Dissertations / Theses on the topic 'Optimization of HVAC energy consumption'

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.
M.S.
The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

The primary purpose of the current research was to implement a numerical model to investigate the interactions between the energy consumption in Heating, Ventilating, and Air Conditioning (HVAC) systems and occupants’ thermal comfort in commercial buildings. A numerical model was developed to perform a thermal analysis of a single zone and simultaneously investigate its occupants’ thermal sensations as a non-linear function of the thermal environmental (i.e. temperature, thermal radiation, humidity, and air speed) and personal factors (i.e. activity and clothing). The zone thermal analyses and thermal comfort calculations were carried out by applying the heat balance method and current thermal comfort standard (ASHRAE STANDARD 55-2004) respectively. The model was then validated and applied on a single generic zone, representing the perimeter office spaces of the Centre for Interactive Research on Sustainability (CIRS), to investigate the impacts of variation in occupants’ behaviors, building’s envelope, HVAC system, and climate on both energy consumption and thermal comfort. Regarding the large number of parameters involved, the initial summer and winter screening analyses were carried out to determine the measures that their impacts on the energy and/or thermal comfort were most significant. These analyses showed that, without any incremental cost, the energy consumption in both new and existing buildings may significantly be reduced with a broader range of setpoints, adaptive clothing for the occupants, and higher air exchange rate over the cooling season. The effects of these measures as well as their combination on the zone thermal performance were then studied in more detail with the whole year analyses. These analyses suggest that with the modest increase in the averaged occupants’ thermal dissatisfaction, the combination scenario can notably reduce the total annual energy consumption of the baseline zone. Considering the global warming and the life of a building, the impacts of climate change on the whole year modeling results were also investigated for the year 2050. According to these analyses, global warming reduced the energy consumption for both the baseline and combination scenario, thanks to the moderate and cold climate of Vancouver.

Smart home automation is the dwelling bridge of smart grid technology, as it integrates the modern home appliances power consumption information over communication networks in the smart grid system. Among all the appliances, Heating, Ventilation and Cooling (HVAC) systems is one of the most primary concerns. Since a great amount of power consumption is contributed by these HVAC systems. Traditionally, HVAC systems run at a fixed schedule without automatic monitoring and control systems, which causes load variation, fluctuations in the electricity demand and inefficient utility operation. In this thesis, we propose a Finite State Machine (FSM) system to model the air condition working status to acquire the relationship between temperature changing and cooling/heating duration. Finally, we introduce the Zigbee communciation protocol into the model, the performance analysis of the impact of end-to-end delay over HVAC systems is presented.

This master thesis report has studied several methods to improve the energy consumption of an electric vehicle equipped with two permanent magnet synchronous motors. Two driving torque distribution maps are developed based on efficiency map and load transfer, respectively. The drive torque distribution map based on the efficiency map shows up to 8.94% energy saving. Two regenerative braking strategies are designed and compared. Both strategies have pure regenerative brake at low decelerations and it is controlled by a modified acceleration pedal map. Strategy 1 does not add more regenerative braking when the brake pedal is pressed thus it is simpler while strategy 2 can blend in more motor torque. Rear axle steering is also studied in terms of contribution to energy consumption and an LQR controller is developed to control the vehicle with rear axle steering.
Denna rapport avhandlar ett examensarbete där flera metoder har studerats för att förbättra energikonsumptionen för ett elektriskt fordon med två permanentmagnetsynkrona motorer. Två fördelningskartor för drivande moment är framtagna baserat på effektivitetskartor och lastöverföring. Fördelningskartorna för drivande moment som är baserat på effektivitet visar upp till 8,94% energiminskning. Två olika regenerativa bromsstrategier är framtagna och jämförda. Båda strategierna har ren regeneration vid låga decelerationer och är reglerat genom modifierat gaspedalsmappning. Strategi 1 ger inte mer regeneration när bromspedalen trycks ned och är då enklare medans strategi 2 kan blanda in mer vridmoment från elmotorn. Bakaxelstyrning är också studerat i termer av dess bidrag till energikonsumption samt en LQR regulator är utvecklad för reglering av fordonets bakaxelstyrning.

This study addresses 5 uncertainties that exist in the operation of HVAC systems, which will presumably affect the actual energy consumption of the HVAC system in comparison to the consumption under idealized bahavior. We consequently add these parameters and their uncertainty range into the source code, eventually resulting in an EnergyPlus program in which the HVAC operation uncertainty is embedded as so-called model form uncertainty. The upgraded EnergyPlus is tested for each parameter uncertainty separately, and to show the impact of each uncertainty albeit for hypothetical uncertainty ranges of the parameters.

