21 research outputs found

    Model-free Control and Automatic Staging of Variable Refrigerant Flow System with Multiple Outdoor Units

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    For efficient operation of a variable refrigerant flow (VRF) air conditioning system with multiple outdoor units (ODUs), we propose a model-free control strategy based on extremum seeking control along with automatic staging control logic. The proposed strategy is evaluated with a representative VRF system consisting of 12 indoor units (IDUs) and three ODUs. The IDU zone temperature is regulated by EEV opening, and the compressor pressure is regulated by compressor speed. To optimize load sharing among multiple ODUs in operation, a set of bypass valves (BPVs) are added to the suction side of the compressors to manipulate refrigerant flow distribution among different compressors as needed. A penalty-function based multivariable extremum seeking control (ESC) method is used for real-time optimization of system operation. The performance index as the ESC feedback is the total power of the compressors, the ODU fans and the IDU fans, augmented with penalties for securing minimum superheat at the suction side of compressors. The manipulated inputs include the compressor suction pressure setpoint, the openings of BPVs at the suction side of the compressors, and a uniform setpoint of fan speed for all ODUs. As for the ESC feedback, the compressor power is normalized by its capacity. A set of control strategies for staging on/off particular ODUs is developed based on the compressor speed of the operating ODUs. Under increasing load, if the operating compressor(s) speed exceeds the higher limit of operation speed range (80% of rated speed), an additional ODU turned on to meet the load demand. Under decreasing load, it is desirable to turn off the least efficient ODU in a model-free fashion. In this study, an ESC based ODU staging-off strategy is proposed, for which the compressor shaft power normalized by the rated capacity is adopted as the ESC input. In addition to the compressor pressure setpoints and ODU fan speeds, the manipulated inputs of ESC also include the openings of suction-side BPVs in order to optimize load sharing among the multiple ODUs. With online optimization of ODU load sharing based on the normalized compressor power, the ESC can drive less efficient compressor(s) to operate at lower speed/capacity. If the compressor speed of an ODU falls below the preset lower limit of operational speed range (e.g. 20% of the rated speed) for long enough time, this ODU will be turned off. A dynamic simulation model of the multi-ODU VRF system is developed with Dymola and TIL Library. Simulation studies have been performed to evaluate the proposed ESC strategy for energy efficient operation during constant load patterns and the control logic for staging on and off ODU during load increase and decrease. The total power searched by the ESC is shown to be close to that obtained by a genetic algorithm based global optimization procedure in Dymola. Also, ESC is shown to be able to turn off least efficient ODU during load decrease without model knowledge. The load-sharing BPV at the compressor suction-side demonstrates bearable pressure loss except for the scenarios of large split ratio

    Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

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    Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques

    Model free real-time optimization for vapor compression systems

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    A vapor compression system's optimal input settings vary according to changes in environmental conditions. Tracking these optimal input trajectories can be challenging when insufficient information for a reliable system model is available. An alternative set of optimization approaches use system measurements. This thesis focuses on one such approach, extremum seeking control, which uses performance index measurements to determine optimal system settings. Forgoing system model knowledge and relying exclusively on data allows an optimization approach to function well on many different plants. However, this added adaptivity comes at a performance cost. Using prior system model knowledge can be helpful for ensuring that a controller design works from the start of operation and inputs can be changed as soon as information about environmental conditions is updated. By contrast, data based methods may require the control designer to spend a time generating data in order to obtain enough information about the system to make good decisions online. A central theme of this work is addressing the trade off between using prior system model knowledge and ensuring sufficient adaptability of the extremum seeking optimization approach. Two main factors in the extremum seeking design are considered: the choice of extremum seeking control law and the choice of extremum seeking control input. Extremum seeking control laws come from the field of mathematical optimization; this thesis considers the pros and cons of choosing between gradient descent and Newton descent. Both simulations and experimental results show that while Newton descent extremum seeking is less reliant on model knowledge, but slower to find optimal inputs than gradient descent extremum seeking. Because of extremum seeking's adaptability to different plants, many different inputs can be chosen for implementation. However, using an approach known as self-optimizing control, knowledge about the plant's behavior can help choose set points with optimal values that are insensitive to changes in environmental conditions. Finding these special inputs turns the input tracking problem into a regulation problem. Both simulation and experimental results confirm that combining self-optimizing control and extremum seeking control can help improve tracking even as environmental conditions change

