Power peak shaving with data transmission delays for thermal management in smart buildings

This paper presents a scheme aimed at mitigating the influence of random data transmission delays in networked thermal appliance control systems in smart buildings. The impact of this type of delays is first analyzed, and it is proposed to utilize loose timing synchronization and add blank gaps between the consecutive appliance operations to avoid the possible violation of the given power budget. A cooperative control of thermal appliance operation is developed using a networked Model Predictive Control (MPC)-based controller to deal with delays. It is also shown that the schedulability of such a control scheme can be assessed online. The performance of the proposed control scheme is assessed by a simulation study based on the thermal dynamics of an eight-room office building. The obtained results show that the proposed solution can achieve an efficient power peaks shaving in the presence of random network delays

Game-theoretic decentralized model predictive control of thermal appliances in discrete-event systems framework

This paper presents a decentralized model predictive control (MPC) scheme for thermal appliances coordination control in smart buildings. The general system structure consists of a set of local MPC controllers and a game-theoretic supervisory control constructed in the framework of discrete-event systems (DES). In this hierarchical control scheme, a set of local controllers work independently to maintain the thermal comfort level in different zones, and a centralized supervisory control is used to coordinate the local controllers according to the power capacity and the current performance. Global optimality is ensured by satisfying the Nash equilibrium at the coordination layer. The validity of the proposed method is assessed by a simulation experiment including two case studies. The results show that the developed control scheme can achieve a significant reduction of the peak power consumption while providing an adequate temperature regulation performance if the system is P-observable

Scheduling distributed energy resources and smart buildings of a microgrid via multi-time scale and model predictive control method

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted

Commande prédictive désynchronisée pour le contrôle d'une grande population de systèmes thermostatiques

RÉSUMÉ Ce mémoire porte sur la modélisation et sur le contrôle d’une grande population de systèmes thermostatiques (TCLs) contrôlés individuellement par une commande prédictive. Le contrôle d’une grande population de systèmes o˙re beaucoup d’opportunités telles que le contrôle en fréquence, le suivi de charges, l’équilibrage énergétique qui peut contribuer à l’amélioration de la stabilité du réseau électrique. Les TCLs sont aussi un moyen d’absorber la production fluctuante d’énergie renouvelable générée par des éoliennes, fermes de panneaux solaires. De plus, la plupart des TCLs, comme les chau˙ages, les climatiseurs, les chau˙e-eaux, les réfrigé-rateurs, ont une consommation d’énergie flexible et élastique en termes de performances. Les TCLs sont considérés comme des éléments importants pour gérer la régulation de la charge, et plus particulièrement peuvent jouer un rôle majeur pour réduire la consommation de pointe et combler les creux de consommation. Ils sont aussi des éléments d’ajustement dans le cadre d’une tarification dynamique de l’énergie dans un réseau électrique intelligent. Le contrôle d’une grande population de systèmes thermostatiques est un problème qui est abordé depuis longtemps et qui continue d’attirer l’attention des chercheurs dans la littérature actuelle. Un des défis majeurs du contrôle d’une grande population de TCLs est la synchronisation des appareils entre eux. Un tel phénomène peut apparaître après une panne de courant, et cela implique des pics de puissance et des oscillations de puissance dans le réseau. Pour aborder ce problème, ce mémoire développe deux méthodes décentralisées qui vont hétérogénéiser individuellement le processus de prise de décision des MPC. Ces deux méthodes consistent à ajouter un délai aléatoire dans la trajectoire de référence et pénaliser aléatoirement la fonction objectif du MPC. Ces méthodes ont été validées dans le contexte du contrôle des ventilateurs de serveurs dans les centres de données. Typiquement, un centre de données est construit à des fins commerciales et abritent des centaines voire des milliers d’étagères pour stocker les serveurs informatiques, qui elles-mêmes peuvent contenir des dizaines de serveurs, ce qui représente une grande population homogène de TCLs. Un modèle thermique dynamique permet de représenter le comportement thermique à l’intérieur des serveurs, et un contrôleur MPC décentralisé permet le contrôle de la température de ceux-ci. Pour ca-ractériser la désynchronisation des TCLs contrôlés par MPC, un modèle composé d’une paire d’équations de transport semi-linéaires couplées est utilisé, en plus des simulations de Monte-Carlo. Les simulations numériques montrent que le comportement global obtenu grâce à cette paire d’équations di˙érentielles correspond aux résultats générés par les simulations de Monte-Carlo. Ceci confirme la validité de l’approche utilisée.----------ABSTRACT This thesis addresses the modeling and control of large populations of thermostatically con-trolled loads (TCLs) operated by model predictive control (MPC) schemes at the level of each TCL. Aggregates of large populations of TCLs can be managed to offer auxiliary services, such as frequency control, load following, and energy balancing, which can contribute to maintaining the overall stability of power networks. TCLs can also provide a means for absorbing the fluctuations of renewable energy generated by, e.g., wind turbines and solar photovoltaic plants. Moreover, due to the fact that most of the TCLs, including space heaters, air conditioners, hot water tanks, and refrigerators, exhibit flexibilities in power demand for their operation and elasticities in terms of performance restrictions, they are considered to be one of the most important Demand Response (DR) resources that can provide such features as power peak shaving and valley filling and enable dynamic pricing schemes in the context of the Smart Grid. Indeed, control of aggregated TCL populations is a long-time standing problem, which continues to attract much attention in the recent literature. A critical issue in the operation of a large population of TCLs is the occurrence of synchronization due to the phenomenon of cold load pickup, which may result in high power demand peaks and load oscillations. To tackle this problem, this thesis developed two fully decentralized schemes that would randomize the decision-making process of the MPC individually by each TCL, namely adding random delays in reference signal and extra penalizations on MPC objective functions. The proposed control schemes are validated in the context of the operation of fans in server enclosures in datacenters. Typically, data centers are built from general purpose commercially available o˙-the-shelf (COTS) processors. A data center may have hundreds or even thousands of server racks; each may host several tens of server enclosures, which represents a large population of homogenous TCLs. The thermal dynamics of the fans has been established, and a decentralized MPC control scheme has been designed for the control of a large population of fans. To characterize desynchronized MPC-based TCLs control schemes, a model governed by a pair of coupled semi-linear transport equations for describing the dynamic behavior of the population has been developed, in addition to Monte-Carlo simulations. Numerical simulation studies show that the aggregate behavior derived from this partial differential equation (PDE) model fits well with the results generated by the Monte-Carlo simulation. This confirmed the validity of the proposed approach

