Total optimization of smart community using sequence-based deterministic initialization and k-means based initial searching points generation

This paper proposes total optimization of smart community (SC) using sequence-based deterministic initialization and k-means based initial searching points generation. In this paper, energy supply models such as electric power utility, natural gas utility, drinking water plant, and waste water treatment plant, and energy consumption models such as industry, building, residence, and railroad are utilized. Using the SC model, energy costs, actual electric power at peak load hours, and the amount of CO2 emission of the whole SC is minimized. Differential Evolutionary Particle Swarm Optimization (DEEPSO) is applied as the optimization technique with the proposed initial searching points generation method based on the sequence-based deterministic initialization and k-means. The proposed method is applied to a model of Toyama city, which is a moderately-sized city in Japan. Optimal operation by the proposed method is compared with that by an initial searching points generation method using pseudo-random number generator (PRNG) and the proposed method.