Evaluation and improvement of energy flexibility and performance of building heating, ventilation, and air-conditioning systems

The foreseen reduction of available fossil fuels, the continued increase in global energy demand, and the irrefutable evidence of climate change, along with the implementation of a global commitment to achieve a net-zero emissions target, have greatly sharpened commercial interest in using renewable energy resources (RER). However, the high penetration of RER-based stochastic power generation systems has resulted in a significant requirement for increased flexibility on the demand side that can allow buildings to adapt to increasingly dynamic energy supply conditions to support power grid operation and optimization. Failure to adapt may carry serious electrical blackouts and can compromise the safety of the supply side. The building sector accounts for a substantial amount of global energy usage and offers great opportunities for energy flexibility. Building energy flexibility is an important and emerging concept in the modern energy landscape, which can support the sustainable transition of the power sector. Building heating, ventilation, and air-conditioning (HVAC) systems are one of the leading energy consumers in buildings, which can be used as a key flexible source. The HVAC systems with integrated thermal energy storage (TES) can further enhance building energy flexibility. This thesis contributes to the evolving field of demand flexibility and introduces methodologies to evaluate and improve energy flexibility and performance of building HVAC systems

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

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

Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction and an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. Also the implemented distributed MPC algorithm is described and validated with simulation studies

Optimization and multivariable control of refrigeration systems

Los ciclos de refrigeración por compresión de vapor constituyen el método más extendido a nivel mundial para la generación de frío. Estos sistemas se utilizan en áreas tan diversas como regulación de la temperatura en estancias habitadas, almacenamiento y transporte de alimentos y múltiples procesos industriales. Dado el considerable impacto causado por el consumo energético de estos sistemas en los balances económicos y medioambientales de los países desarrollados y en vías de desarrollo, y teniendo en cuenta la escasez creciente de fuentes de energía fósiles y el desarrollo todavía lento de las diferentes tecnologías de energía renovable, la operación óptima en términos de eficiencia energética de los sistemas de refrigeración por compresión de vapor existentes se presenta como un problema clave que abordar. Esta Tesis aborda la operación óptima de los ciclos de refrigeración desde el punto de vista de la eficiencia energética. Aunque el trabajo se centra principalmente en sistemas de una etapa de compresión y un recinto a refrigerar, se analizan también otras configuraciones con varias etapas y varios recintos. Existen varios factores clave para alcanzar la operación óptima de un sistema de refrigeración en el campo del Control Automático: el modelado, la optimización y el control propiamente dicho. En primer lugar, se estudia ampliamente el modelado estático y dinámico de los sistemas de refrigeración. En cuanto al segundo, se desarrolla un modelo dinámico simplificado y orientado al control de un ciclo de una etapa de compresión y un recinto a refrigerar. El objetivo es que pueda ser incorporado en estrategias de control basado en modelo, donde se requieren tanto una baja carga computacional como una descripción suficientemente precisa de la dinámica dominante del sistema, de acuerdo con los objetivos de control. En segundo lugar, se analiza la operación óptima en régimen permanente de un ciclo de una etapa de compresión y un recinto a refrigerar. Dada una cierta demanda de frío, el objetivo de la fase de optimización es calcular el ciclo en régimen permanente que alcanza la máxima eficiencia energética posible asegurando la satisfacción de la demanda de frío y a la vez respetando las restricciones de operación. Una vez calculado, se pretende que este ciclo óptimo constituya la referencia a seguir por parte del controlador. Finalmente, se estudia asimismo el problema de control. En la literatura sobre sistemas de refrigeración se encuentran principalmente dos esquemas: el control convencional y el control centrado en la eficiencia energética. En el primer esquema, además de la referencia impuesta por la demanda de frío, se impone un valor bajo pero constante como referencia para el grado de sobrecalentamiento del refrigerante a la salida del evaporador, to achieve the cycle defined by the optimization stage by manipulating the available control actions. Therefore, the controllability of the one-stage, one-load-demand cycle is analysed using linear theory and a nonlinear pointwise analysis based on the phase portrait method. Given the conclusions of the controllability analysis, a suboptimal hierarchical control strategy is proposed to achieve the highest possible efficiency while satisfying the cooling load. Most contributions of this Thesis are of theoretical nature. Notwithstanding, the application of the proposed control strategy to a multi-compression-stage, multi-loaddemand experimental plant is intended. Then, steady-state identification of the plant is performed from experimental data, whereas validation of the models considering different plant configurations is also carried out.Premio Extraordinario de Doctorado U

Model free real-time optimization for vapor compression systems

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

Predictive control, a way to optimize energy use in buildings

211 p.The energy requirements of our globalized world carries out interdependence conditions and instability in certain areas. In developed societies, buildings expend about 40% of energy, and half it is used by the building climate systems to ensure thermal comfort. It must also be remarked that more than a half of the consumed power in Europa is imported. For these reasons, the reduction of the amount of power used in buildings has become an objective for the European power policy. This PhD Thesis studies two specific problems dealing them from a control perspective. The first problem is given by the big stock of aged poor quality residential blocks that are still in some districts of modern cities. The use of a Model Predictive Control (MPC) allows using the weather forecast to reduce the energy use ensuring thermal comfort. Results of the study show potential energy savings about 9 % when compared with the ones of a thermostatic control. The use of an MPC with a Time of Use (TOU) power rate shows also important economic benefits although there is no energy savings.The second problem that handles this PHD Thesis is to improve the blind system control of an office building in order to reduce the energy required need to maintain the comfort conditions in the offices. The analysis is checked with the two-office module found in the literature. This module is made of high quality materials, north-south oriented, and climatized by an MPC-controlled main TABS and auxiliary AHU systems. The novel enhanced blind system control uses the information of the required energy use foreseen by the MPC to regulate the blind system. Obtained results show a potential energy use reduction of 15 % in South oriented office when the novel system is compared with a hysteresis blind system control

Force sensors for active safety, stability enhancement and lightweight construction of road vehicles

Force and moment measurement at different locations within road vehicles is a multifaceted, comprehensive and forthcoming technology that might play a breakthrough role in automotive engineering. The paper aims to describe why such technology seems so promising. A literature review is accomplished on which forces can be measured and what can be obtained with force and moment data. Additionally, attention is devoted to where–and how–force and moments can be measured effectively. Force and moment measurement technology is also studied with an historical perspective, briefly analysing the past applications. Active safety systems (ADAS up to full automated driving) and automotive stability enhancement systems are expected to be impacted by the measurement of forces and moments at the wheels. Friction potential evaluation and driver model development and monitoring have been–and are expected to be–major field of research. Force and moment measurement technology may also be exploited for lightweight construction purposes with remarkable synergistic effects with active safety and stability enhancement systems. Possible innovations on lightweight construction and sustainable mobility are to be expected thanks to force and moment measurement

Roadmap to Resilient Ultra-Low Energy Built Environment with Deep Integration of Renewables in 2050: Proceedings, Montreal Symposium

Conference proceedings of the Montreal Symposium "Roadmap to Resilient Ultra-Low Energy Built Environment with Deep Integration of Renewables in 2050", held online on October 16, 2020