    Learning-Based Controller Design with Application to a Chiller Process

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    In this thesis, we present and study a few approaches for constructing controllers for uncertain systems, using a combination of classical control theory and modern machine learning methods. The thesis can be divided into two subtopics. The first, which is the focus of the first two papers, is dual control. The second, which is the focus of the third and last paper, is multiple-input multiple-output (MIMO) control of a chiller process. In dual control, the goal is to construct controllers for uncertain systems that in expectation minimize some cost over a certain time horizon. To achieve this, the controller must take into account the dual goals of accumulating more information about the process, by applying some probing input, and using the available information for controlling the system. This is referred to as the exploration-exploitation trade-off. Although optimal dual controllers in theory can be computed by solving a functional equation, this is usually intractable in practice, with only some simple special cases as exceptions. Therefore, it is interesting to examine methods for approximating optimal dual control. In the first paper, we take the approach of approximating the value function, which is the solution of the functional equation that can be used to deduce the optimal control, by using artificial neural networks. In the second paper, neural networks are used to represent and estimate hyperstates, which contain information about the conditional probability distributions of the system uncertainties. The optimal dual controller is a function of the hyperstate, and hence it should be useful to have a representation of this quantity when constructing an approximately optimal dual controller. The hyperstate transition model is used in combination with a reinforcement learning algorithm for constructing a dual controller from stochastic simulations of a system model that includes models of the system uncertainties. In the third paper, we suggest a simple reinforcement learning method that can be used to construct a decoupling matrix that allows MIMO control of a chiller process. Compared to the commonly used single-input single-output (SISO) structures, these controllers can decrease the variations in some system signals. This makes it possible to run the system at operating points closer to some constraints, which in turn can enable more energy-efficient operation

    Gray box dynamic modeling of vapor compression systems for control optimization

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    Buildings account for 75% of electricity use in the U.S. and more than 24% of building electrical energy is consumed by vapor compression equipment, including air-conditioners, refrigerators/freezers and heat pumps. Dynamic modeling of vapor compression systems (VCS) is particularly important for developing and validating optimal control strategies to maximize the system efficiency and reliability. However, existing modeling techniques are rarely used in control practices because of the significant model development effort and requirement of high computational resources. This dissertation presents an efficient and robust gray-box dynamic modeling approach for VCS to support control optimization. The presented methodology allows automated construction of data-driven VCS models with minimum training data and human inputs. The overall approach incorporates a multi-stage training procedure with separate estimation of the steady-state and dynamic model parameters along with a finite control volume scheme to achieve good model identifiability while ensuring adequate prediction accuracy. To improve model reliability, the modeling approach incorporates sensitivity analysis and de-correlating steps in a pre-conditioning procedure to avoid over-parameterization. The system-level training identifies the refrigerant charge that minimizes the steady-state simulation errors while the dynamic modeling stage transforms the established steady-state system model into a dynamic counterpart, in which the optimal thermal capacitances of the heat exchanger walls are identified to best reproduce system transient responses

    Anti-Idling Systems for Service Vehicles with A/C-R Units: Modeling, Holistic Control, and Experiments

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    As people have begun to pay more attention to energy conservation and emission reduction in recent years, anti-idling has become a growing concern for automobile engineers due to the low efficiency and high emissions caused by engine idling, i.e., the engine is running when the vehicle is not moving. Currently, different technologies and products have emerged in an effort to minimize engine idling. By studying and comparing most of these methods, the conclusion can be drawn that there is still much room to improve existing anti-idling technologies and products. As a result, the optimized Regenerative Auxiliary Power System (RAPS) is proposed. Service vehicles usually refer to a class of vehicles that are used for special purposes, such as public buses, delivery trucks, and long-haul trucks. Among them, there are vehicles with auxiliary devices such as air conditioning or refrigeration (A/C-R) systems that are essential to be kept running regardless of the vehicle motion. In addition, such auxiliary systems usually account for a large portion of fuel from the tank. Food delivery trucks, tourist buses, and cement trucks are examples of such service vehicles. As a leading contributor to greenhouse gas emissions, these vehicles sometimes have to frequently idle to for example keep people comfortable, and keep food fresh on loading and unloading stops. This research is intended to develop and implement a novel RAPS for such service vehicles with the A/C-R system as the main auxiliary device. The proposed RAPS can not only electrify the auxiliary systems to achieve anti-idling but also use regenerative braking energy to power them. As the main power consuming device, the A/C-R system should be treated carefully in terms of its efficiency and performance. Thus, the developments of an advanced controller for A/C-R system to minimize energy consumption and an optimum power management system to maximize the overall efficiency of the RAPS are the primary objectives of this thesis. In this thesis, a model predictive controller (MPC) is designed based on a new A/C-R simplified model to minimize the power consumption while meeting the temperature requirements. The controller is extensively validated under both common and frosting conditions. Meanwhile, after integrating the RAPS into a service vehicle, its powertrain turns into a parallel hybrid system due to the addition of an energy storage system (ESS). For the sake of maximizing the overall efficiency, RAPS requires a power management controller to determine the power flow between different energy sources. As a result, a predictive power management controller is developed to achieve this objective, where a regenerative iv braking control strategy is developed to meet the driver’s braking demand while recovering the maximum braking energy when vehicles brake. For the implementation of the above controllers, a holistic controller of the RAPS is designed to deal with the auxiliary power minimization and power management simultaneously so as to maximize the overall energy efficiency and meet the high nonlinearities and wide operating conditions

