Multiple-chiller Plant Research Articles

An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties

Chiller sequencing control is crucial to the reliable and energy-efficient operation of multiple-chiller plants. It should not only ensure an adequate supply of cooling capacity for buildings, but also make the chillers work efficiently. The measurement uncertainties cannot be avoided and have significant negative effects on the chiller sequencing control. To cope with the challenges and uncertainties, this study proposed an online robust sequencing control strategy using a probabilistic approach for chiller plants under low-quality and uncertain flow measurements. An uncertainty processing model of flow measurements was developed based on the Bayesian inference and Markov chain Monte Carlo methods and an energy balance model. As the core of the proposed control strategy, the uncertainty processing model can quantify the measurement uncertainties of water flow rates accurately. According to the probability analysis, an online decision-making scheme was designed, and the risks in the online decision-making processes were assessed. Compared with the conventional chiller sequencing control strategies, the proposed control strategy could reduce the impacts of flow measurement uncertainties significantly. The results of case studies showed that the root-mean-square error of cooling loads was reduced significantly by about 79%, the total switching number of chillers was reduced by up to 35.71% under the positive flow measurement uncertainties, and the cumulative unmet cooling load was reduced by up to 31.22% under the negative flow measurement uncertainties. The proposed chiller sequencing control strategy is able to tolerate flow measurement uncertainties.

A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.

Particle Swarm optimization for control operation of an all-variable speed water-cooled chiller plant

Variety of research studies have focused on developing energy-efficient control schemes for multiple-chiller plants. However, few studies have conducted optimization of the chiller plant with non-identical chillers based on dynamic energy modeling approach. A dynamic energy model with Modelica language for an all-variable speed primary-secondary water-cooled chiller plant was developed based on design submittals and operation control sequences and was calibrated using trend data. Then, Particle Swarm Optimization search algorithm was employed to find optimum value of control parameters for the chiller plant with non-identical chillers including chilled water temperature setpoint, condenser water temperature setpoint, and chiller stage sequence. The optimization results show that holistic optimization, which simultaneously optimizes all considered control variables for chiller operation, achieves the maximum savings: 10.5% of total chiller plant energy consumption on a hot day and 13.6% of total chiller plant energy consumption on a moderate day. The average daily chiller plant efficiency was improved from 0.59 kW/ton (COP of 5.96) to 0.53 kW/ton (COP of 6.64) in the hot day and from 0.70 kW/ton (COP of 5.02) to 0.59 kW/ton (COP of 5.96) in the moderate day.

Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types

Configuring the number and size of chillers in a multiple-chiller plant properly is an efficient way to improve the plant energy efficiency. At the design stage, the optimal configuration can be achieved through matching the capacity to load as closely as possible across the full-load profile. However, in spite of the fact that current literature offers practical recommendations, a systematic method to optimize the configuration of multiple-chiller plants is lacking. Due to the lack of accurate information at the design stage and only limited knowledge of the eventual realization it is hard to predict the building’s cooling load. Moreover, there is no operational data to predict the system performance. Both explain the existence of uncertainty in the HVAC plant design process. This paper, therefore, proposes a strategy to optimize the configuration of multiple-chiller plants, which takes account of the load side uncertainty as well as the COP uncertainty and selects the optimal configuration through a life-cycle analysis. Both the load side uncertainty and the COP uncertainty are quantified using statistical distributions. To facilitate applications, the distributions of the cooling load profile of different types of buildings under different weather conditions are investigated and are classified into four categories, and the optimal configuration schemes under each type of cooling load distribution are analyzed and summarized in a tabulated form.

Application of artificial neural network and genetic algorithm to the optimization of load distribution for a multiple-type-chiller plant

Numerous methods are available in various studies that investigate the optimization of central air conditioning systems. However, the majority of these studies focus on a single chiller or multiple chillers of identical capacity and type. Although the data indicated substantial energy conservation, all relevant findings were optimized under continuous concave functions, which cannot resolve the optimal load distributions of multiple-chiller plants commonly seen today due to their discontinuous properties. This study targets a large public exhibition venue, which uses a central air conditioning system consisting of heterogeneous chillers: fixed /variable frequency screw chillers, and centrifugal chillers. This system adopts a manual method (MM) for controlling the on/off operation of chillers; after an extended period of monitoring and collecting data, the operational characteristics of each chiller are better understood. This study uses the neural network (NN) to establish a chiller power consumption model, which considers the operating constrains of various types of chillers to ensure stable operation of the chiller plant. Genetic algorithm (GA) is also integrated while satisfying cooling load conditions to optimize chiller loading and determine the minimum power consumption of chiller plant. This is achieved by overcoming the deficiency of the traditional method, which fails to converge at low loads, and liberating from the restrictions limited by discontinuous functions. The results show that integrating NN and GA (NNGA) between 95% and 55% cooling loads improved power savings compared to MM load distributions, saving total power consumption by approximately 14.55%. A simulation data analysis for a one-year operation period showed that the proposed method can save up to 16.68% in power consumption over the MM method on a monthly basis and is conclusively superior to the MM controlled chiller model. This proved that the NNGA method proposed in this study provides an effective solution for optimizing load distributions of common multiple-chiller plants in parallel operation.

