Chilled Water Plant Research Articles

Public-public partnerships : A twist on the traditional publicprivate partnership that works for your airport

As airport operators, the provision of chilled water and back-up power generation is a necessary element of our utility infrastructure. This paper outlines the benefits and pitfalls related to outsourcing this increasingly critical infrastructure to the local power company and describes the case of Orlando International Airport (MCO), which formed a public-public partnership with Orlando Utilities Commission (OUC). Over the course of a multiyear negotiation, we engaged OUC to take on operating the chilled water plant and the back-up generation facility for the airport’s new Terminal C. Throughout the negotiation, we addressed many challenges. The aim of this paper is to familiarise the airport operator with the transaction and some of the key issues we faced and resolved at MCO.

Model-based data center cooling controls comparative co-design

This article presents a comparative simulation-based control logic design process. It uses the Control Description Language (CDL) and the ASHRAE Guideline 36 high-performing building control sequences with the Modelica Buildings Library (MBL) to demonstrate a comparative analysis of two control designs for a data center chilled water plant. Details include a description of the closed-loop plant and control design methodology, including sizing and parameterization, base and alternative (Guideline 36) control logic with software implementation structure, and outline of the simulation experimentation process. The selected control designs are paired with comparable chilled water plant configurations. The models include a chiller, a water-side economizer, and an evaporative cooling tower. The plant provides cooling at 27ºC zone supply air temperature to a data center in Sacramento, CA. The comparative simulation results examined the impacts of a selected control logic detail, and present an example model-based design application. Overall, the simulation results showed a 25% annual and a 18% summer energy use reduction for alternative controls. This shows that simulation-based control logic design performance evaluation can improve energy efficiency and resilience aspects of system controls at large.

Optimizing mixed cool thermal storage systems across a connected community

A high level of electric demand flexibility must be integrated into our building infrastructure to enable greater renewable energy penetration in the grid. In the U.S., 9 % of electricity generated is used to cool buildings in a periodic manner, making this end-use an ideal target for active management through cool thermal energy storage (CTES) technologies. Historic uses for CTES are designed around central chilled water plants, but these systems cool less than 25 % of U.S. commercial floorspace. Emerging technologies are under development to serve the many smaller distributed cooling systems, such as rooftop units (RTUs), and have the potential to add CTES to an additional 66 % of cooled commercial floorspace. However, these unitary thermal storage systems (UTSS) lack the modeling and analysis tools to evaluate them in the future interactive grid context. This study develops the modeling and optimization tools necessary to simultaneously examine central and distributed ice storage systems within the multi-building, connected community context. An integrated simulation-optimization workflow is created to allow for rapid customized analysis. Results demonstrate the energy and flexibility tradeoffs of various implementations.

Improving training efficiency for scalable automated fault detection and diagnosis in chilled water plants by transfer learning

Automated fault detection and diagnosis (AFDD) is crucial for improving the energy efficiency of air conditioning systems, and its quantitative effects have become clearer in recent years. Although many AFDD methods are proposed in the literature, there is insufficient research on their scalability to different systems. The authors previously proposed an AFDD method using a fault dataset created by simulation that can be used to train convolutional neural network (CNN); however, this AFDD method is difficult to apply to different systems because it requires significant computation time to generate a fault dataset by simulation and train the CNN. In this study, we focused on transfer learning and examined whether reducing the size of a fault dataset and re-training a CNN with only some parameters of the originally-trained CNN would reduce the computation time and make training more efficient. Specifically, we ran simulations, calculated the fault dataset, trained the CNN, and applied transfer learning using two real chilled-water plants. As a result, it was confirmed that by reducing the fault dataset by a certain amount and the limiting the number of epochs for re-training, it is possible to achieve reasonable diagnostic performance while reducing the computation time to less than half compared to our previous method. This study also has value in that it examined the application of transfer learning using multiple real systems.

