Application of Artificial Intelligence in Adequacy Evaluation of Power Systems

Abstract

The expansion of modern power systems is accelerating due to the rapid integration of components such as generators, transformers, and transmission lines. As a result, the number of system events in a power system exponentially increases. With the increase in system events, using traditional analytical and simulation methods for evaluating the adequacy of power systems has become computationally intractable. Further, integrating renewable power on a large scale requires accurate modeling. Over the past two decades, Artificial Intelligence (AI) techniques have been applied to improve the efficiency of power system adequacy evaluation. Branches of AI widely used in power system adequacy evaluation include machine learning methods and AI-based optimization algorithms. This chapter offers a much-needed summary of AI applications for assessing power system adequacy. The applications of AI methods, including genetic algorithms, particle swarm optimization, ant colony optimization, neural networks, and support vector machines, in power system adequacy evaluation are investigated. The transformation of traditional adequacy evaluation frameworks into novel AI-based frameworks is discussed, highlighting the contribution of AI. Further, several case studies demonstrate the enhanced computational efficiency of AI-based adequacy evaluation approaches compared to conventional methods.