Robustness Analysis and Enhancement of Chiller Sequencing Control under Uncertainties

Chiller sequencing control is a vital function for the feasible and energy-efficient operation of multiple-chiller plant in a centralized HVAC system. Various control strategies have been developed and applied to achieve overall energy efficiency of chiller plant with guarantying sufficient cooling for indoor thermal comfort. However, these control strategies suffer from various uncertainties and usually fail to achieve the expected control and/or energy performance. Therefore, this study presents a robustness analysis of chiller sequencing control in order to understand the robustness of the typical control strategies under uncertainties. Based on the robustness analysis, a simple and applicable control method is developed to enhance the robustness and reliability of chiller sequencing control under uncertainties.

Amelioration of the cooling load based chiller sequencing control

Cooling Load based Control (CLC) for the chiller sequencing is a commonly used control strategy for multiple-chiller plants. To improve the energy efficiency of these chiller plants, researchers proposed various CLC optimization approaches, which can be divided into two groups: studies to optimize the load distribution and studies to identify the optimal number of operating chillers. However, both groups have their own deficiencies and do not consider the impact of each other. This paper aims to improve the CLC by proposing three new approaches. The first optimizes the load distribution by adjusting the critical points for the chiller staging, which is easier to be implemented than existing approaches. In addition, by considering the impact of the load distribution on the cooling tower energy consumption and the pump energy consumption, this approach can achieve a better energy saving. The second optimizes the number of operating chillers by modulating the critical points and the condenser water set point in order to achieve the minimal energy consumption of the entire chiller plant that may not be guaranteed by existing approaches. The third combines the first two approaches to provide a holistic solution. The proposed three approaches were evaluated via a case study. The results show that the total energy consumption saving for the studied chiller plant is 0.5%, 5.3% and 5.6% by the three approaches, respectively. An energy saving of 4.9–11.8% can be achieved for the chillers at the cost of more energy consumption by the cooling towers (increases of 5.8–43.8%). The pumps’ energy saving varies from −8.6% to 2.0%, depending on the approach.

Robustness analysis of chiller sequencing control

Multiple-chiller plant is commonly employed in the heating, ventilating and air-conditioning system to increase operational feasibility and energy-efficiency under part load condition. In a multiple-chiller plant, chiller sequencing control plays a key role in achieving overall energy efficiency while not sacrifices the cooling sufficiency for indoor thermal comfort. Various sequencing control strategies have been developed and implemented in practice. Based on the observation that (i) uncertainty, which cannot be avoided in chiller sequencing control, has a significant impact on the control performance and may cause the control fail to achieve the expected control and/or energy performance; and (ii) in current literature few studies have systematically addressed this issue, this paper therefore presents a study on robustness analysis of chiller sequencing control in order to understand the robustness of various chiller sequencing control strategies under different types of uncertainty. Based on the robustness analysis, a simple and applicable method is developed to select the most robust control strategy for a given chiller plant in the presence of uncertainties, which will be verified using case studies.

Uncertainty analysis for chiller sequencing control

Chiller sequencing control is an essential function for a multiple-chiller plant that switches on and off chillers in terms of building instantaneous cooing load. It significantly affects both indoor temperature control (hence indoor thermal comfort) and building energy consumption. Various chiller sequencing controls have been developed and implemented in practice, and all of them switch on or off chillers according to a direct or an indirect indicator of the building instantaneous cooling load. Potential uncertainties in the direct or indirect indicator may cause the sequencing control misbehave and deteriorate the control and energy performance of the chiller plant. Until now, there is no any systematic study to investigate those uncertainties. This paper, therefore, proposes such a study. Four typical chiller sequencing controls are considered, including total cooling load-based sequencing control, return water temperature-based sequencing control, bypass flow-based sequencing control, and direct power-based sequencing control. Their uncertainty sources are identified and grouped into different categorizes. In order to facilitate the uncertainty modelling and analysis, all of those uncertainties are shifted to the load indicator of the corresponding sequencing control. Case studies are presented to show that using the proposed method of uncertainty shift and modelling the impacts of the uncertainties on the sequencing controls can be easily identified and analysed.

Fusion of redundant measurements for enhancing the reliability of total cooling load based chiller sequencing control

Total cooling load based chiller sequence in multiple-chiller plants is essentially the best approach to stage a chiller on or off in order to satisfy thermal comfort requirement and achieve energy efficiency simultaneously. In practice, however, this approach cannot be reliably implemented. The reason is the measurement of the cooling load of multiple-chiller plants is not always consistent enough for staging chillers on or off appropriately. Measurement uncertainties, including noises, outliers and biases, have a significant influence on the performance of the sequencing operation. This paper develops a strategy of fusing available redundant measurements to reduce the measurement uncertainties. With a moving window, the proposed strategy can (i) remove measurement outliers according to a calibrated Moffat distance between redundant measurements; (ii) reduce the influence of measurement noises by merging redundant measurements; and (iii) calibrate the bias of the merged measurements. Simulation studies are represented to show the merits of the proposed strategy for improving the reliability of the total cooling load based chiller sequencing control.

