Dependable multi-population differential evolutionary particle swarm optimization for optimal operational planning of energy plants
Norihiro Nishimura, Yoshikazu Fukuyama, Tetsuro Matsui · 2017
This paper proposes dependable multi-population differential evolutionary particle swarm optimization (DEEPSO) for optimal operational planning of energy plants. The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP). Optimal operational planning of numbers of energy plants are calculated simultaneously in a data center. Therefore, the problem is required to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for this challenge is speeding up by parallel and distributed processing (PDP). However, PDP utilizes numbers of processes and countermeasures for various faults of the processes should be considered. The problem requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality are required even if some of the calculation results cannot be returned from distributed processes. The results indicate that the proposed method can improve solution quality compared with the conventional parallel DEEPSO based method using a master-slave model even if some of the calculation results cannot be returned from distributed processes.