Heating, ventilating and air-conditioning (HVAC) system is complex non-linear system with multi-variables simultaneously contributing to the system process. It poses challenges for both system modeling and performance optimization. Traditional modeling methods based on statistical or mathematical functions limit the characteristics of system operation and management. Data-driven models have shown powerful strength in non-linear system modeling and complex pattern recognition. Sufficient successful applications of data mining have proved its capability in extracting models accurately describing the relation of inner system. The heuristic techniques such as neural networks, support vector machine, and boosting tree have largely expanded to the modeling process of HVAC system. Evolutionary computation has rapidly merged to the center stage of solving the multi-objective optimization problem. Inspired from the biology behavior, it has shown the tremendous power in finding the optimal solution of complex problem. Different applications of evolutionary computation can be found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply the evolutionary computation approach in optimizing the performance of HVAC system. The energy saving can be achieved by implementing the optimal control setpoints with IAQ maintained at an acceptable level. A trade-off between energy saving and indoor air quality maintenance is also investigated by assigning different weights to the corresponding objective function. The major contribution of this research is to provide the optimal settings for the existing system to improve its efficiency and different preference-based operation methods to optimally utilize the resources.

HVAC (Heating Ventilating and Air-Conditioning) system is multivariate, nonlinear, and shares time-varying characteristics. It poses challenges for both system modeling and performance optimization. Traditional modeling approaches based on mathematical equations limit the nature of the optimization models and solution approaches. Computational intelligence is an emerging area of study which provides powerful tools for modeling and analyzing complex systems. Computational intelligence is concerned with discovery of structures in data and recognition of patterns. It encompasses techniques such as neural networks, fuzzy logic, and so on. These techniques derive rules, patterns, and develop complex mappings from the data. The recent advances in information technology have enabled collection of large volumes of data. Computational intelligence embraces biology-inspired paradigms like evolutionary computation and particle swarm intelligence in solving complex optimization problems. Successful applications of computational intelligence have been found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply computational intelligence approach in modeling and optimization of HVAC systems. In this research, four HVAC sub-systems are investigated: the AHU (Air Handling Unit), VAV (Variable Air Volume), ventilation system, and thermal zone. Various computational intelligence approaches are used to identify parameters or problem solving. Energy savings are accomplished by minimizing the cooling output, reheating output or fan running time as well as on-line monitoring. One contribution of the research reported in the thesis is the use of computational intelligence algorithms to establish nonlinear mappings among different parameters. Another major contribution is in using heuristics algorithms to solve multi-objective optimization problems.

During the last 10 years solar cooling systems attracted more and more interest not only in the research area but also on a private and commercial level. Several demonstration plants have been installed in different European countries and first companies started to commercialise also small scale absorption cooling machines. However, not all of the installed systems operate efficiently and some are, from the primary energy point of view, even worse than conventional systems with a compression chiller. The main reason for this is a poor system design combined with suboptimal control. Often several non optimised components, each separately controlled, are put together to form a ‘cooling system’. To overcome these drawbacks several attempts are made within IEA task 38 (International Energy Agency Solar Heating and Cooling Programme) to improve the system design through optimised design guidelines which are supported by simulation based design tools. Furthermore, guidelines for an optimised control of different systems are developed. In parallel several companies like the SolarNext AG in Rimsting, Germany started the development of solar cooling kits with optimised components and optimised system controllers. To support this process the following contributions are made within the present work: - For the design and dimensioning of solar driven absorption cooling systems a detailed and structured simulation based analysis highlights the main influencing factors on the required solar system size to reach a defined solar fraction on the overall heating energy demand of the chiller. These results offer useful guidelines for an energy and cost efficient system design. - Detailed system simulations of an installed solar cooling system focus on the influence of the system configuration, control strategy and system component control on the overall primary energy efficiency. From the results found a detailed set of clear recommendations for highly energy efficient system configurations and control of solar driven absorption cooling systems is provided. - For optimised control of open desiccant evaporative cooling systems (DEC) an innovative model based system controller is developed and presented. This controller consists of an electricity optimised sequence controller which is assisted by a primary energy optimisation tool. The optimisation tool is based on simplified simulation models and is intended to be operated as an online tool which evaluates continuously the optimum operation mode of the DEC system to ensure high primary energy efficiency of the system. Tests of the controller in the simulation environment showed that compared to a system with energy optimised standard control the innovative model based system controller can further improve the primary energy efficiency by 19 %.

