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.
翻译:评估能源过渡和能源市场自由化对资源充足性的影响是一项越来越重要和艰巨的任务,能源系统日益复杂,要求有适当的能源系统建模方法,导致计算要求增加。此外,由于复杂,不确定性也随之增加,要求进行概率评估和情景分析。为了充分有效地满足这些不同要求,需要从数据科学领域采用新方法来加快目前的方法。通过系统的文献审查,我们希望缩小以下三个学科之间的差距:(1)对电力供应安全的评估,(2)人工智能和(3)实验设计。为此,我们对某些应用领域和方法进行大规模的数量审查,并综合不同学科之间的关系。除其他调查结果外,我们确定采用AI方法来模拟复杂的电力供应安全模式,并应用AI为基础的方法来预测储存发送和(非)稳定性,作为尚未充分涵盖的有前途的应用领域。我们最后要找到一条新的方法管道,以便充分和高效地处理目前和今后在评估电力供应安全方面面临的挑战。