Towards integration of virtual meters into building energy management systems: Development and assessment of thermal meters for cooling

The increasing availability of sub-hourly operational data represents an untapped opportunity to help better manage and operate buildings. However, specific variables may not be typically measured, and virtual sensors and meters can be used to infer these key variables. This paper presents the development and assessment of three virtual energy meters (VEMs), which aim to estimate cooling load at different levels by leveraging available data from: 1) the chilled water plants, 2) the Air Handling Units (AHUs), and 3) the Variable Air Volume (VAV) boxes. These VEMs were developed based on mass and energy balance equations and applied to an existing large commercial building equipped with primary/secondary AHUs and VAV systems. Case study results show 11% difference in average between similar VEMs in two years and that 23% of the building cooling load is for dehumidification. Among the over 500 office rooms, 9% consume more than 3 times the average cooling energy. This paper also discussed in detail the merits of each VEM, and their potential applications including performance tracking, energy use mapping, inefficiency detection and operation optimization. This work highlights the potential of VEMs and represents a first step towards their integration into building energy management systems.

Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation

The integration of thermal energy storage in chilled water systems is an effective way to improve energy efficiency and is essential for achieving carbon emission reduction. However, the commonly used large-scale thermal energy storage needs significantly larger space, which hinders the wide application of thermal storage in large number of existing buildings. Unlike previous studies, this study integrated a small-scale stratified chilled water storage tank into chilled water plants and proposed a global optimal control strategy to enhance the overall system energy performance. The proposed strategy determines the optimal settings of stratified chilled water storage tank charging/discharging flow rate, chilled water supply temperature, and the number of chillers in order to minimize the daily energy consumption of the chilled water plants under varying load conditions. The stratified chilled water storage tank was modelled as a “virtual chiller” to quantify the energy consumption related to the charging/discharging. Multiple charging/discharging cycles were controlled for optimal chiller loading. The proposed control strategy was evaluated in a simulated complex central chilled water plant. The results show that the proposed optimal control strategy can save the daily energy consumption of the central chilled water plant by 4.35–7.67%, 2.10–3.90%, and 2.30–5.15% in three typical weather conditions.

Integrated framework for optimization of air- and water-side HVAC systems to minimize electric utility cost of existing commercial districts

Summer peak electric demand of commercial buildings and districts is typically driven by the cooling load. This paper presents a novel approach to holistically optimize both the air- and water-side of the HVAC system in existing commercial districts. Therefore, this paper is different from the existing literature in its focus on integrated optimization of both air- and water-side of the HVAC system in a district scale as opposed to only airside or waterside. Methodologies proposed in this paper for integrated optimization of air- and water-side are brand-agnostic and their capabilities are demonstrated using a chilled water plant at Colorado School of Mines’ campus. Based on the results for the test district, Mines’ integrated air- and water-side optimizations of the HVAC system reduces its peak electric demand by 15% and 13%, respectively, for August 19 and 20, 2019. The air- and water-side optimization of the HVAC system resulted in greater savings than that achieved with only the waterside demand optimization method discussed in authors' previous paper focused on existing commercial districts.

Data-Driven Self-Optimizing Control With Parametric Programing Based Constraint Handling

AbstractSelf-optimizing control (SOC) aims to achieve near-optimal operation regardless of variation in disturbances. For constrained operation, the model-based SOC has been solved as an explicit model predictive control (MPC) problem, where the changes in active constraint sets under large variations in disturbance inputs can be determined via parametric programing. For constrained SOC design with historical data only, this paper presents an extended work for a data-driven method that Zhao et al. proposed (2022 “Global Self-Optimizing Control With Data-Driven Optimal Selection of Controlled Variables With Application to Chiller Plant”, ASME J. Dyn. Sys., Meas., Control., 144(2), p. 021008.) for obtaining global SOC solution. While this work remains to adopt the Koopman subspace model that has been used, the SOC-associated constrained optimization problem is solved by applying the multiparameter quadratic programing technique. The solutions consist of piecewise-affine control laws and active constraint sets that are determined via partitions of the disturbance space. The proposed approach is illustrated with a Modelica simulation model of a chilled-water plant, where minimizing the total power consumption is the economic objective. The results show that the proposed method yields the total power very close to that by an offline genetic algorithm-based global optimization procedure, and substantially better than a best-practice rule-based control strategy. The degree of constraint violations under high cooling load is also smaller with the solution of constrained SOC.

