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Space heating is one of the primary components of residential energy usage in the U.S., accounting for nearly 43% (EIA, 2015) of the total residential energy consumption. To reduce this energy usage, heat-pumps provide an energy-efficient alternative to currently prevalent systems such as electric heaters and gas furnaces. Advanced control strategies have the potential to further improve heat-pump system energy efficiency and comfort delivery. In recent years, advancements in the microprocessor field have made it possible to widely implement advanced energy-efficient controls within heat-pump systems. However, still only a very small fraction of residential air-conditioners and heat-pumps currently sold in the U.S. market utilize these next-generation controls (ACEEE, 2019). To facilitate an acceleration in the development and implementation of advanced control architectures within heat-pump equipment, a load-based testing methodology can be utilized. Load-based testing allows realistic dynamic behavior and performance evaluation of energy efficiency and comfort delivery for heat pumping and air conditioning equipment with embedded controls in a laboratory setting. In the load-based testing methodology, the sensible and latent loads of a representative residential building are emulated in the indoor psychrometric test room by dynamically varying the test room conditions utilizing a virtual building model. The test equipment responds dynamically to this virtual building with its embedded controls based on the thermostat sensing response. This enables engineers to evaluate the performance of a heat-pump in a controlled setting under dynamic conditions that are similar to a field application but with a significant reduction in testing time and cost. This paper demonstrates the application of load-based testing for evaluating the performance of a 5-ton split-type residential heat-pump with its integrated controls in a heating mode application. Furthermore, the effect of equipment oversizing and undersizing on the heat-pump energy consumption and comfort delivery are also presented.

A computationally efficient and accurate modeling approach is critically important for designing and evaluating controls and fault detection and diagnosis (FDD) algorithms. This paper proposes a reduced order modeling approach for vapor compression cycles (VCC) that involves application of nonlinear model order reduction (MOR) methods to dynamic heat exchanger (HX) models to generate reduced order HX models. A reformulated finite volume HX model was first developed that matches the baseline MOR model structure. Then, a nonlinear MOR framework based on Proper Orthogonal Decomposition (POD) and a Discrete Empirical Interpolation Method (DEIM) was developed for generating nonlinear reduced order HX models. The proposed approach was implemented within a comprehensive VCC model. Reduced order HX models were constructed for a centrifugal chiller system and coupled to quasistatic models of a compressor and expansion valve to complete the reduced order VCC model. The reduced cycle model was implemented within the Modelicabased platform and used to predict loadchange transients over a wide range of operating conditions for comparison with measurements. The proposed reduced order modeling approach is computationally efficient and accurately captures cycle dynamics.

Vapor compression cycles would have many applications in the space industry if it was not for the uncertainty imposed by microgravity environments on two-phase systems. A first step towards Zero-G for technologies involving fluid dynamics can be terrestrial testing at different orientations. For vapor compression cycles, there is very little literature describing this type of research. This paper describes the anomalies encountered during the pursuit of a continuous operation of a R134a vapor compression cycle while positioning it at fixed angles around one axis between 0 and 360°. Experimental data was collected on a dedicated test stand across two configurations, one using a flat-plate evaporator and the other configuration using a tube-in-tube evaporator. Liquid flooding of the suction line was observed for both configurations but also continuous operation throughout a complete loop for certain cycle conditions. Charge migration towards the evaporator when it was put at the bottom was calculated based on differing measurements of the two mass flow meters in the liquid and suction line.

