Multi-objective genetic algorithm optimized for energy consumption and cost in building energy system design

Abstract

Distributed energy systems based on cogeneration systems afford excellent energy saving potential through the effective use of waste heat from power generators. However, unless an appropriate combination of machinery and operation are used, the expected performance is not achieved, but it is quite difficult to determine the optimal combination of machinery and operation. The authors had already developed and proposed a new optimal design method for building energy systems or distributed energy systems using genetic algorithms (GA) in some previous studies (e.g. Ooka, R. et al., 2008). GAs are able to handle nonlinear optimization problems. The proposed method designs the most efficient energy system by optimizing operation of available systems with consideration for the optimal machinery capacity in the systems. However, it is only intended to optimize primary energy consumption. In practical applications, it is necessary that the method is able to search for optimal energy systems based on various objectives, such as environmental impact factors, economic factors, building structure factors, and so on. Therefore, the method was improved in this study to enable examination of energy systems with various objectives using Multi-Objective Genetic Algorithms (MOGA). This study developed the optimal design method for energy systems in a single building as the first step, with the aim of establishing the optimal design method for a distributed energy system. A case study involving a hospital building was carried out to examine the application potential of the method as the optimal design tool.