Dependable multi-population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

Abstract

This paper proposes dependable multi-population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed-integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi-population DEEPSO (MP-DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi-population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.