Textile dyeing and finishing industry uses dryers/tenters for drying and heat-setting fabrics. A very large fraction of the heating value of the fuel consumed in the burner ends up as waste in the dryer exhaust. An initial calculation showed that up to 90% of the energy consumed in the tenter is wasted. Therefore, quantifying the energy waste and determining drying characteristics are vitally important to optimizing the tenter and dryer operations. This research developed a portable off-line gas chromatography-based characterization system to assess the excess energy consumption. For low-demanding heat-setting situations, energy savings can be realized quickly. On the other hand, there are demanding situations where fabric drying represents the production bottleneck. The drying rate may be governed either by the rate of heat transport or by the rate of moisture transport. A mathematical model is being developed that incorporates both these processes. The model parameters are being obtained from bench-scale dryer studies in the laboratories. The model will be validated using production scale data. This will enable one to predict optimization dryer operation strategies.

Thesis (M.S.)--West Virginia University, 2005.
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In HVAC (Heating, Ventilation and Air Conditioning) plants of medium-high cooling capacity, multiple-chiller systems are often employed. In such systems, chillers are independent of each other in order to provide standby capacity, operational exibility, and less disruption maintenance. However, the problem of an eciently managing of multiple-chiller systems is complex in many respects. In particular, the electrical energy consumption in the chiller plant markedly increases if the chillers are managed improperly, therefore signicant energy savings can be achieved by optimizing the chiller operations of HVAC systems. In this Thesis an unied method for Multi-Chiller Management optimization is presented, that deals simultaneously with the Optimal Chiller Loading and Optimal Chiller Sequencing problems. The main objective is that of reducing both power consumption and operative costs. The approach is based on a cooling load estimation algorithm, and the optimization step is performed by means of a multi-phase genetic algorithm, that provides an ecient and suitable approach to solve this kind of complex multi-objective optimization problem. The performance of the algorithm is evaluated by resorting to a dynamic simulation environment, developed in Matlab and Simulink, where the plant dynamics are accurately described. It is shown that the proposed algorithm gives superior performance with respect to standard approaches, in terms of both energy performance and load prole tracking.
Negli impianti HVAC di capacità frigorifera medio-grande vengono spesso impiegati sistemi con più refrigeratori di liquido (chiller) in parallelo. Il problema della gestione eciente di tali sistemi è complesso sotto diversi punti di vista. In particolare, il consumo di energia elettrica dell'impianto aumenta notevolmente allorché i refrigeratori siano gestiti scorrettamente. In questa Tesi viene presentato un metodo unicato per l'ottimizzazione della gestione di chiller in parallelo che risolve simultaneamente i problemi del carico ottimo e della sequenza ottima di accensioni/spegnimenti relativi ai refrigeratori. L'obiettivo principale è quello ridurre il consumo energetico ed abbassare i costi di esercizio. L'approccio si basa su un algoritmo di stima del carico frigorifero richiesto e l'ottimizzazione è realizzata attraverso l'impiego di un algoritmo genetico multi-fase; quest'ultimo fornisce un approccio eciente per risolvere questo genere di problema di ottimo multi-obiettivo. Le prestazioni dell'algoritmo sono valutate ricorrendo ad un ambiente di simulazione dinamico, sviluppato in Matlab e Simulink, dove le dinamiche del sistema sono accuratamente descritte. Si evince che l'algoritmo proposto fornisce prestazioni superiori, rispetto agli approcci standard, sia in termini di soddisfacimento del carico che di prestazione energetica.

Context: Recent trends show that there is a tremendous shift from IT companies following traditional methods by hosting their applications/systems in self-managed on premise data centers to using the so-called cloud data centers. Cloud computing has received immense popularity due to its architecture and the ease of usage. Due to this increase in demand and shift in practices, there has been a tremendous increase in number of data centers over a period, resulting in increase of energy consumption. In this thesis work, a research is carried out on optimizing the energy consumption of a typical cloud data center. OpenStack cloud computing software is chosen as the platform in this research. We have used live migration as a key aspect in this research. Objectives: In this research, our objectives are as follows: Design an OpenStack testbed to implement the migration of virtual machines. To estimate the energy consumption of the data center. To design a heuristic algorithm to evaluate the performance metrics and to optimize the overall energy consumption. Methods: We have used PowerAPI, a software tool to estimate the energy consumption of hosts as well as virtual machines. A heuristic algorithm is designed and implemented in an instrumental OpenStack testbed to optimize the energy consumption. Server consolidation and load balancing of virtual machines methodologies are used in the heuristic algorithm design. Our research is carried out against the functionality of Nova scheduler of OpenStack. Results: Results section describes the values of performance metrics yielded by carrying out the experiment. The obtained results showed that energy can be optimized significantly by modifying the way OpenStack nova scheduler can work. The experiment is carried out on vanilla OpenStack and OpenStack with the heuristic algorithm in place, In the second case, the nova scheduler algorithms are not used but the heuristic algorithm is used instead. The CPU utilization and CPU load were noticed to be higher than the metrics observed in case of OpenStack with nova scheduler. Energy consumption is observed to be lesser than the consumption in OpenStack design with nova scheduler. Conclusions: The research tells that energy consumption can be optimized significantly using desired algorithms without compromising the service quality it offers. However, the design impacts on CPU slightly as the metrics are observed to be higher when compared to that in case of OpenStack with nova scheduler. Although it won’t have noticeable impact on the system.

