Multi‐objective unit commitment using search space‐based crazy particle swarm optimisation and normal boundary intersection technique

In this study, a multi‐objective unit commitment problem is formulated and solved considering the profit maximisation and emission minimisation. To find the optimal schedule of the generation units, search space‐based crazy particle swarm optimisation is proposed. A search space is the combination of binary states for unit ON(1)/OFF(0) status, which is used for the movement of the particles to maintain good exploration and exploitation search capabilities. To solve the problem and generate the non‐inferior solutions, normal boundary intersection (NBI) method is applied. The main advantage of the NBI technique is to provide a set of uniformly distributed non‐dominated solutions regardless of the scales of objective function values. To select a non‐inferior solution a fuzzy‐based decision making approach is employed. The effectiveness of the proposed method has been tested on the large‐scale power system.