    Modellazione dinamica e valutazione delle prestazioni energetiche stagionali di sistemi basati su pompe di calore aria-acqua

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    In this Thesis a series of numerical models for the evaluation of the seasonal performance of reversible air-to-water heat pump systems coupled to residential and non-residential buildings are presented. The exploitation of the energy saving potential linked to the adoption of heat pumps is a hard task for designers due to the influence on their energy performance of several factors, like the external climate variability, the heat pump modulation capacity, the system control strategy and the hydronic loop configuration. The aim of this work is to study in detail all these aspects. In the first part of this Thesis a series of models which use a temperature class approach for the prediction of the seasonal performance of reversible air source heat pumps are shown. An innovative methodology for the calculation of the seasonal performance of an air-to-water heat pump has been proposed as an extension of the procedure reported by the European standard EN 14825. This methodology can be applied not only to air-to-water single-stage heat pumps (On-off HPs) but also to multi-stage (MSHPs) and inverter-driven units (IDHPs). In the second part, dynamic simulation has been used with the aim to optimize the control systems of the heat pump and of the HVAC plant. A series of dynamic models, developed by means of TRNSYS, are presented to study the behavior of On-off HPs, MSHPs and IDHPs. The main goal of these dynamic simulations is to show the influence of the heat pump control strategies and of the lay-out of the hydronic loop used to couple the heat pump to the emitters on the seasonal performance of the system. A particular focus is given to the modeling of the energy losses linked to on-off cycling.Questa Tesi presenta una serie di modelli numerici sviluppati per la valutazione delle prestazioni stagionali di sistemi basati su pompe di calore reversibili di tipo aria-acqua accoppiate a edifici residenziali e non residenziali. Lo sfruttamento del potenziale risparmio energetico legato all'adozione di pompe di calore èun compito difficile per i progettisti, in quanto diversi fattori come la variabilità delle condizioni climatiche esterne, la capacità delle pompe di calore di modulare la potenza termica/frigorifera erogata, la logica di controllo del sistema e la configurazione impiantistica utilizzata influiscono sulle prestazioni energetiche ottenibili. Lo scopo di questo lavoro è quello di studiare in dettaglio tutti questi aspetti. Nella prima parte della Tesi viene presentata una serie di modelli basati su un approccio di tipo "temperature class" per la previsione delle prestazioni stagionali di una pompa di calore reversibile aria-acqua. Viene proposta un'innovativa procedura di calcolo per la determinazione dell'efficienza stagionale della pompa di calore costruita come un'estensione della metodologia riportata dalla norma europea EN 14825. Tale procedura può essere applicata non solo per lo studio di pompe di calore mono-compressore (On-off HPs), ma anche di pompe di calore multi-compressore (MSHPs) ed a velocità variabile (IDHPs). Nella seconda parte della Tesi la simulazione dinamica è stata utilizzata allo scopo di ottimizzare il sistema di controllo della pompa di calore e dell’impianto. I modelli dinamici sono stati realizzati utilizzando il software TRNSYS e permettono di simulare il comportamento dinamico di On-off HPs, MSHPs e IDHPs. Lo scopo principale dei modelli dinamici presentati è quello di evidenziare l'influenza dei sistemi di regolazione della pompa di calore e della configurazione del circuito idronico utilizzato per accoppiare la pompa di calore ai terminali sulle prestazioni stagionali dell'impianto. Particolare attenzione è stata rivolta alla modellazione delle perdite energetiche legate ai cicli di on-off della pompa di calore

    Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

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    The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

    Advances in Theoretical and Computational Energy Optimization Processes

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    The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

    分散型エネルギーシステムにおける設備保全とシステム最適化に関する研究

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    Owing to the continuous growth in the world\u27s energy demand, the problems of energy consumption, greenhouse gas emission, and environmental pollution have become increasingly prominent. The distributed energy resource (DER) system is a high-efficiency energy system that can promote energy-saving and decrease carbon emissions. the focus of this research is on the equipment maintenance and system optimization of DER. In the maintenance optimization stage, a maintenance priority assessment method is used to allocate maintenance management resources based on the assessment results to help managers develop reasonable maintenance strategies and reduce maintenance costs. In the system design optimization stage, the capacity and operation strategy of the system is optimized for the energy demand of users to achieve the purpose of improving economic benefits and promoting energy saving and emission reduction.北九州市立大
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