The objective of this thesis is to create an automatic water management system capable of optimizing the usage of warm water stored in two water tanks to reduce the monthly energy consumption of the instant water heater installed in a residential house. This system is called Water Mixing System (WMS). The two heat sources considered are: PV-T system and heat rejected by the air condition system. The PV-T system is a new technology that allows transformation of the sun radiation into both electricity and warm water, increasing the efficiency of the panel compared to either a common photovoltaic panel or solar collector. The air-conditioning heat source, instead, recovers the heat rejected by the condenser to the environment by employing a heat exchanger that stores the heat collected in the water tank.

Les grandes évolutions de la technique de systèmes embarqués au cours des dernières années ont permis avec succès la combinaison de la détection, le traitement des données, et diverses technologies de communication sans fil tout en un nœud. Les réseaux de capteurs sans fil (WSN) qui se composent d'un grand nombre de ces nœuds ont attiré l'attention du monde entier sur les établissements scolaires et les communautés industrielles, puisque leurs applications sont très répandues dans des domaines tels que la surveillance de l'environnement, le domaine militaire, le suivi des événements et la détection des catastrophes. En raison de la dépendance sur la batterie, la consommation d'énergie des réseaux de capteurs a toujours été la préoccupation la plus importante. Dans cet article, une méthode mixte est utilisée pour l'évaluation précise de l'énergie sur les réseaux de capteurs, ce qui inclut la conception d'un environnement de SystemC simulation base au niveau du système et au niveau des transactions pour l'exploration de l'énergie, et la construction d'une plate-forme de mesure d'énergie pour les mesures de nœud banc d'essai dans le monde réel pour calibrer et valider à la fois le modèle de simulation énergétique de nœud et le modèle de fonctionnement. La consommation d'énergie élaborée de plusieurs différents réseaux basés sur la plate-forme de nœud sont étudiées et comparées dans différents types de scénarios, et puis des stratégies globales d'économie d'énergie sont également données après chaque scénario pour les développeurs et les chercheurs qui se concentrent sur la conception des réseaux de capteurs efficacité énergétique. Un cadre de l'optimisation basée sur un algorithme génétique est conçu et mis en œuvre à l'aide de MATLAB pour les réseaux de capteurs conscients de l'énergie. En raison de la propriété de recherche global des algorithmes génétiques, le cadre de l'optimisation peut automatiquement et intelligemment régler des centaines de solutions possibles pour trouver le compromis le plus approprié entre la consommation d'énergie et d'autres indicateurs de performance. Haute efficacité et la fiabilité du cadre de la recherche des solutions de compromis entre l'énergie de nœud, la perte de paquets réseau et la latence ont été prouvés par réglage paramètres de l'algorithme CSMA / CA de unslotted (le mode non-beacon de IEEE 802.15.4) dans notre simulation basé sur SystemC via une fonction de coût de la somme pondérée. En outre, le cadre est également disponible pour la tâche d'optimisation basée sur multi-scénarios et multi-objectif par l'étude d'une application médicale typique sur le corps humain.