Application of heat pumps and thermal storage systems for improved control and performance of microgrids

The high penetration of renewable energy sources (RES), in particular, the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. Residential and commercial buildings have considerable potential for providing load exibility by exploiting energy-e_cient devices like ground source heat pump (GSHP). The proper integration of PV systems with the GSHP could reduce power demand from demand-side. This research provides a practical attempt to integrate PV systems and GSHPs e_ectively into buildings and the grid. The multi-directional approach in this work requires an optimal control strategy to reduce energy cost and provide an opportunity for power trade-o_ or feed-in in the electricity market. In this study, some optimal control models are developed to overcome both the operational and technical constraints of demand-side management (DSM) and for optimum integration of RES. This research focuses on the development of an optimal real-time thermal energy management system for smart homes to respond to DR for peak-load shifting. The intention is to manage the operation of a GSHP to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the thermal energy storage (TES) while optimising the operation of the heat distributors to control indoor temperature. This thesis proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), and thermal and electrical energy storage systems. The results of this research demonstrate to rooftop PV system owners that investment in combined TSS and battery can be more profitable as this system can minimise life cycle costs. This thesis also presents an analysis of the potential impact of residential HP systems into reserve capacity market. This research presents a business aggregate model for controlling residential HPs (RHPs) of a group of houses that energy aggregators can utilise to earn capacity credits. A control strategy is proposed based on a dynamic aggregate RHPs coupled with TES model and predicting trading intervals capacity requirements through forecasting demand and non-scheduled generation. RHPs coupled with TES are optimised to provide DSM reserve capacity. A rebound effect reduction method is proposed that reduces the peak rebound RHPs power

Model Predictive Control for Demand Response Management Systems in Smart Buildings

