Novel Self-adaptive Genetic Algorithm for Solving AC Security Constrained Short-term Hydrothermal Scheduling

Abstract

In this paper, a novel Self-Adaptive Genetic Algorithm (SAGA) is proposed to solve the AC Security Constrained Short-term Hydrothermal Scheduling (SCSHTS) problem. The efficiency of the proposed algorithm is tested on the benchmark version of the IEEE 30 BUS system, which consists of four thermal power plants and two hydropower plants, as well as an optimization period of two intervals. The new proposed algorithm is then applied to a standard IEEE 30 BUS system, taking into account all necessary system and security constraints. The results obtained from the new proposed SAGA are compared with those obtained from some metaheuristic methods, such as Conventional Cuckoo Search Algorithm (CCSA), Effective Novel Cuckoo Search Algorithm (ENCSA), and Modified Cuckoo Search Algorithm (MCSA). The simulation results indicate that the proposed SAGA provides better solutions than the other optimization methods that have been applied to this optimization problem.