Energy is the vital source of life and it plays a key role in development of human society. Any living creature relies on a source of energy to exist. Similarly, machines require power to operate. Starting with Industrial Revolution, the modern life clearly depends on energy. We need energy for almost everything we do in our daily life, including transportation, agriculture, telecommunication, powering industry, heating, cooling and lighting our buildings, powering electric equipment etc. Global energy requirement is set to increase due to many factors such as rapid industrialization, urbanization, population growth, and growing demand for higher living standards. There is a variety of energy resources available on our planet and non-renewable fossil fuels have been the main source of energy ever since the Industrial Revolution. Unfortunately, unsustainable consumption of energy resources and reliance on fossil fuels has led to severe problems such as energy resource scarcity, global climate change and environmental pollution. The building sector compromising homes, public buildings and businesses represent a major share of global energy and resource consumption. Therefore, while buildings provide numerous benefits to society, they also have major environmental impacts. To build and operate buildings, we consume about 40 % of global energy, 25 % of global water, and 40 % of other global resources. Moreover, buildings are involved in producing approximately one third of greenhouse gas emissions. Today, the stress put on the environment by building sector has reached dangerous levels therefore urgent measures are required to approach buildings and to minimize their negative impacts. We can design energy-efficient buildings only when we know where and why energy is needed and how it is used. Most of the energy consumed in buildings is used for heating, cooling, ventilating and lighting the indoor spaces, for sanitary water heating purposes and powering plug-in appliances required for daily life activities. Moreover, on-site renewable energy generation supports building energy efficiency by providing sustainable energy sources for the building energy needs. The production and consumption of energy carriers in buildings occur through the network of interconnected building sub-systems. A change in one energy process affects other energy processes. Thus, the overall building energy efficiency depends on the combined impact of the building with its systems interacting dynamically all among themselves, with building occupants and with outdoor conditions. Therefore, designing buildings for energy efficiency requires paying attention to complex interactions between the exterior environment and the internal conditions separated by building envelope complemented by building systems. In addition to building energy and CO2 emission performance, there are also other criteria for designers to consider for a comprehensive building design. For instance, building energy cost is one of the major cost types during building life span. Therefore, improving building efficiency not only addresses the challenges of global climate change but also high operational costs and consequent economic resource dependency. However, investments in energy efficiency measures can be costly, too. As a result, the economic viability of design options should be analysed carefully during decision-making process and cost-effective design choices needs to be identified. Furthermore, while applying measures to improve building performance, comfort conditions of occupants should not be neglected, as well. Advances in science and technologies introduced many approaches and technological products that can be benefitted in building design. However, it could be rather difficult to select what design strategies to follow and which technologies to implement among many for cost-effective energy efficiency while satisfying equally valued and beneficial objectives including comfort and environmental issues. Even using the state-of-the-art energy technologies can only have limited impact on the overall building performance if the building and system integration is not well explored. Conventional design methods, which are linear and sequential, are inadequate to address the inter-depended nature of buildings. There is a strong need today for new methods that can evaluate the overall building performance from different aspects while treating the building, its systems and surrounding as a whole and provide quantitative insight information for the designers. Therefore, in the current study, we purpose a simulation-based optimization methodology where improving building performance is taken integrally as one-problem and the interactions between building structure, HVAC equipment and building-integrated renewable energy production are simultaneously and dynamically solved through mathematical optimization techniques while looking for a balanced combination of several design options and design objectives for real-life design challenges. The objective of the methodology is to explore cost-effective energy saving options among a considered list of energy efficiency measures, which can provide comfort while limiting harmful environmental impacts in the long term therefore financial, environmental and comfort benefits are considered and assessed together. During the optimization-based search, building architectural features, building envelope features, size and type of HVAC equipment that belong to a pre-designed HVAC system and size and type of considered renewable system alternatives are explored simultaneously together for an optimal combination under given constraints. The developed optimization framework consists of three main modules: the optimizer, the simulator, and a user-created energy efficiency measures database. The responsibility of the optimizer is to control the entire process by implementing the optimization algorithm, to trigger simulation for performance calculation, to assign new values to variables, to calculate objective function, to impose constraints, and to check stopping criteria. The optimizer module is based on GenOpt optimization environment. However, a sub-module was designed, developed and added to optimization structure to enable Genopt to communicate with the user-created database module. Therefore, every time the value of a variable is updated, the technical and financial information of a matching product or system equipment is read from the database, written into simulation model, and fed to the objective formula. The simulator evaluates energy-related performance metrics and functional constraints through dynamic simulation techniques provided by EnergyPlus simulation tool. The database defines and organizes design variables and stores user-collected cost related, technical and non-technical data about the building energy efficiency measures to be tested during the optimization. An updated version of Particle Swarm Optimization with constriction coefficient is used as the optimization algorithm. The study covers multi-dimensional building design aims through a single-objective optimization approach where multi objectives are represented in a ε-Constraint penalty approach. The primary objective is taken as minimization of building global costs due to changes in design variables therefore it includes minimization of costs occur due to operational energy and water consumption together with ownership costs of building materials and building systems. Moreover, a set of penalty functions including equipment capacity, user comfort, CO2 emissions and renewable system payback period are added to the main objective function in the form of constraints to restrict the solution region to user-set design target. Consequently, multi-objective design aims are translated into a single-objective where the penalty functions acts as secondary objectives. The performance of the proposed optimization methodology was evaluated through a case study implementation where different design scenarios were created, optimized and analysed. A hypothetical base-case office building was defined. Three cities located in Turkey namely Istanbul, Ankara and Antalya were selected as building locations. Therefore, the performance of the methodology in different climatic conditions was investigated. An equipment database consists of actual building materials and system equipment commonly used in Turkish construction sector was prepared. In addition, technical and financial data necessary for objective function calculation were collected from the market. The results of the case studies showed that application of the proposed methodology achieved giving climate-appropriate design recommendations, which resulted in major cost reductions and energy savings. One of the most important contributing factors of this thesis is introducing an integrative method where building architectural elements, HVAC system equipment and renewable systems are simultaneously investigated and optimized while interactions between building and systems are being dynamically captured. Moreover, this research is distinctive from previous studies because it makes possible investigating actual market products as energy efficiency design options through its proposed database application and a sub-program that connect optimization engine with the data library. Therefore, application of the methodology can provide support on real-world building design projects and can prevent a mismatch between the optimization recommendations and the available market solutions. Furthermore, another contributing merit of this research is that it achieves formulating competing building design aims in a single objective function, which can still capture multi-dimensions of building design challenge. Global costs are minimized while energy savings are achieved, CO2-equivalent emission is reduced, right-sized equipment are selected, thermal comfort is provided to users and target payback periods of investments are assured. To conclude, the proposed methodology links building energy performance requirements to financial and environmental targets and it provides a promising structure for addressing real life building design challenges through fast and efficient optimization techniques.

