriesmannMuenchRidhaetal.2021, author = {Priesmann, Jan and M{\"u}nch, Justin and Ridha, Elias and Spiegel, Thomas and Reich, Marius and Adam, Mario and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}, 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{PriesmannMuenchTillmannsetal.2024, author = {Priesmann, Jan and M{\"u}nch, Justin and Tillmanns, Marius and Ridha, E. and Spiegel, T. 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} }