A Unified MPC Formulation for Control of Commercial HVAC Systems in Multiple Climate Zones

Model predictive control (MPC) has been widely investigated for climate control of commercial buildings for both energy efficiency and demand flexibility. However, most MPC formulations ignore humidity and latent heat. The inclusion of moisture makes the problem considerably more challenging, primarily since a cooling and dehumidifying coil model which accounts for both sensible and latent heat transfers is needed. In our recent work, we proposed an MPC controller in which humidity and latent heat were incorporated in a principled manner, by using a reduced-order model of the cooling coil. Because of the highly nonlinear nature of the process in a cooling coil, the model needs to be modified based on certain weather/climatic conditions to have sufficient prediction accuracy. Doing so, however, leads to a mixed-integer nonlinear program (MINLP) that is challenging to solve. In this work, we propose an MPC formulation that retains the NLP (nonlinear programming problem) structure in all climate zones/weather conditions. This feature makes the control system capable of autonomous operation. Simulations in multiple climate zones and weather conditions verify the energy savings performance, and autonomy of the proposed controller. We also compare the performance of the proposed MPC controller with an MPC formulation that does not explicitly consider humidity. Under certain conditions, it is found that the MPC controller that excludes humidity leads to poor humidity control, or higher energy usage as it is unaware of the latent load on the cooling coil

INDOOR ENVIRONMENTAL QUALITY (IEQ) AND BUILDING ENERGY OPTIMIZATION THROUGH MODEL PREDICTIVE CONTROL (MPC)

This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings’ thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These include thermal comfort control based on ASHRAE comfort zone (based on temperature and relative humidity) and Predicted Mean Vote (PMV) and ventilation control based on ASHRAE 62.1 and Demand Control Ventilation (DCV). The building energy consumption was also evaluated with and without integrated lighting and window blind control. The simulation results revealed better performance of MPC both in terms of energy savings as well as maintaining acceptable indoor environmental quality. Energy saving as high as 48% was possible using MPC with integrated lighting and window blind control. A new critical contaminant - based demand control ventilation strategy was also developed to ensure acceptable or higher indoor air quality. Common indoor and outdoor contaminants were considered in the study and the method resulted in superior performance especially for buildings with strong indoor or outdoor contaminant sources compared to conventional CO2 - based demand control ventilation which only monitors CO2 to vary the minimum outdoor air ventilation rate

Experimental Demonstration of Model Predictive Control in a Medium-Sized Commercial Building

This paper presents the implementation and experimental demonstration results of a practically effective and computationally efficient model predictive control (MPC) algorithm used to optimize the energy use of the heating, ventilation, and air-conditioning (HVAC) system in a multi-zone medium-sized commercial building. Advanced building control technologies are key enablers for intelligent operations of future buildings, however, adopting these technologies are quite difficult in practice mainly due to the cost-sensitive nature of the building industry. This paper presents the results of implementing optimization-based control algorithm and demonstrates the effectiveness of its energy-saving feature and improved thermal comfort along with lessons-learned. The performance of the implemented MPC algorithm was estimated relative to baseline days (heuristic-based control) with similar outdoor air temperature patterns during the cooling and shoulder seasons (September to November, 2013), and it was concluded that MPC reduced the total electrical energy consumption by more than 20% on average while improving thermal comfort in terms of temperature and maintaining similar zone CO2 levels

Energy Conservation in an Office Building Using an Enhanced Blind System Control

The two spaces office module is usually considered as a representative case-study to analyse the energetic improvement in office buildings. In this kind of buildings, the use of a model predictive control (MPC) scheme for the climate system control provides energy savings over 15% in comparison to classic control policies. This paper focuses on the influence of solar radiation on the climate control of the office module under Belgian weather conditions. Considering MPC as main climate control, it proposes a novel distributed enhanced control for the blind system (BS) that takes into account part of the predictive information of the MPC. In addition to the savings that are usually achieved by MPC, it adds a potential 15% improvement in global energy use with respect to the usually proposed BS hysteresis control. Moreover, from the simulation results it can be concluded that the thermal comfort is also improved. The proposed BS scheme increases the energy use ratio between the thermally activated building system (TABS) and air-handling unit (AHU); therefore increasing the use of TABS and allowing economic savings, due to the use of more cost-effective thermal equipment.This work was supported in part by the University of the Basque Country (UPV/EHU) through Project GIU14/07 and by the Basque Government through Project IT987-16, as well as by the MINECO through the Research Project DPI2015-70075-R (MINECO/FEDER). The work of Bram van der Heijde is funded by the European Union, the European Regional Development Fund ERDF, Flanders Innovation & Entrepreneurship and the Province of Limburg (Belgium) through the project EFRO Project 936 "Towards a Sustainable Energy Supply in Cities". The authors also want to recognize Maarten Sourbron, Damien Picard and Stefan Antonov from The SySis of KU Leuven for their support in the investigation

