Two-Objective Genetic Algorithm Optimization of Chilled Water Plant Design

Chilled water central plant accounts a large portion of total energy use and cost in building. The chilled water central plant design will have a significant impact on this energy cost. This paper proposes a multiple objective design optimization method for optimal design of chilled water central plants. The method integrates whole system models with multi-objective genetic algorithm optimization solver to minimize the annual energy cost, initial cost, the life cycle cost, or any combination of those costs. The design variables considered are chilled water and condenser water piping diameters, chilled water supply temperature, and condenser and chilled water temperature differences. The proposed approach combines cooling load analysis and head and energy calculations integrated with whole chilled water plant model. The pump head calculations including piping, all fittings, valves, and devices are achieved by developed chilled and condenser water flow model. The energy calculations are done by using generic chiller, fan, and pump models. The method is tested on an existing three-story, eighty-eight thousand square foot building. The annual energy cost vs. initial cost, and initial cost vs. life cycle cost were selected as two objective functions to be solved by two-objective GA optimization algorithm to obtain a set of solutions for better design decisions. A whole building energy simulation model is used to generate the hourly cooling loads and then the optimal design variables are found to minimize the two objective functions. The testing results show this approach will achieve better results than rules-of-thumb or traditional design procedures. The life cycle cost saving could be up to 8% depending on project specifications and locations.