Economic power generation scheduling exploiting hill-climbed Sine–Cosine​ algorithm

Abstract

This paper recommends a stochastic population-based optimization procedure named the hill-climbed Sine–Cosine algorithm (HcSCA) to solve the economic generation scheduling (EcGS) of thermal units. The sine–cosine algorithm creates a set of preliminary random individuals and needs them to change away from or near to the destination to create a new population-based on functions of sine and cosine. The sine–cosine algorithm finds the solution quickly while balancing exploration and exploitation but stagnates to a sub-optimal solution. To avoid stagnation, the sine–cosine optimization procedure coordinates local search and hill-climbing heuristics. The specific features i.e. valve-point loading effect, ramp-rate limits, generator limits for generation, prohibited operating zones, and power demand constraints with losses are assumed to resolve power economic thermal generation scheduling. The success of the recommended method is observed on small- and large-scale power test systems. The outcome of the recommended algorithm is matched with erstwhile algorithms. From the experimental results, it is experienced that HcSCA provides a competing solution to economic thermal generation scheduling problems.