Artificial intelligence and design of experiments for resource adequacy assessment in power systems

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 of 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 modeling approaches. With our systematic literature review, we want to close the gap between the three disciplines (1) resource adequacy assessments, (2) artificial intelligence (AI), and (3) design of experiments. For this, we conduct a large-scale systematic review of 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 probabilistic resource adequacy assessment 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 yet been sufficiently covered. Eventually, we define a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of resource adequacy accounting for modeling the complexity and uncertainties of future developments.