Pour garantir à la fois une bonne performance des services offerts par des centres de données, et une consommation énergétique raisonnable, une analyse détaillée du comportement de ces systèmes est essentielle pour la conception d'algorithmes d'optimisation efficaces permettant de réduire la consommation énergétique. Cette thèse, s'inscrit dans ce contexte, et notre travail principal consiste à concevoir des systèmes de gestion dynamique de l'énergie basés sur des modèles stochastiques de files d'attente contrôlées. Le but est de rechercher les politiques de contrôle optimales afin de les appliquer sur des centres de données, ce qui devrait répondre aux demandes croissantes de réduction de la consommation énergétique et de la pollution numérique tout en préservant la qualité de service. Nous nous sommes intéressés d’abord à la modélisation de la gestion dynamique de l’énergie par un modèle stochastique pour un centre de données homogène, principalement pour étudier certaines propriétés structurelles de la stratégie optimale, telle que la monotonie. Après, comme des centres de données présentent un niveau non négligeable d'hétérogénéité de serveurs en termes de consommation d'énergie et de taux de service, nous avons généralisé le modèle homogène à un modèle hétérogène. De plus, comme le réveil (resp. l'arrêt) d’un serveur de centre de données n’est pas instantané et nécessite un peu plus de temps pour passer du mode veille au mode prêt à fonctionner, nous avons étendu le modèle dans le but d'inclure cette latence temporelle des serveurs. Tout au long de cette optimisation exacte, les arrivées et les taux de service sont spécifiés avec des histogrammes pouvant être obtenus à partir de traces réelles, de données empiriques ou de mesures de trafic entrant. Nous avons montré que la taille du modèle MDP est très grande et conduit au problème de l’explosion d’espace d'états et à un temps de calcul important. Ainsi, nous avons montré que l'optimisation optimale nécessitant le passage par un MDP est souvent difficile, voire pratiquement impossible pour les grands centres de données. Surtout si nous prenons en compte des aspects réels tels que l'hétérogénéité ou la latence des serveurs. Alors, nous avons suggéré ce que nous appelons l’algorithme greedy-window qui permet de trouver une stratégie sous-optimale meilleure que celle produite lorsqu’on envisage un mécanisme spécial comme les approches à seuil. Et plus important encore, contrairement à l’approche MDP, cet algorithme n’exige pas la construction complète de la structure qui encode toutes les stratégies possibles. Ainsi, cette algorithme donne une stratégie très proche de la stratégie optimale avec des complexités spatio-temporelles très faibles. Cela rend cette solution pratique, évolutive, dynamique et peut être mise en ligne
To ensure both good data center service performance and reasonable power consumption, a detailed analysis of the behavior of these systems is essential for the design of efficient optimization algorithms to reduce energy consumption. This thesis fits into this context, and our main work is to design dynamic energy management systems based on stochastic models of controlled queues. The goal is to search for optimal control policies for data center management, which should meet the growing demands of reducing energy consumption and digital pollution while maintaining quality of service. We first focused on the modeling of dynamic energy management by a stochastic model for a homogeneous data center, mainly to study some structural properties of the optimal strategy, such as monotony. Afterwards, since data centers have a significant level of server heterogeneity in terms of energy consumption and service rates, we have generalized the homogeneous model to a heterogeneous model. In addition, since the data center server's wake-up and shutdown are not instantaneous and a server requires a little more time to go from sleep mode to ready-to-work mode, we have extended the model to the purpose of including this server time latency. Throughout this exact optimization, arrivals and service rates are specified with histograms that can be obtained from actual traces, empirical data, or traffic measurements. We have shown that the size of the MDP model is very large and leads to the problem of the explosion of state space and a large computation time. Thus, we have shown that optimal optimization requiring a MDP is often difficult or almost impossible to apply for large data centers. Especially if we take into account real aspects such as server heterogeneity or latency. So, we have suggested what we call the greedy-window algorithm that allows to find a sub-optimal strategy better than that produced when considering a special mechanism like threshold approaches. And more importantly, unlike the MDP approach, this algorithm does not require the complete construction of the structure that encodes all possible strategies. Thus, this algorithm gives a strategy very close to the optimal strategy with very low space-time complexities. This makes this solution practical, scalable, dynamic and can be put online