RÉSUMÉ Les bâtiments représentent une portion importante de la consommation énergétique globale. Par exemple, aux USA, le secteur des bâtiments est responsable de 40% de la consommation énergétique totale. Plus de 50% de la consommation d’électricité est liée directement aux systèmes de chauffage, de ventilation et de climatisation (CVC). Cette réalité a incité beaucoup de chercheurs à développer de nouvelles solutions pour la gestion de la consommation énergétique dans les bâtiments, qui impacte la demande de pointe et les coûts associés. La conception de systèmes de commande dans les bâtiments représente un défi important car beaucoup d’éléments, tels que les prévisions météorologiques, les niveaux d’occupation, les coûts énergétiques, etc., doivent être considérés lors du développement de nouveaux algorithmes. Un bâtiment est un système complexe constitué d’un ensemble de sous-systèmes ayant différents comportements dynamiques. Par conséquent, il peut ne pas être possible de traiter ce type de systèmes avec un seul modèle dynamique. Récemment, différentes méthodes ont été développées et mises en application pour la commande de systèmes de bâtiments dans le contexte des réseaux intelligents, parmi lesquelles la commande prédictive (Model Predictive Control - MPC) est l’une des techniques les plus fréquemment adoptées. La popularité du MPC est principalement due à sa capacité à gérer des contraintes multiples, des processus qui varient dans le temps, des retards et des incertitudes, ainsi que des perturbations. Ce projet de recherche doctorale vise à développer des solutions pour la gestion de consommation énergétique dans les bâtiments intelligents en utilisant le MPC. Les techniques développées pour la gestion énergétique des systèmes CVC permet de réduire la consommation énergétique tout en respectant le confort des occupants et les contraintes telles que la qualité de service et les contraintes opérationnelles. Dans le cadre des MPC, différentes contraintes de capacité énergétique peuvent être imposées pour répondre aux spécifications de conception pendant la durée de l’opération. Les systèmes CVC considérés reposent sur une architecture à structure en couches qui réduit la complexité du système, facilitant ainsi les modifications et l’adaptation. Cette structure en couches prend également en charge la coordination entre tous les composants. Étant donné que les appareils thermiques des bâtiments consomment la plus grande partie de la consommation électrique, soit plus du tiers sur la consommation totale d’énergie, la recherche met l’emphase sur la commande de ce type d’appareils. En outre, la propriété de dynamique lente, la flexibilité de fonctionnement et l’élasticité requise pour les performances des appareils thermiques en font de bons candidats pour la gestion réponse à la demande (Demand Response - DR) dans les bâtiments intelligents.----------ABSTRACT Buildings represent the biggest consumer of global energy consumption. For instance, in the US, the building sector is responsible for 40% of the total power usage. More than 50% of the consumption is directly related to heating, ventilation and air-conditioning (HVAC) systems. This reality has prompted many researchers to develop new solutions for the management of HVAC power consumption in buildings, which impacts peak load demand and the associated costs. Control design for buildings becomes increasingly challenging as many components, such as weather predictions, occupancy levels, energy costs, etc., have to be considered while develop-ing new algorithms. A building is a complex system that consists of a set of subsystems with di˙erent dynamic behaviors. Therefore, it may not be feasible to deal with such a system with a single dynamic model. In recent years, a rich set of conventional and modern control schemes have been developed and implemented for the control of building systems in the context of the Smart Grid, among which model redictive control (MPC) is one of the most-frequently adopted techniques. The popularity of MPC is mainly due to its ability to handle multiple constraints, time varying processes, delays, uncertainties, as well as disturbances. This PhD research project aims at developing solutions for demand response (DR) man-agement in smart buildings using the MPC. The proposed MPC control techniques are im-plemented for energy management of HVAC systems to reduce the power consumption and meet the occupant’s comfort while taking into account such restrictions as quality of service and operational constraints. In the framework of MPC, di˙erent power capacity constraints can be imposed to test the schemes’ robustness to meet the design specifications over the operation time. The considered HVAC systems are built on an architecture with a layered structure that reduces the system complexity, thereby facilitating modifications and adaptation. This layered structure also supports the coordination between all the components. As thermal appliances in buildings consume the largest portion of the power at more than one-third of the total energy usage, the emphasis of the research is put in the first stage on the control of this type of devices. In addition, the slow dynamic property, the flexibility in operation, and the elasticity in performance requirement of thermal appliances make them good candidates for DR management in smart 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