Space cooling and heating equipment account for nearly 32% of the total residential electricity consumption in the U.S. In the residential space conditioning equipment market, air-conditioning and heat-pumping systems are prevalent, so even a slight improvement in these system efficiencies can have a significant impact on reducing the overall energy consumption. Over the years, the energy efficiency benchmarks established by the U.S. Department of Energy have been successful in encouraging manufacturers to develop higher efficiency equipment. These benchmarks are based on an energy efficiency standard, and these standards are based on a rating test procedure that forms the technical basis. Currently, in the U.S., AHRI 210/240 is the rating procedure for residential air-conditioning and heat-pumping equipment, which is based on a steady-state performance measurement method with a degradation coefficient to account for the cycling losses in part-load conditions. Although it provides a standard metric to compare different equipment performances, there has been a debate that this current methodology fails to appropriately characterize the performance of systems with variable-speed compressors and advanced control design. This is largely attributed to the steady-state nature of this current testing approach, which also involves overriding the equipment native control. In contrast to this, a load-based testing methodology has been developed in which the equipment responds to a simulated virtual building load, and the system dynamic performance is measured with its integrated controls. The load-based testing methodology is described in detail by Hjortland and Braun (2019), Patil et al. (2018), and Cheng et al. (2021), which forms the basis for CSA standard draft EXP07:2019 (CSA, 2019). In this paper, these two performance measurement methodologies, steady-state and dynamic load-based, are compared for application to a 5ton residential heat-pump system. The equipment performance was measured in cooling mode and the seasonal performance estimates based on the two testing approaches are compared. The differences in the two test methodologies' performance evaluation results are discussed with a causal analysis of the observed differences.

Buildings account for 39% of the CO2 emissions in the U.S. Although emerging building energy-efficient technologies lead to improved energy efficiency and lower environmental impact in the operational phase, not much attention has been paid to the environmental impact associated with the materials and manufacturing of the building mechanical systems. This paper presents an integrated life-cycle assessment method for buildings that accounts for the embodied and use phase carbon impacts of the building and its mechanical systems. The proposed methodology relies on EnergyPlus to generate the use-phase energy consumption for any given building. A material 'grabber' routine was developed to automatically extract building envelope material information from EnergyPlus models, which is then used for envelope embodied carbon analysis. For the mechanical equipment, embodied carbon accounting was performed for two representative air-conditioning and heat pump units: a 4-ton packaged unit and a 4-ton split heat pump. The different elements were incorporated in the overall life-cycle assessment tool along with lighting embodied data to allow generation of a whole building environmental performance report. Five Department of Energy commercial building prototypes were used as case studies and analysis results for seven U.S. climate locations are presented in this paper. The results show that for the investigated prototypical buildings, the use phase energy consumption has a dominant impact on the overall building environmental performance: the embodied carbon contribution is less than 9% for all considered cases. However, the tradeoffs could change dramatically as the U.S. moves towards net-zero buildings and the tool presented in this paper could be used to consider these tradeoffs.

Vapor compression cycles (VCCs) are a promising technology for refrigeration needs on future space craft due to their generally high cooling COP. However, due to microgravity there lies a risk of liquid flooding the compressor during start-up. Thus, to better prepare VCCs for microgravity applications, it is significant to understand the dependence of two-phase refrigerant on gravity during start-up. In this work, liquid flooding is evaluated at the start-up of a VCC and a possibility for passive compressor protection is considered. The experimental setup has two configurations. In the first, two-phase phenomena can be observed in a transparent tube and different tube insertions can be tested for their effectiveness as a liquid flooding obstruction. In the second configuration, liquid flooding from a commercial evaporator can be evaluated for different charge levels. The results show a clear effect of tube insertions on liquid flooding in a straight tube and find the felt tube insertion to be most effective at impeding flow. The evaporator test results also present a strong correlation of liquid flooding parameters with the charge level and show only a small dependence on the orientation of the evaporator.

Real-time power supply and demand balances are critical to ensure stable power frequency and quality power services. However, the growing integration of renewable energy increasingly challenges the power infrastructure because most of the renewable resources, e.g., solar and wind energy, are intermittent and difficult to predict. To meet the stringent power frequency requirements, more fast reacting frequency regulation resources are being brought online among which grid-level batteries are the dominant ones. Although batteries can provide fast and high quality regulation services, they suffer from high initial cost, high environmental impact and round-trip efficiency loss. When providing fast frequency regulation services, battery life span could be significantly reduced, leading to even higher cost per unit of frequency regulation service. Buildings consume more than 73% of the electricity in the US, offering significant regulation reserves for the power grid. Variable-speed air-conditioning systems are taking an increasing share of the market due to the higher efficiency requirements imposed by federal agencies. In addition to efficiency benefits, variable-speed cooling/heating systems are also perfectly suited for providing ancillary services as these units can modulate their power continuously over a wide range. Compared to batteries, HVAC systems have several advantages when providing frequency regulation services: 1) in theory, they do not incur any round-trip efficiency loss; 2) the time response of an AC unit could be faster than a battery (especially compared to energy batteries with slower ramping rates and relatively larger capacities); 3) the existing regulation capacity of HVAC systems is huge and the implementation cost is much lower; 4) the environmental impact of using HVAC systems for regulation services is lower than for battery systems. This paper presents lab test results of a variable-speed heat pump for providing ancillary services. Regulation performances for both the traditional (slow) and dynamic (fast) regulation services are reported. The tested performance scores were above 0.97 and the regulation performance even beats the average battery regulation performance. Preliminary economic analysis was also performed using historical PJM prices. It was shown that the credit received for providing ancillary services could easily offset 50% of the HVAC energy cost under the tested conditions.

