Dependability evaluation of parallel differential evolutionary particle swarm optimization for on-line optimal operational planning of energy plants

This paper evaluates speed-up and dependability of parallel differential evolutionary particle swarm optimization (DEEPSO) for on-line optimal operational planning of energy plants. The planning can be formulated as a mixed integer nonlinear optimization problem (MINLP). When optimal operational planning of numbers of energy plants are calculated simultaneously in a data center, it 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. On-line optimal operational planning requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping quality of solutions are required even if some of the calculation results cannot be returned from distributed processes. Using the proposed parallel DEEPSO based method, it is observed that calculation time becomes about 3 times faster than a sequential calculation, and high quality of solutions can be kept even with high fault probabilities.