Short-term hydrothermal generation scheduling using differential real-coded quantum-inspired evolutionary algorithm

Abstract

Short-term hydrothermal generation scheduling aims at determining optimal hydro and thermal generations to achieve minimum fuel cost of thermal plants for a 1 day or a 1 week while meeting various hydraulic and electric system constraints. The problem is viewed as a complex and nonlinear hard problem considering valve-points effects and transmission losses with a set of operation operational and physical constraints. This paper presents a novel effective differential real-coded quantum-inspired evolutionary algorithm (DRQEA) for solving this complicated problem. Some improvements like real-coded rule, adaptive differential mutation and crossover mechanism are proposed in DRQEA to enhance the global search ability in continuous space. Meanwhile, various constraints are handled effectively by using heuristic strategies designed by their characteristics. The effectiveness of the proposed approach is demonstrated on two hydrothermal test systems, which consist of two sub-systems: hydro sub-system and thermal sub-system. The obtained results of the proposed approach are compared with other methods, and simulation and comparison results clearly show that DRQEA is able to provide better solution than other reported methods, both in the solution quality and the convergence speed. The proposed algorithm can also apply to other dynamic optimization problem with nonlinear and non-convex characteristics in power system.