Uplifting the Complexity of Analysis for Probabilistic Security of Electricity Supply Assessments using Artificial Neural Networks
- 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,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.…


| Author: | Justin Münch, Jan Priesmann, Marius Reich, Marius Tillmanns, Aaron Praktiknjo, Mario Adam |
|---|---|
| Qualitätssicherung: | peer reviewed |
| open access: | Gold - Erstveröffentlichung mit Lizenzhinweis |
| Institutes: | Zentrum für Innovative Energiesysteme (ZIES) |
| Zentrum für Innovative Energiesysteme (ZIES) / E² - Erneuerbare Energien und Energieeffizienz | |
| agreement: | DEAL Elsevier |
| Fachbereich/Einrichtung: | Hochschule Düsseldorf / Fachbereich - Maschinenbau und Verfahrenstechnik |
| Document Type: | Article |
| Year of Completion: | 2024 |
| Language of Publication: | English |
| Publisher: | Elsevier |
| Parent Title (English): | Energy and AI |
| Article Number: | 100401 |
| URN: | urn:nbn:de:hbz:due62-opus-45359 |
| DOI: | https://doi.org/10.1016/j.egyai.2024.100401 |
| ISSN: | 2666-5468 |
| Tag: | DFG Publikationskosten; HSD Publikationsfonds; KIVi Artificial neural networks; Metamodeling; Probabilistic simulation; Regression; Security of electricity supply |
| Funding institution: | DFG / zentraler HSD-Publikationsfonds |
| Corresponding Author: | Justin Münch |
| Information on the Research Data: | The data that support the findings of this scientific publication are available from the corresponding author upon reasonable request. |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten |
| Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
| Release Date: | 2024/09/11 |
| Note: | Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences. |