Responsive Building Envelope for Grid-Interactive Efficient Buildings – Thermal Performance and Control

The building sector accounts for 30% of total energy consumption worldwide. Responsive building envelopes (or RBEs) are one of the approaches to achieving net-zero energy and grid-interactive efficient buildings. However, research and development of RBEs are still in the early stages of technologies, simulation, control, and design. The control strategies in prior studies did not fully explore the potential of RBEs or they obtained good performance with high design and deployment costs. A low-cost strategy that does not require knowledge of complex systems is needed, while no studies have investigated online implementations of model-free control approaches for RBEs. To address these challenges, this dissertation describes a multidisciplinary study of the modeling, control, and design of RBEs, to understand mechanisms governing their dynamic properties and synthesis rules of multiple technologies through simulation analyses. Widely applicable mathematical models are developed that can be easily extended for multiple RBE types with validation. Computational frameworks (or co-simulation testbeds) that flexibly integrate multiple control methods and building simulation models are established with higher computation efficiency than that using commercial software during offline training. To overcome the limitations of the control strategies (e.g., rule-based control and MPC) in prior research, a novel easy-to-implement yet flexible ‘demand-based’ control strategy, and model-free online control strategies using deep reinforced learning are proposed for RBEs composed of active insulation systems (AISs). Both the physics-derived and model-free control strategies fully leverage the advantages of AISs and provide higher energy savings and thermal comfort improvement over traditional temperature-based control methods in prior research and demand-based control. The case studies of RBEs that integrate AISs and high thermal mass or self-adaptive/active modules (e.g., evaporative cooling techniques and dynamic glazing/shading) demonstrate the superior performance of AISs in regulating thermal energy transfer to offset AC demands during the synergy. Moreover, the controller design and training implications are elaborated. The applicability assessment of promising RBE configurations is presented along with design implications based on building energy analyses in multiple scenarios. The design and control implications represent an interactive and holistic way to operate RBEs allowing energy and thermal comfort performances to be tuned for maximum efficiency

Model predictive control for energy efficient cooling and dehumidification

Thesis (Ph. D. in Building Technology)--Massachusetts Institute of Technology, Dept. of Architecture, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 147-154).Energy has become a primary concern in countries worldwide, and is a focus of debates on national security, climate change, global economy, and the developing world. With more people in developing countries adopting the lifestyle of western countries as rapidly as possible, limited only by economic means, a tremendous increase in world's energy consumption in the next few decades seems difficult to avoid. The building sector is of particular interest, since it accounts for a large portion of the total energy market: currently in the U.S. forty percent of the total energy and seventy percent of electricity is consumed by residential and commercial buildings. Within commercial buildings, cooling equipment represents the second largest consumer of electricity. This research analyzes one option for reducing space cooling energy consumption, an advanced cooling system termed low-lift cooling system (LLCS). The system comprises thermally activated building surfaces (TABS) with water running through pipes embedded in a building's construction to serve both as cool storage and as a means of delivering the cooling effect. The LLCS utilizes model predictive control (MPC) algorithm that, based on weather and load predictions, determines the cooling strategy over next 24 hours that minimizes energy consumption. Different objectives, such as minimizing the total cost of electricity, can be achieved by modifying the objective function. Currently there is no commercially or publicly available software that allows the analysis of systems that employ MPC. The first goal of this research was to develop a computer algorithm that can simulate the LLCS performance, but also the performance of other cooling systems that employ MPC. The second goal was to analyze the LLCS performance across different U.S. climates relative to a conventional cooling system and to explore different dehumidification strategies that can be used in combination with the LLCS. This research significantly advances the knowledge of simulation and performance of the LLCS. The developed MPC algorithm enables a systematic study of primary factors influencing dynamic controls and the savings potential for an individual building. The algorithm is highly modular, enabling easy future expansion, and is sufficiently fast and robust for an implementation real buildings. The results of the analysis suggest that the electricity savings using the LLCS are up to 50% relative to an all-air system under conventional control and up to 23% relative to an all-air system under MPC. The savings were achieved through lower fan and pump transport energy and better utilization of part-load efficiencies inherent in inverter-compressor equipment, a result of the TABS technology and the optimal control.by Tea Zakula.Ph.D.in Building Technolog

