LAUSR

Abstract The current HVAC design practice is sequential or iterative at best. This conservative approach does not consider the increased potential enabled by advanced sensing and control technologies that may be installed at a later date. Selection and optimization of the building control system is mostly an independent step in a sequential design process. The optimal design and operation of HVAC systems must account for the interconnected controls between the subsystems. In this paper, we develop a data-driven, simulation-based, black-box optimization approach based on Bayesian optimization (BO) to efficiently explore the design space and jointly optimize both the system and control-design parameters of a commercial building chiller plant. The co-design optimization determines the optimal number/configuration and size of the chillers, and the optimal chiller sequencing control variables that minimize the overall energy consumption, peak-load, operating, and capital costs incurred over a design horizon, subject to cooling load constraints. The problem is formulated as a mixed-integer programming model and solved using BO that leverages a high-fidelity commercial chiller plant emulator to evaluate different candidate designs. Based on real chiller data, we conducted a detailed economic assessment and numerical study that highlighted that, compared to current design practices, the co-optimization approach resulted in capital cost savings of about 0.7 million US dollars, annual energy savings of nearly 33%, and a 56% reduction in peak power demand while delivering the same level of cooling to the building due to the chiller’s optimized sizing and operation (switching thresholds, chiller staging).