Multiobjective Long-Term Generation Scheduling of Cascade Hydroelectricity System Using a Quantum-Behaved Particle Swarm Optimization Based on Decomposition

Multiobjective long-term generation scheduling (MOLTGS) plays a vital role in coordinating the contradiction between the generation and reliability of cascade hydroelectricity system (CHS). In this paper, a multiobjective quantum-behaved particle swarm optimization based on decomposition (MOQPSO/D) is presented to solve the MOLTGS problem of maximizing total hydroelectricity generation and firm hydroelectricity output. In MOQPSO/D, the improved logistic map is adopted to initialize the population in the feasible space, where various equality and inequality constraints are handled by a constraint handling method based on conversion and repair strategies. An improved Tchebycheff decomposition with a modified generator of direction vectors, a modified QPSO operator and normal cloud mutation are the key components of MOQPSO/D in addressing the nonlinearity and complexity of MOLTGS. The feasibility and effectiveness of MOQPSO/D are verified first on numerical experiments of benchmark instances and then on real engineering examples of the Three Gorges (TG) and Gezhouba (GZB) plants within three representative years. The results show that MOQPSO/D can not only perform better robustness, convergence and diversity performance than other seven competitors but also output a group of Pareto optimal schemes within a reasonable amount of time. So, this paper provides a new effective alternative to solve the MOLTGS problem of CHS when considering both generation and reliability objectives.