Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

作     者：Justin Münch Jan Priesmann Marius Reich Marius Tillmanns Aaron Praktiknjo Mario Adam 

作者机构：University of Applied Sciences DuesseldorfCentre of Innovative Energy SystemsMuensterstr.15640476 DuesseldorfGermany RWTH Aachen UniversityE.ON Energy Research CenterChair for Energy System Economics(FCN-ESE)Mathieustrasse 1052074 AachenGermany 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2024年第17卷第3期

页      面：313-326页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080802[工学-电力系统及其自动化] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funded by the Deutsche Forschungsgemeinschaft(DFG German Research Foundation)-532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences 

主　　题：Security of electricity supply Probabilistic simulation Metamodeling Artificial neural networks Regression 

摘      要：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 min 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.