Search

The current testing and rating procedure for residential air conditioners and heat pumps is based on a steady-state performance measurement approach with a degradation coefficient to account for cycling losses at part-load conditions. Test equipment performance is measured under various ambient conditions with varying compressor and fan speeds, and the results are propagated through a temperature-bin method to estimate seasonal performance. Although the current rating approach offers a standardized performance metric for comparing the relative performances of different equipment, it involves disabling the native controls and, as a result, does not consider the impact of integrated controls for test units and their dynamic interactions with representative building loads. As an alternative, a load-based testing methodology (CSA EXP07) has been developed in which the dynamic performance of equipment is measured in a test facility by allowing it to respond to a simulated virtual building model. This study compares the steady-state and dynamic load-based performance measurement methodologies for use on a 5-ton residential heat pump system in order to understand the differences and their significance for the next-generation rating procedure. The differences in the two test methodologies' performance evaluation results are discussed with a causal analysis of the observed differences. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of air conditioners and heat pumps is crucial for reducing the effects of continuously rising energy demands for space conditioning. One of the effective and tested approaches for achieving this goal has been to set energy efficiency benchmarks based on minimum energy performance standards which drive technological innovation and implementation in the market. For heat pumps, a testing and rating procedure that estimates equipment seasonal performance from laboratory measurements forms the technical basis for these standards. However, with current residential heat pumps rating standards, significant differences have been observed between efficiency measured in the lab and actual operational performance in field applications. One of the reasons for this is that with current rating approaches, the test unit native controls are overridden when measuring performance in the laboratory. As an alternative, a load-based testing approach has been developed that captures dynamic interactions of the integrated controls, equipment and building with the goal of better-representing field application conditions. In this paper, a load-based testing approach previously developed for air-conditioners is extended for heat–pump heating-mode and demonstrated for a variable-speed system. Climate-specific heating seasonal performance ratings were determined by propagating the test results through a temperature-bin method. [ABSTRACT FROM AUTHOR]

This paper presents a near-optimal control algorithm for minimizing energy costs in hybrid cooling plants in response to cooling loads. The approach is based upon an approximate solution to the partial differential equations involved in minimizing the cost of energy consumed by the plant with respect to two control variables: cooling tower airflow and condenser water flow. The parameters of the algorithm can be determined with design information of the chillers and cooling towers, along with some measurements of total condenser water flow and pump power consumption. In addition to reducing plant operating costs, the algorithm simplifies tower control and is more stable compared with conventional tower control strategies such as constant condenser water supply temperature or constant approach to wet-bulb. A large-scale cooling plant model was utilized to evaluate the performance of the approach compared with optimal control and two heuristic control strategies over six months. The difference between the energy costs associated with the near-optimal control approach and optimal control was 1.7%. Comparison with the heuristic control strategies shows that significant energy savings can be achieved with the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the application of a multi-agent control methodology to large chiller plants. The approach, originally conceived to automate controller design and reduce engineering costs in building energy systems, consisted of a multi-agent simulation framework with distributed consensus-based optimization algorithms. To adapt the approach to this scenario, agents representing physical components of a cooling plant were developed and incorporated in the framework along with two alternative optimization methods: centralized, parallel optimization with a genetic algorithm (GA), and a combination of the GA with a quasi-newton method to handle non-linear equality constraints associated to physical component behavior. An existing cooling plant was utilized as case study to simulate the performance of the methods under different operating conditions. The results demonstrated the difficulty of the consensus-based algorithms to find optimal solutions. The GAs, on the other hand, showed that significant energy savings can be achieved through the implementation of multi-agent control with algorithms capable of handling non-convex objective functions and a combination of discrete and continuous variables, which are characteristic of central cooling systems. [ABSTRACT FROM AUTHOR]

Building audits are conducted in many commercial buildings to identify opportunities to reduce energy costs and improve building operation. Because audits require significant effort by building engineers, they are usually only affordable for larger commercial buildings. "No-touch" building audit tools have thus been developed to identify potential savings based on a simplified analysis of building energy consumption patterns via high-level energy data such as monthly utility bills. This paper presents a comprehensive and standardized methodology to evaluate the accuracy of no-touch audit tools in detecting and diagnosing building energy problems and quantifying potential energy savings. The test suite is based on output data from a well-characterized set of building energy models, and the methodology is illustrated by applying it to a representative no-touch building audit tool. Results show that the tool estimates building energy end uses with reasonable accuracy but is less accurate in identifying probably causes of high energy. [ABSTRACT FROM AUTHOR]

