Multi-objective short-term scheduling of thermoelectric power systems using a novel multi-objective θ-improved cuckoo optimisation algorithm

This study proposes a multi-objective optimal static and dynamic scheduling of thermoelectric power systems considering the conflicting environmental and economical objectives. Meantime, some restrictions such as valve-point effects, prohibited operating zones, multi-fuel options, line flow limits as well as spinning reserve should be taken into account in order to ensure secure real-time power system operation. A novel multi-objective theta-improved cuckoo optimisation algorithm is projected to solve the optimisation problems by defining a set of nondominated points as the solutions. The suggested method moves forward the particles to the problem search space in the polar coordinates as a substitute of the Cartesian one. In addition, in order to achieve better performance and higher-convergence speed, several improvement strategies are utilised. This algorithm is equipped with a novel powerful mutation strategy in order to increase the population diversity and to amend the convergence criteria. Furthermore, a fuzzy-based clustering is used to control the size of the repository and a niching method is utilised to choose the best solution during the optimisation process and to ensure diversity among non-dominated solutions. Performance of the proposed algorithm is tested on 6-, 10-, 14-, 40- and 100-unit test systems and compared with those of other well-known methods.