Multi-objective Artificial Bee Colony Algorithm for Long-term Scheduling of Hydropower System: A Case Study of China

The multi-objective long-term economic dispatch in hydropower system is a complicated nonlinear optimization problem with a group of complex constraints which makes the optimization of conflict objectives through traditional methods a hard task. This paper is aim to present a novel multi-objective evolutionary algorithm named multiobjective artificial bee colony (MOABC) algorithm and compares the efficiency of MOABC and established algorithms in long-term cascaded hydropower system dispatch. The introduced modified employed phase improves the global optimal capability of MOABC algorithm, and a novel probability calculation method is employed to improve the search ability of onlooker bee phase. Moreover, a modified employed bees phase contributes to escape local extreme value. Additionally, a local search method based on chaos theory has been introduced. The update strategy of external archive set has been introduced. The performance of proposed MOABC has been demonstrated through a set of standard test functions. In order to verify the effectiveness of proposed algorithm further, the case of the world biggest hydropower system, Three Gorges Project (TGP), has been studied in this paper. Numerical results and comparisons demonstrate the effectiveness and efficiency of proposed algorithms which applying in the long-term scheduling of TGP hydropower systems in China. The results showed the proposed method have a better convergence ability and distribution of the Pareto front.