In the current electrical grid, utility companies have begun to use demand side management (DSM) programs and time-of-use (TOU) pricing schemes to shape the residential load profile. However, it is difficult for the residential users to respond to the pricing signal and manually manage the operation of various household appliances. Hence, the autonomous energy consumption scheduling of residential loads and electric vehicles (EVs) is necessary for the users to benefit from the DSM programs. In this thesis, we propose different algorithms to schedule the energy consumption of residential loads and EVs, and provide ancillary services to the electrical grid. First, we study the DSM for areas with high photovoltaic (PV) penetration. Since many rooftop PV units can be integrated in the distribution network, the voltage rise issue occurs when the reverse power flow from the households to the substation is significant. We use stochastic programming to formulate an energy consumption scheduling problem, which takes into account the voltage rise issue and the uncertainty of the power generation from PV units. We propose an algorithm by solving the formulated problem and jointly shave the peak load and reduce the reverse power flow. Subsequently, we study using the EVs to provide the frequency regulation service. We formulate a problem to schedule the hourly regulation capacity of the EVs using the probabilistic robust optimization framework. Our formulation takes into account the limited battery capacity of the EVs and the uncertainty of the automatic generation control (AGC) signal. An efficient algorithm is proposed to solve the formulated problem based on duality. Last but not least, we study the market participation of an aggregator which coordinates a fleet of EVs to provide frequency regulation service to an independent system operator (ISO). The two-settlement market system (i.e., the day-ahead market (DAM) and real-time market) is considered. We analyze two types of DAMs based on the market rules of New York ISO and California ISO. We formulate a problem to determine the bid for the aggregator in the DAM using stochastic program and conditional value at risk. Efficient algorithms are proposed to tackle the formulated problem.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.
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Com o crescimento das redes celulares e com o surgimento de novas tecnologias, o consumo de energia e a eficiência energética das redes celulares se tornaram mais importantes. Recentemente, as comunicações verdes têm recebido muita atenção, a fim de reduzir o consumo de energia e minimizar as emissões de dióxido de carbono (CO2). Neste trabalho, estamos interessados em métodos de eficiência energética que otimizam a redução do consumo de energia das redes celulares, especialmente alterando a potência transmitida de estações de base. Investigamos algumas soluções encontradas na literatura, a saber, o sleep mode e o cell zooming. Nós investigamos também o uso do Coordinated Multi-Point (CoMP), uma tecnologia de rádio de transmissão coordenada, a fim de melhorar a qualidade da rede. _______________________________________________________________________________________________ ABSTRACT
With the growth of cellular networks and emergence of new technologies, the power consumption and energy efficiency of cellular networks have become more important. Recently, green communications have received much attention in order to reduce the energy consumption and minimize the carbon dioxide (CO2) emission. In this work, we are interested in power efficiency methods which optimize the energy saving of the cellular networks especially altering the transmitted power of base stations. We investigate some solutions found in the literature, namely sleep mode and cell zooming techniques. We also investigate the use of Coordinated Multi-Point (CoMP) transmission radio technology in order to improve the quality of the network. _______________________________________________________________________________________________ RÉSUMÉ
Avec la croissance des réseaux cellulaires et l'émergence de nouvelles technologies, la consommation d'énergie et l'efficacité énergétique des réseaux cellulaires sont devenues plus importantes. Récemment, les communications vertes ont reçu beaucoup d'attention dans le but de réduire la consommation d'énergie et de réduire les émissions de dioxyde de carbone (CO2). Dans ce travail, nous nous intéressons à des méthodes d'efficacité de puissance qui optimisent l’économie d'énergie des réseaux cellulaires en altérant particulièrement la puissance transmise des stations de base. Nous avons étudié des solutions trouvées dans la littérature, à savoir le sleep mode et le cell zooming. Nous avons étudié également l'utilisation de Coordinated Multi-Point (COMP), une technologie de radio de transmission coordonnée, afin d'améliorer la qualité du réseau.

Thesis (M.S. in Mechanical Engineering and M.S. in Information Technology Management)--Naval Postgraduate School, September 2003.
Thesis advisor(s): Joshua H. Gordis, Dan Boger. Includes bibliographical references (p. 157-158). Also available online.