Combined Cooling, Heat and Power (CCHP) systems have great potential to recover low-grade thermal energy, resulting in higher energy efficiency, reduced emission rates, lower operating costs and a higher level of energy security. In order to fully realize the benefits of CCHP systems in terms of reduced cost and carbon dioxide emissions, effective optimization and control strategies are required. This work presents an approach for optimizing the operation of the CCHP system using a detailed network energy flow model solved by genetic algorithm. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The performance of the system will be compared and evaluated with respect to economic and environmental benefits.

This paper presents the results of the evaluation of a control algorithm for minimizing energy costs in cooling plants with chilled water storage. The control approach requires very little plant information, is computationally simple, and is general in the sense that it is applicable to different types of utility rate structures including real-time pricing (RTP), time-of-use (TOU) and TOU with demand charges. The control algorithm was evaluated using a model of an existing chiller plant of significant complexity built in a simulation testbed for different combinations of storage sizes, load profiles and electricity rates. The performance of the near-optimal algorithm was compared with three different approaches: optimum control (baseline), and two heuristic control strategies commonly used for thermal energy storage. The energy costs obtained with the near-optimal control algorithm were within approximately 2% of the costs associated with optimal control. Comparison with the heuristic control strategies shows that significant energy savings can be achieved with the proposed control method.

The recent availability of variable electric energy and demand rates for residential buildings is providing incentives for the application of thermal storage for cooling that previously has been limited to commercial buildings. This is particularly relevant for hot climates where air-conditioning (A/C) use is the primary cause for peak electricity demand. Thermal storage allows consumers to store “cooling” when demand is low and minimize operation of the A/C during peak periods. From an economic perspective, the use of storage can significantly reduce operating costs depending on the utility rate incentives. In addition, storage can lead to a reduction in the installed cost of the primary cooling equipment because of a reduction in the peak equipment cooling requirement. However, this reduced equipment cost is counteracted by the additional costs required for storage and a secondary loop. This paper considers the overall economics associated with a packaged A/C integrated with ice energy storage for residential cooling applications. The evaluation was performed using a model of the proposed system that estimates system performance and operating cost over a cooling season for different locations and utility rates and using a generalized control strategy presented in a companion paper. The proposed system is compared to a conventional spilt system A/C in terms of initial cost, operating cost, and economic payback. In addition, we investigate the trade-off between equipment cooling capacity and storage size to determine minimum payback period for each situation. The optimization results show that the systems with the shortest payback period have initial costs that are very similar to the baseline system. In addition, the payback periods are attractive in locations with favorable utility rates and long cooling seasons (i.e., hot climates).

Representing multi-zone building envelope systems with linear time invariant state-space forms provides a way of applying various advanced control methodologies for better design and control of buildings. For example, system properties such as time constant and frequency response of building envelopes can be easily investigated with the help of control toolkits such as Matlab/Simulink and a reduced-order model can be developed by applying a model order reduction technique, once a reliable LTI building representation is developed. However, in order to make the LTI representation approach useful for industry, an interfacing tool that automatically extracts building system information from input files of popular building energy simulation (BES) tools and constructs a physical thermal network from the data is needed. This paper presents a conceptual strategy to interpret object (class) of a building energy simulation software and a methodology to develop a high fidelity LTI thermal network model. A case study applying this approach is provided which utilizes a model-order reduction method that converts a BES building envelope model for a multi-zone building into a reduced-order LTI model (ROM). Comparisons of predicted building load profiles and computation times between the BES model and ROM are also provided.

