PNNL

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 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 chiller plant. The control co-design optimization determines the optimal number/configuration and size of the chillers, and optimizes the chiller sequencing control variables to minimize the overall energy consumption, peak-load, operating, and capital costs incurred over a design horizon, subject to cooling load constraints. The optimization is formulated as a mixed-integer programming (MIP) model and solved using BO that leverages a high-fidelity commercial chiller plant emulator to evaluate different candidate designs. We also conducted a detailed economic assessment and numerical study. Compared to current design practice, the co-optimization-based 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. These significant benefits are attributable to the chillers' optimized sizing and operation (switching thresholds, chiller staging).