Buildings are responsible for more than 73% of the total electricity usage in the United States and have a significant contribution to peak electrical demand of the power grid. Thus, proper building demand management could improve the robustness and efficiency of grid operation and could also result in economic benefits for building owners. Almost half of the building electricity usage occurs in the commercial building sector where 60% of the installed cooling capacity is attributed to small commercial buildings employing packaged air-conditioning systems (e.g., rooftop units). Thus, understanding the demand response potential and economic benefits for the small commercial sector is of great importance. In particular, the effect of climate, building type, and utility structure on demand response potential is valuable information for building managers in identifying the most beneficial demand response strategies and for utilities in developing better utility structure rate incentives to achieve demand reduction. This article presents a simulation-based assessment approach and highlights the key assessment results for three demand response strategies—thermostat, shade, and lighting controls—across a range of building types, vintages, locations, and utility rate structures. The control performance is evaluated in terms of utility cost savings, peak demand reductions, and indoor comfort impacts in comparison to a baseline night setup strategy. The tradeoff between cost savings and comfort is also explicitly investigated. The assessment results show that the demand response controls lead to slightly worse indoor comfort compared to the baseline control, with an increase in the median percentage of people dissatisfied from 5.7% to 8.1%. However, significant demand reductions and cost savings could be achieved across all tested cases with the magnitudes dependent on the weather condition and building type. Annual electricity cost savings of 5 to 25 cents/ft2 (54 to 269 cents/m2) were demonstrated for time-of-use energy rates with demand charges, whereas the savings were relatively small, below 5 cents/ft2 (54 cents/m2) for most cases, when buildings subscribed to flat rates without demand charges. [ABSTRACT FROM AUTHOR]

A load-based methodology for testing and rating the performance of unitary air conditioning equipment has been developed and demonstrated using a laboratory psychrometric chamber test facility. The methodology simulates representative sensible and latent loads of a commercial building as well as the thermal and latent capacitance of buildings using psychrometric chamber test facilities. The advantage of the proposed test methodology is that there is no requirement to disable the native control systems designed by equipment manufacturers. This results in more realistic representations of the installed performance of equipment that include interaction of their embedded controls with the building. The load-based control approach is described and applied to the control system of an existing psychrometric chamber test facility. Results using the developed test methodology collected from fixed-speed and variable-speed RTUs show significant differences between actual part-load performance and steady-state performance at equivalent ambient operating conditions. A test procedure that includes interaction of the equipment controls with a virtual building could enable future equipment ratings that are both climate and building-type specific. This would not be possible for the current testing approaches that override native controls with no building interaction. [ABSTRACT FROM AUTHOR]

This article introduces a simple empirical model that can be readily implemented in building simulation programs to model the effects of refrigerant charge on air conditioner performance. Under- or over-charged refrigerant faults are relatively common and impact performance of equipment. It is important to understand their overall impacts in the context of whole building energy consumption and comfort in order to prioritize their importance relative to other building issues. This article develops an empirical model using some existing data. The model applies corrections to the rated values of cooling capacity, energy input ratio, and sensible heat ratio of existing air-conditioning equipment modeling approaches. Model predictions are compared with experimental results obtained from three air conditioners that have fixed orifices as expansion devices. The accuracy of the model is compared with a more complex empirical model and is shown to provide better accuracy for predicting cooling capacity and sensible heat ratio. [ABSTRACT FROM PUBLISHER]

