TY - JOUR A1 - Nolting, Lars A1 - Spiegel, Thomas A1 - Reich, Marius A1 - Adam, Mario A1 - Praktiknjo, Aaron T1 - Can energy system modeling benefit from artificial neural networks? Application of two-stage metamodels to reduce computation of security of supply assessments JF - Computers & Industrial Engineering KW - KIVi Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-26979 SN - 0360-8352 VL - 142 PB - Elsevier ER - TY - CHAP A1 - Nolting, Lars A1 - Spiegel, Thomas A1 - Reich, Marius A1 - Adam, Mario A1 - Praktiknjo, Aaron T1 - 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) T2 - CIE – 48th International Conference on Computers & Industrial Engineering, 02.-05.12.2018, Auckland / New Zealand KW - KIVi Y1 - 2018 CY - Auckland 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 - 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 -