This paper presents results of testing variable-speed heat pumps using a new load-based testing methodology that is described in a companion paper. The testing methodology involves emulating the response of a building’s sensible and latent loads to equipment controls by dynamically adjusting the temperature and humidity setpoints of the psychrometric chamber reconditioning system using a simple building model. The advantage of this approach over existing testing approaches specified in ratings standards is that it considers the interaction of the integrated controls with the equipment. As a result, it better captures the full range of part-load operation and the benefits of improved controls. This paper presents performance results for application of the automated load-based testing methodology to different variable-speed residential heat pump systems. In order to assess the benefits of load-based testing versus existing standards, tests were also conducted based on AHRI 210/240 and seasonal performance estimates are compared using data obtained with the two testing approaches.

Compared to other types of compressors, linear compressors have drawn much attention for this application due to low friction, low power losses and compactness. Very little work was found in the open literature related to the comprehensive dynamic compressor modeling of linear compressors coupled with mechanical and electrical sub-models. This paper presents a comprehensive and generalized simulation model that is used to simulate the dynamic performance of a linear compressor. The model is based on mass and energy balance equations applied to open control volumes. The overall model is composed of several sub-models that include a piston dynamic model, an electrical motor model, a valve dynamic model, and a heat transfer model. When integrated together with an overall energy flow model for the compressor, it is possible to predict piston vibration, temperatures, and pressures within the compressor as well as overall performance. The results of the compressor model are being validated using experimental data from a commercially available linear compressor used in domestic refrigerator/freezers. The predictions by the compressor model have been compared to the measured mass flow rate, motor power and overall efficiencies. The comparisons indicate that the simulation model accurately predicts the compressor performance and can be used to study the performance of this type of compressor. The model will be used to improve the compressor design via parametric studies and to identify key parameters affecting the system transients.

A comprehensive mechanistic model of an open-drive single-screw expander for waste heat recovery applications by means of an Organic Rankine Cycle (ORC) has been previously developed and validated by the authors. The model included sub-models of geometry, leakage flows, in-chamber heat transfer, simplified friction losses and a single-lumped temperature overall energy balance. Furthermore, a non-symmetric approach was also implemented to simulate the simultaneous expansion process occurring on both side of the central rotor. In this work, the mechanistic model has been extended to compute the loads on the starwheels bearings as well as the main rotor. Forces and moments analyses allowed integration of a more detailed friction loss analysis and identification of a thermal resistance network of the housing with multiple lumped temperatures. To the best of the author’s knowledge such a detailed integrated thermal model of a single-screw machine has not been reported in the available literature. The multi-lumped temperature overall energy balance (OEB) is validated with experimental data obtained from a dedicated ORC test stand. A comparison between single-lumped and multi-lumped temperature approaches on the performance predictions is provided along with a discussion about the losses distribution inside the machine.

Positive displacement compressors are critical components in today’s vapor compression refrigeration, air conditioning, and heat pumping applications and can also be applied as expanders in power generation systems, such as organic Rankine cycles (ORC). The simulation of such systems is essential to predict and optimize the performance behavior at full- and part-load conditions. To this end, comprehensive system models are built by including different sub-models corresponding to each cycle component (e.g., heat exchangers, compressor, linesets). In general, the higher the complexity of each sub-models utilized to capture the physics, the higher the computational time required to solve a simulation run. In this work, deep learning is utilized to obtain high-accuracy performance predictions of positive displacement machines. A fixed-speed two-phase injected and vapor injected scroll compressor for air-conditioning applications and an oil-free scroll expander for low-grade waste heat recovery by means of an ORC are considered as test cases. In particular, Artificial Neural Network (ANN)-based models have been developed for each of the machines and trained using experimental data collected at the Ray W. Herrick Laboratories. The results of the training and testing of the models are presented as well as a discussion of the reliability of such models for extrapolating performance. In addition, the ANN models are compared with conventional empirical and semi-empirical modeling approaches. The models have been implemented in the Python programming language by using the open-source Keras package.