Binary whale optimization algorithm for unit commitment problem in power system operation

Abstract

This chapter discusses the development and application of an intelligent computational technique called binary whale optimization algorithm (BWOA) and its application to solve the unit commitment (UC) problem. The whale optimization is a heuristic approach that mimics the intelligence associated with hunting and feeding behaviour of whales. The two distinct properties of location updates of whales, namely shrinking approach and spiral update approach are used for optimizing the position of prey. To improvise the real-valued whale optimization algorithm for binary UC problem, update process of whale position is mapped to binary search space using various transfer functions. The binary variants include three sigmoidal transformations and two tangent hyperbolic transformations. The binary variants presented are evaluated using extensive numerical experiments on various test systems and operating conditions. The simulation results are presented and compared to various existing classical/traditional, heuristic and meta-heuristic approaches. In addition, the statistical significance of proposed BWOA approaches among other binary approaches and within themselves is verified using a series of standard statistical tests. The same demonstrates the effectiveness of proposed BWOA to solve UC problem of small, medium and large scale.