Development of Component and System Models for a Novel Residential Vapor-Compression System Integrating Water-Based Thermal Energy Storage and Incorporating Both Rule-Based and Model Predictive Control Options

Abstract

This work aims to model a novel vapor-compression system (VCS) integrating water-based thermal energy storage (TES) for residential space heating and cooling. The model will be used in studying and evaluating the proposed system’s potential in reducing electricity cost, minimizing carbon emissions, and maximizing wind energy utilization. Also, the model will enable studying control schemes and the impact of the future scenarios for electricity generation mix profiles and utility rate structures.

Initially, the work reviews existing technology for VCS integrating water-based TES. This review helps in understanding the current landscape and informs the development of the proposed system. Following this, the potential of water-based TES in shifting space heating and cooling loads is examined through a preliminary study. The study’s findings highlight the promising capability of water-based TES in load shifting, with the TES’s footprint area being at most 4.5% of a typical single-family house’s footprint area.

To enhance connecting TES with VCS, a novel three-fluid fin-and-tube heat exchanger (Tri-Coil™) has been proposed for integration with a ducted split system (DSS). TriCoil™ has been modeled, simulated, and experimentally validated. Simulation results are promising for TriCoil™ as an alternative to a conventional A-shape heat exchanger for indoor units considering the coil volume-to-capacity ratio.

Building upon the TriCoil™ development, a system simulation suite has been introduced for VCS that integrates TES using TriCoil™. This suite incorporates VCS, TES, and rule-based control models, considering utility rates, building load profiles, and weather data. A parametric study within this suite further explores the impact of various factors like TES size, temperature setpoint, and charging timing and duration on system performance.

To optimize the system’s operation, a model predictive control (MPC) has been developed. This MPC manages the system’s operational modes to reduce electricity costs, minimize carbon emissions, and improve wind energy utilization by shifting electricity loads. Additionally, the results of a parametric analysis involving the MPC’s numerical solvers, TES temperature setpoints, and forecast horizons are presented. Finally, the system’s potential is demonstrated through a case study of a single-family house located in Stillwater, OK.