Multi-area economic dispatch in bulk system using self-learning Cuckoo search algorithm

This paper proposes the Self-learning Cuckoo search algorithm to solve Multi-Area Economic Dispatch problems. The main objective of multi-area economic dispatch is to minimize the total fuel cost while satisfying balanced-power constraint in each area and limitations of generators and transmission lines. In addition, the proposed method is an improvement of the Cuckoo search algorithm with a new strategy to enhance Cuckoo eggs. The Cuckoo eggs will learn together to give the better solutions. The proposed method has been evaluated on two case studies of MAED to investigate the efficiency. Numerical results show that the proposed method is better than the conventional Cuckoo search algorithm and other methods in literature. However, in large-scale system, the computational time is slower than other methods.