This article presents a general approach for determining maximum monthly energy cost savings associated with optimal supervisory control for cooling in commercial buildings in the presence of utility rates that include both demand and time-of-use energy charges. The resulting tool has a month-long time horizon because of the nature of demand changes and is only useful for benchmarking the performance of simpler and shorter-term demand response and optimal control approaches. Attempts to solve this optimization problem using a centralized formulation failed and, therefore, the benchmarking problem was formulated as a dynamic optimization problem within a multi-agent control framework so that the monthly optimization problem is segmented into several sub-problems where each sub-problem involves system optimization for a shorter period of time, for example, a 1-day period. The daily-scale optimization involves determination of trajectories of zone set-point temperatures that minimize an integral cost function with a demand cost constraint determined by a demand agent. In order to further simplify the daily optimization, dynamics associated with the cooling system are neglected and optimal control of the cooling system is assumed to be based on heuristics determined through upfront analysis. This approach leads to significantly reduced computational requirements and more importantly, it provides guaranteed convergence in the multi-agent optimization. Results for a single-zone building case study are presented to illustrate the potential cost savings. In addition, a simpler and more practical short-term optimization approach with a demand-limiting heuristic is proposed and evaluated in comparison to the benchmarking optimization results for this case study and achieves most of the potential savings. [ABSTRACT FROM AUTHOR]

Metal hydrides have been investigated for use in environmentally friendly heat pumps. These systems operate using reversible adsorption and desorption of hydrogen from metallic compounds and can be incorporated in a cycle having a work input (compressor) or thermal energy input (generator). Some challenges faced by compressor driven metal hydride heat pumps are poor heat transfer in the metal hydride beds and high compressor discharge temperatures. To overcome these challenges, this article investigates the use of a metal-hydride slurry in conjunction with various isothermal compression techniques. Liquid-flooded, electrochemical, and liquid piston compressors were modeled and integrated into a system model in order to assess their impact on the performance of the slurry-based metal hydride heat pump system. [ABSTRACT FROM PUBLISHER]

In this article, virtual sensors for air-side diagnostics are developed and validated for rooftop air conditioners. The virtual air-side sensors require indoor fan differential pressure and speed measurements. Experimental data from laboratory measurements are used to evaluate the accuracy of the virtual sensors. Methodologies for improving the accuracy of air-side temperature sensors often used for outdoor-air economizer diagnostics have also been developed and evaluated. Outdoor-air temperature correction correlations improve single-point temperature inaccuracy caused by exhaust-air recirculation. A model correcting a single-point mixed-air temperature measurement for inaccuracy caused by thermal stratification and non-uniform velocity distributions at the evaporator inlet has also been described and validated. Finally, methods for estimating outdoor ventilation air fraction are presented and compared to measurements. [ABSTRACT FROM PUBLISHER]

This article presents a generalized control heuristic to reset supply air temperature set-points for direct-expansion units with capacity modulation and variable airflow and integrates the heuristic into a simplified model predictive control framework that precools building thermal mass through zone temperature set-point adjustments. The resulting control methodology is appropriate for control of medium-sized commercial buildings that employ variable air volume air distribution with direct-expansion units for primary cooling. The heuristic is shown to work well for different direct-expansion systems with different compressor types and fan–duct combinations. To assess the integrated energy savings potential of the heuristic strategy and simplified model predictive control, a simulation platform was developed for a medium-sized commercial building. Simulations were carried out for a 100-day cooling season in different U.S. locations and under different control strategies. The heuristic control provided energy savings of between 5% and 10% relative to conventional control, depending on the system type and location. Approximately 4% additional energy savings was achieved when the heuristic was integrated into a simplified model predictive control having only moderate computational requirements. The cost savings could be significantly greater if time-of-use and demand charges were considered. [ABSTRACT FROM PUBLISHER]

While buildings served by multiple rooftop units are widely prevalent in retail and food industries, optimal coordination among rooftop units has received little attention. Currently, most buildings utilize plain feedback or heuristic strategies to determine rooftop unit operations. This article presents a switched dynamic programming based approach for optimal scheduling of rooftop unit operations. Using a mid-sized restaurant served by multiple rooftop units as an example, a switched affine dynamic system model is first presented. The rooftop unit coordination control problem is then formulated as a switched optimal control problem with nontrivial switching cost. A dynamic programming based method is proposed for the solution of the switched optimal control problem, which has reasonable computational complexity and can be implemented either as a one-shot solution or as part of a model predictive control algorithm. To mitigate the growing computational complexity of the proposed algorithm, the approach is extended by proposing a distributed extension of the model predictive control formulation. Finally, a simulation based evaluation of the various formulations is conducted, and the salient features of the formulation are highlighted. [ABSTRACT FROM PUBLISHER]