A novel energy-saving method for air-cooled chiller plant by parallel connection

A novel method was put forward for improving the energy efficiency of air-cooled water chiller plant operating on part load conditions. The conventional multiple-chiller plant was proposed to be integrated into one refrigeration cycle, by connecting those separate compressors, condensers and evaporators in parallel, respectively. The integrated multiple-chiller plant uses the electronic expansion valve to control refrigerant flow, achieving variable condensing temperature control. A prototype composed of four reciprocating compressors (including one variable-speed compressor), with total nominal cooling capacity of 120 kW was simulated and experimented. Both the simulative and experimental results indicated that applying this novel energy-saving method, the air-cooled chiller plant could get a significant performance improvement on various part load ratio (PLR) conditions, due to the apparent decrease of condensing temperature and some increase of evaporating temperature. Under the same outdoor temperature of 35 °C, when the PLR decreased from 100% to 50%, the COP increased by about 16.2% in simulation and 9.5% in experiment. Also, the practical refrigeration output ratio of the system was 55% on the condition of 50% PLR.

Simulation model of a screw liquid chiller for process industries using local heat transfer integration approach

This paper presents a system simulation model of an oil-injected screw liquid chiller, where the refrigerant shell and tube heat exchangers are modelled following local heat transfer integration approach. All major components of the system are modelled in a modular format such as an oil-injected screw compressor, a shell and tube condenser, a flooded evaporator, and a high side-float valve. The simulation results are validated with the experimental data of a multiple-chiller plant at a process industry. The validated results show that the part-load ratio and the glycol-water temperature at the evaporator inlet affect the system performance considerably as compared to the temperature of cooling water entering the condenser.

Three-zone system simulation model of a multiple-chiller plant

This paper presents a system simulation model of an oil-injected screw chiller. The refrigerant (shell and tube) heat exchangers are modeled, using a three-zone approach, to study the effects of the operational parameters on the fractional area allocated to each phase within the heat exchangers. All major components of the system such as, an oil-injected screw compressor, a shell and tube condenser, a flooded evaporator and a high side-float valve, are modeled in a modular format. The predicted results are validated with experimental data collected from a multiple-chiller plant at a process industry. The results show that the part-load ratio and the temperature of glycol-water entering the evaporator affect the system performance significantly and have strong influence on the fractional areas allocated to each phase within the condenser.

One-zone simulation model of an oil-injected screw chiller

This paper presents a one-zone steady-state system model of an oil-injected screw chiller. The model can be used as a design and optimization tool for system performance of multiple-chiller plant in process industries. All major components of the system are modelled in a modular format including the oil-injected screw compressor, shell and tube condenser, flooded evaporator and a high side-float value. The model results are validated with the experimental data from a multiple-chiller plant at a process industry. The validated results show that the part-load ratio and the glycol–water temperature at the evaporator inlet significantly affect the system performance as compared to the temperature of cooling water entering the condenser. Copyright © 2004 John Wiley & Sons, Ltd.

Applying condensing-temperature control in air-cooled reciprocating water chillers for energy efficiency

This paper reports on the modelling and findings of the energy performance of an air-cooled reciprocating multiple-chiller plant under the conventional head pressure control and the new condensing-temperature control in a subtropical climate. The simulation model was validated using the operating data of an existing chiller plant. As noted from this existing air-cooled reciprocating chiller plant, there was a substantial efficiency drop at part-load resulting from the head pressure control. If operating at variable lower condensing-temperatures based on the established operating mode of the condenser fans and compressors, it is shown that the chiller consumption can be maintained below 2 kW/refrigeration ton throughout the entire range of outdoor temperature and part-load conditions, giving an average efficiency of 1.08 kW/refrigeration ton. The energy imposition due to cycling on more condenser fans can be compensated by the reduced compressor consumption. Potential energy savings of 18.2 and 29% in the annual chiller consumption are achievable by applying the condensing-temperature control to two existing chiller plants studied. This supports the need to develop the condensing-temperature control as an improvement to the conventional head pressure control.

Part load efficiency of air-cooled multiple-chiller plants

This paper reports an analysis of the energy efficiency of air-cooled chiller plants in buildings and opportunity of improved part load efficiency. Load-frequency profile, weather-load profile and thermal performance lines obtained by simulation of typical office and hotel buildings in a subtropical climate are examined to address the methods of determining the plant size, identifying the loading characteristics, optimizing the combination of multiple-chillers and evaluating the energy saving alternatives in design or retrofit. It is shown that the percentage design load for office is best related to external temperature; for hotels the product of external temperature and humidity is more appropriate as a climatic index. The results indicate that for a particular value of percentage design load, the likely outdoor temperature can be determined statistically, and the consumption can be determined from a set of chiller efficiency curves as a function of chiller part load ratio and entering condenser air temperature. The opportunity for improving the operating efficiency of air-cooled multiple-chiller plants by lowering the condensing temperature at lower outdoor temperatures is discussed.