Global Self-Optimizing Control With Data-Driven Optimal Selection of Controlled Variables With Application to Chiller Plant

AbstractIn this paper, we propose a global self-optimizing control (SOC) approach, where nonlinear dynamic model is obtained from historical data of plant operation via the framework of sparse identification of nonlinear dynamics (SINDy) combined with regularized regression. With the nonlinear static input–output map obtained by forcing steady-state operation, the globally optimal solutions of controlled variables (CVs) can be found by tracking the necessary conditions of optimality (NCO) in an analytical fashion. After validation with a numerical example, the proposed method is evaluated using a modelica-based dynamic model of a chilled-water plant. The economic objective for chiller plant operation is to minimize the total power of compressor, condenser water pump, and cooling tower fan, while the cooling tower fan speed and condenser water mass flow rate are used as manipulated inputs. The operating data are generated based on realistic ambient and load conditions and a best-practice rule-based strategy for chiller operation. The control structure with the SOC method yields a total power consumption close to the global optimum and substantially smaller than that of a best-practice rule-based chiller plant control strategy. The proposed method promises a global SOC solution using dynamic operation data, for cost-effective and adaptive control structure optimization.

Extremum-seeking control integrated online input selection with application to a chilled-water plant

Extremum Seeking Control (ESC) is a model-free control solution for real-time optimization of system operation where model acquisition is difficult and/or cost prohibitive,. For many HVAC and refrigeration systems, there can be a large number of candidate inputs for ESC design, however, some inputs affect the performance measure to a greater degree than others. This paper presents an online input selection method for multivariable ESC, which uses a singular value decomposition (SVD) analysis coupled with a dither-demodulation based online Hessian estimate for the underlying static map. A subset of physical inputs or a new set of inputs via linear combination of the physical inputs can be determined using the proposed approach. We present an analysis for quantifying the loss bound of achievable optimum output with the underlying input selection. Further, the Hessian estimation error bound is quantified with perturbation analysis. The proposed method is evaluated with Modelica simulation models of chilled-water plants, one with a single chiller and the other with two parallel chillers. The simulation results validate the effectiveness of the proposed method of input selection.

Optimal chiller loading in dual-temperature chilled water plants for energy saving

Buildings account for almost 40% of global energy consumption. Due to the high energy consumption of chilled water plants, various studies have optimized chiller loading in plants with multiple chillers for energy conservation. However, few studies have optimized dual-temperature chiller plants, even though better energy efficiency could be achieved than that of typical single-temperature chiller plants. This paper proposes two optimal control strategies for dual-temperature chilled water plants, strategy B and strategy C. Strategy B optimizes the cooling load distribution of the chillers in each group by adjusting the cooling load ratio of each chiller. Under this strategy, the energy consumption of the chiller plant for the entire cooling season was reduced by 10.1%. Meanwhile, strategy C optimizes the cooling load distribution among chillers in the same chiller group and between two chiller groups, by simultaneously adjusting the temperature setpoint of the air leaving the primary cooling coils and the partial load ratio of each chiller. By considering both the impact of the chilled water loop and the air handling process, strategy C achieved greater energy saving (16.4%) for the entire cooling season. In hot summer months, the energy savings arise mainly from optimization of the cooling load distribution among chillers in each chiller group, as this optimization accounts for 63–68% of the total savings. In moderate months, optimizing the cooling load distribution among chillers in the same group and optimizing the distribution between two chiller groups account for nearly the same proportion of the total energy savings.

Comparison of data-driven statistical techniques for cooling demand modelling of electric chiller plants in commercial districts

This paper models and forecasts the cooling demand of centralized chiller plants in commercial districts using different statistical techniques – including OLS, Lasso, ARX, SARIMA, SARIMAX and Cochrane–Orcutt. Direct estimation of the test error using a validation data set is used to compare different techniques and to quantify goodness of fit. As a validation, these statistical techniques are compared for forecasting the cooling demand of the largest chilled water plant on the Colorado School of Mines’ campus. Overall, Cochrane–Orcutt provides the highest accuracy among these techniques, with a lowest MSE, RMSE, CV-RMSE and MBE and highest r 2 value. As a showcase of the capabilities of the developed cooling demand, the predicted demand is coupled with Hydeman et al.’s electric chiller model to predict chiller’s electric demand. The RMSE, CV-RMSE and r 2 of the electric chiller model are 22.7 kWe, 17% and 0.84, respectively.

Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil

This work presents an extensive study on methodologies to calibrate electric energy consumption in buildings. A comparison between several calibration methodologies shows different approaches addressing the same issue, suggesting a lack of a unique methodology that is reproducible for every building. Additionally, no methodology fits the Brazilian public context, such as the predominance of Unitary Air Conditioning Systems (UACS) and buildings which have operated for more than 30 years. A new calibration methodology for performance simulation is proposed to deal with such features. The methodology is separated into two evidence-based steps according to the size of the Heating, Ventilation and Air Conditioning (HVAC) systems used to control buildings’ indoor environments: the first step is dedicated to calibrating medium- and large-sized HVAC systems, and the second step is dedicated to calibrating small-sized HVAC systems. University Hospital of University of São Paulo (UH-USP) is used as a test bed to implement the proposed methodology. Accuracy indicators show the efficiency of the methodology in terms of calibrating a simulation of the whole UH-USP building and Chilled Water Plant on a monthly basis in terms of accuracy and the time needed to perform the calibration. However, regarding simulation of UACS, the application of the methodology was inconclusive. This study leaves open the question of the trade-off between increasing model outcome accuracy and the strictness of accuracy indicators applied to UACS and poorly automated large-sized air conditioners.

Electric demand minimization of existing district chiller plants with rigid or flexible thermal demand

Building cooling drives the peak electric demand in summer; therefore, it is an important component in energy related costs. While there have been efforts to optimize chiller(s) on individual buildings and/or district cooling systems, previous studies use simplified models for the involved components (e.g., cooling towers, pumps) that do not fully represent the actual performance of components or neglect some of the utilized components. This paper provides an open-source approach for electric demand minimization of existing district cooling systems developing data-driven performance models with as-operated data to realistically simulate district-scale heating, ventilating, and air-conditioning components. In addition, a flexible, non-linear model for the electric demand minimization of chiller plants in districts with a rigid or flexible thermal cooling demand is proposed in this study. The proposed methodologies for model development and optimization are brand-agnostic, and their capabilities are demonstrated using the Colorado School of Mines’ main chilled water plant. Our results show a daily peak electric demand reduction of up to 15.2% for the week with the highest electric demands on the Mines campus using a rigid thermal cooling demand constraint. A 5% flexibility on thermal cooling demand provides a peak electric demand reduction of up to 18.7%. The results of this study highlight the extent of benefits that could be achieved by optimizing the district-scale operation of chiller plants with and without thermal cooling demand flexibility. This demand flexibility could be achieved through flexible resources and/or district-scale demand side management as buildings have diverse primary applications.

Study of chilled water plant heat exchange for special ships

The sizing of the air conditioning chilled water plant components shall be carried out considering both the summer and winter operating modes. Some devices are used in both modes, and others only in one of two working loads. Depending on the sources of harmful substances from the air conditioning spaces served, can be selected one of the following air recirculation systems: no recirculation (complete replacement with fresh air); partial recirculation (partial replacement of vicious air with fresh air); total recirculation (no fresh air injection).

Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm

As more renewable energy is integrated into the power grid, it is increasingly important to exploit variable electricity pricing structures to minimize commercial utility costs and enable more intermittent renewables on the grid through proactive management of energy storage. Using data from a large campus district energy system, equipped with centralized chilled water plants and a thermal energy storage tank, a novel technique is proposed to optimize this system in real-time, formulated as a mixed-integer quadratic programming problem. This method, titled Quadratic Programming Hybrid with Augmented Constraints, is sufficiently fast to be computed in real-time for a district chiller system. This method is compared to both Branch and Bound and a simple logical decision algorithm in both speed and optimality. The proposed method for solving this mixed-integer quadratic programming problem proves very successful at achieving a near-optimal solution when compared to a standard Branch and Bound (BnB) algorithm. Although suboptimal, the proposed algorithm takes 99.96–99.99% less computational time than the standard BnB and computes an answer to within 29.9% of the BnB objective function. When compared to a simple logical decision algorithm, which represents an operator manually controlling the plant, the proposed method is estimated to yield 8.10–33.7% in savings on chiller energy costs. The Quadratic Programming Hybrid with Augmented Constraints algorithm shows potential for use in a real-time optimization application to exploit variable electricity pricing and significantly reduce the costs of running a chiller plant with thermal energy storage.

Performance improvement of a large chilled-water plant by using simple heat rejection control strategies

Energy efficient cooling systems are urgently required in order to face the increasing cooling demand of the building and industry sectors and comply with the ambitious EU target of greenhouse gas emission reductions. The present work aims to show that simple heat rejection control strategies can lead to significant energy savings. Different approaches of sequencing the operation of several cooling towers and a method to adapt the cooling water temperature set-point to the actual ambient conditions are proposed. These strategies are first investigated theoretically for an existing chilled-water plant of 13.7 MW installed cooling capacity. Up to 20% energy savings are theoretically possible depending on the used control strategy and the weather conditions. A variable cooling water set-point as a function of the wet bulb temperature allows for the most significant savings. After implementation of this measure in the existing cooling plant, 2 years monitoring results show that the overall system efficiency of the chilled water plant could be increased by 15% compared to the initial status confirming the results of the theoretical study.

Strengths and Weaknesses of Existing Building Green Retrofits: Case Study of a LEED EBOM Gold Project

This study investigated the process of existing building green retrofits through examining a Leadership in Energy and Environmental Design for Existing Building: Operations and Maintenance (LEED EBOM) Gold project. The project demonstrated a standard green retrofit process for existing buildings, which includes energy auditing, building performance simulation, and measurement and verification. In this project, four energy conservation measures were applied to improve energy performance: light-emitting diode (LED) lighting, window films, green roofs, and chilled water plant upgrading and optimization. The expected energy saving was 30% after the retrofit; while the actual energy saving was 16%. The error of building performance simulation was one of uncertainties in this retrofit project. Occupancy conditions might be the main reason for this uncertainty. Strengths, weaknesses, opportunities and threats were identified and discussed for the green retrofit. The research results could be used to optimize the existing building retrofit process for better energy performance.

Contributions to the study of functional parameters in the exploitation of chilled water system for particular ships

Under normal operating conditions, the chilled water system controls and distributes cooled water, usually produced by two chilled water plants, to coolers in air treatment units, coolers for machinery spaces and coolers of various electronic components. The main purpose of the chilled water plant is to cool the water from the main return ring at a temperature of 7 [°C] and then to distribute it to consumers through the distribution ring. Under special operating conditions, depending on the cooling requirements, the other chilled water plants may also be switched on. For example, if only three of the five chilled water plants are available and the ship is under special operating conditions, can be ensured cooling conditions for all ship compartments except machinery spaces.

Analysis and prediction of chilled water plant performance based on multivariate statistical methods and large historical data

The large datasets resulting from operating HVAC&R systems are currently scrutinized to find ways to exploit the useful information that they might contain. In this work, historical data of a centrifugal water chiller over the course of more than 1.5 years of operation is used to learn about the system and to suggest modifications to its operation scheme. The results show that principal component analysis (PCA) captures well the variance in the historical data. The first two principal components explained between 62 and 80% of the variance, depending on the cases considered. The main factors responsible for the variation of the chiller operation are found to be the weather and the cold water temperature setpoint. The effect of the sampling time step on the results is also studied. Moreover, this work demonstrates that partial least squares (PLS) regression can adequately predict an important indicator of the chiller performance, namely the coefficient of performance (COP), one time step ahead with an R² of 77.49% and root-mean square error of estimation (RMSEE) of 0.463 using a separate validation set of data. The PLS model was also able to predict future COP values up to 2 time steps (∼3 h) in advance.