Energy Intensity Reduction in Large-Scale Non-Residential Buildings by Dynamic Control of HVAC with Heat Pumps

One of the main elements for increasing energy efficiency in large-scale buildings is identified in the correct management and control of the Heating Ventilation and Air Conditioning (HVAC) systems, particularly those with Heat Pumps (HPs). The present study aimed to evaluate the perspective of energy savings achievable with the implementation of an optimal control of the HVAC with HPs. The proposed measures involve the use of a variable air volume system, demand-controlled ventilation, an energy-aware control of the heat recovery equipment, and an improved control of the heat pump and chiller supply water temperature. The analysis has been applied to an academic building located in Pisa and is carried out by means of dynamic simulation. The achieved energy saving can approach values of more than 80% if compared with actual plants based on fossil fuel technologies. A major part of this energy saving is linked to the use of heat pumps as thermal generators as well as to the implementation of an energy efficient ventilation, emphasizing the importance of such straightforward measures in reducing the energy intensity of large-scale buildings

Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand

Mathematical models of vapor-compression systems for multivariable control of the refrigerant dynamics and indoor air conditions

Thesis (PhD (Electronic Engineering))--University of Pretoria, 2023.Detailed steady and transient state models of vapor compression (VC) systems have been suggested in this work so that the governing parameters of the refrigerant dynamics such as pressure, enthalpy and temperature could be predicted at different operating conditions. The steady and transient state models were validated with experimental data collected during startup and steady state operations. The experimental setup was equipped with a thermostatic expansion valve, a reciprocal compressor and plate heat exchangers for the condenser and evaporator. Recirculated water was adopted as secondary fluid for heat transfer with R-134a refrigerant. The steady state model was developed from first principles with the refrigerant conditions being determined at each junction between the components of the VC system. A steady state matrix was built to determine the model outputs and it could be adopted for similar problems such as steady state modelling of single-condenser-and-multi-evaporators systems. The refrigerant pressures through the evaporator and condenser were in agreement with experiments. Other refrigerant conditions such as enthalpy and temperature through the components were also validated with experiments. The evaporator and condenser modelling in transient state required special attention and Navier-Stokes equations were adopted for this purpose along with a finite volume scheme for discretization of the condenser and evaporator into 3 and n-control volumes. A transient state matrix was also built for outputs’ prediction in transient operating conditions such as startup and shutdown. The refrigerant conditions namely pressure and enthalpy through the evaporator and condenser were validated with experiments. The transient state model was then improved and converted into a control-oriented model with 12 state variables. The control-oriented model considered phase change in the condenser and evaporator namely, superheat, two-phase and subcooling. Model predictive control (MPC) was implemented on the control-oriented model after a model linearization around a steady state point carefully selected from the steady state experiments performed for validation of the steady state modelling. MPC implementation enabled to control superheat and evaporating pressure simultaneously with consideration of the coupling effect between superheat and capacity regulation. MPC was integrated in Simulink with satisfactory performances regarding disturbance rejection and reference tracking. Building up on satisfactory MPC performance for multivariable control of the refrigerant dynamics, a proportional integral derivative (PID)-MPC controller was implemented on a Chiller-Fan coil unit (FCU) to control simultaneously, indoor temperature, humidity and CO2 level with the coupling effect between humidity and temperature taken into consideration. PID was implemented on a sub layer control loop located at the first heat exchanger and fresh air temperature was maintained within settings to level-out with room temperature to prevent from imbalanced loads. Disturbance rejection and set point tracking were satisfactory without necessarily increasing the supply fan and compressor speeds. MPC was implemented on an upper layer control loop located at a secondary heat exchanger to regulate simultaneously indoor humidity, temperature and CO2 level. The coupling effect between humidity and temperature was well taken care of by the MPC loop and CO2 level regulation was performed without additional load as fresh air intake was carefully pre-cooled using the primary heat exchanger controlled with a PID loop. The performance of the sub layer PID was satisfactory with regards to stability, maximum overshoot and settling time whilst reference tracking and disturbance rejection were satisfactory with the upper layer MPC.Electrical, Electronic and Computer EngineeringPhD (Electronic Engineering)Unrestricte