Apply three metaheuristic algorithms for energy storage-based integrated power system to reduce generation cost

Abstract

This research applies new computing methods to optimize the operation of a typical hydrothermal system for one day. The system consists of one thermal power plant (TPP) and one pumped storage hydropower plant (PSHP). The main target of the research is to determine the amount of water that must be discharged or pumped back to the reservoir to reduce the total electricity production cost (TEPC) of TTP. The volumes of water storage in the reservoir at the beginning and end points of the schedule must be the same. Three meta-heuristic algorithms are applied, including Coot optimizer (COOT), aquila optimizer (AO), and particle swarm optimizations (PSO) in which COOT and AO were proposed at early 2021. The results show that the effectiveness of COOT is better than AO, PSO and several methods in previous studies. Hence, COOT is considered a powerful computing tool for the problem.