The Utilization of Quantum Inspired Computational Intelligent in Power Systems Optimization

Abstract

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based intelligence can be an efficient alternative. This need is highlighted by technology advancements and bulk integration of the renewable energies in power grids. The Quantum inspired computational intelligence (QCI) techniques as a young discipline in computational intelligence field of research shows a promising future in optimization problems. The Quantum inspired computational intelligence (QCI) is known to effectively solve large-scale nonlinear optimization problems. This chapter will present a detailed overview of the quantum inspired computational intelligence and its variants in power systems optimization. Also, it provides a survey on the power system applications that have benefited from the powerful QCI as an optimization technique. For each application, technical details that are required for applying QCI, and the most efficient fitness functions are also discussed. In this chapter the definition, categorization and motivation for QCI employment in power systems will be elaborated. The major challenges and hinders for implementation will be discussed. The significance of this study is to present an overview on the applications of QCI in solving various power system problems in electrical engineering, which may be a useful resource for researchers to understand the state of art in QCI application in electric power systems to enable them for further explores. Current chapter will present the generalized introduction to the power systems optimization, the areas and categories and how their results and targets can be affected by optimization variables. Furthermore, the QCI methods for the power systems optimization will be presented.