The importance of energy efficiency has grown alongside awareness of climate change due to the rapid increase of greenhouse gases. With the increasing trend regarding mobile subscribers, it is necessary to prevent an expansion of energy consumption via mobile networks. In this thesis, the energy optimization of the new radio access technology called 5G NR utilizing different sleep states to put base stations to sleep when they are not transmitting data is discussed. Energy savings and file latency with heterogeneous and super dense urban scenarios was evaluated through simulations with different network deployments. An updated power model has been proposed and the sensitivity of the new power model was analyzed by adjusting wake-up time and sleep factors. This showed that careful implementation is necessary when adjusting these parameter settings, although in most cases it did not change the end results by much. Since 5G NR has more potential in energy optimization compared to the previous generation mobile network 4G LTE, up to 4 sleep states was implemented on the NR base stations and one idle mode on LTE base stations. To mitigate unnecessary sleep, deactivation timers are used which decides when to put base stations to sleep. Without deactivation timers, the delay could increase significantly, while with deactivation timers the delay increase would only be a few percent. Up to 42.5% energy could be saved with LTE-NR non-standalone deployment and 72.7% energy with NR standalone deployment compared to LTE standalone deployment, while minimally impacting the delay on file by 1%.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 83-85).
This thesis proposes a decentralized reward-based incentive mechanism to address the problem of noncomplying subsidiaries when the parent company wish to meet its targeted energy consumption level. Besides its effectiveness in ensuring compliance, the proposed mechanism is advantageous as it is able to induce the optimal subsidiary behavior that maximizes the company profit given a carefully chosen reward allocation scheme. In addition, when the company is willing to trade part of its profit for an operationally simple mechanism, simple uniform allocation scheme is highly effective when the subsidiaries exhibit certain degree of symmetry. The results above are drawn from our investigation on a more general model: Cournot competition under a joint constraint. For this model, we study the equilibrium behavior under free competition and compare the profit and total surplus achieved with the corresponding values when different levels of coordination are introduced in the market (i.e., the Monopoly market and the society-wide coordinated market). We establish tight upper bounds for the profit and total surplus loss due to lack of coordination as functions of various market characteristics (i.e., number of firms, intensity of competition and asymmetry between firms).
by Jingxi Wang.
S.M.

This study has been motivated by an understanding of the twin needs to reduce carbon dioxide emissions and increase the access to have complete control of the ventilation system available in the buildings. In consideration of the increasing utilization of fossil fuels, there is an extensive threat of increased global warming conditions associated. To ensure sustainable development, improvement of social welfare and wealth creation, energy is an essential factor. The consumption of electricity and energy delivered per floor area in Sweden has been considerably rising since 2014. The aim of this study is twofold where the authors have mapped and defined the specific customer needs for choosing an “add-on energy optimization module” for the existing HVAC systems in Sweden’s commercial market. Secondly, the study has also focussed to identify the acceptance of the complementary good technology from the perspective of a customer’s experience of value creation. It is a case study carried out in collaboration with a Swedish cleantech company, that will be named “Company-X” in the further part of the study. This company utilises a part of space technology to secure a healthy indoor air climate and at the same time save energy in buildings. The thesis has been carried out qualitatively. Since there is a preunderstanding of this theory where an abductive approach with semi-structured interviews has been followed to perceive the current market situation. The study further underlines the importance and need of making investments for a cleaning module combined with an optimization algorithm which can be easily mounted on current ventilation systems like Lego pieces. Under this module, the air quality is monitored, and the system adapts to current conditions concerning time. The results designate that it is possible to maintain a predefined indoor air quality to the lowest possible energy consumption by real-time monitoring with this cleaning module at facilities that are equipped with single or multiple-split HVAC systems. The best results towards attaining greater energy savings can be obtained from the association of Building energy management system and Air-handling unit with this cleaning module.

L'inquiétude croissante concernant le futur des ressources énergétique a fait de l'optimisation énergétique une priorité dans tous les secteurs. De nombreux sujets de recherche se sont focalisés sur celui du bâtiment étant le principal consommateur d'énergie, en particulier à cause de ses besoins en chauffage. Beaucoup de propositions pour réduire la consommations ont été faites. Ceux-ci vont de l'amélioration de l'isolation au changement du système de gestion du thermostat en passant par la formation des occupants à une meilleure gestion de leur bâtiment. Cette thèse propose une nouvelle méthode de contrôle qui permet de minimiser la consommation énergétique et dépenses budgétaires. La méthode génère un planning énergétique sur une période de temps pré-définie, ceci en prenant compte du confort thermique des occupants. Elle est basée sur l'application de la méthode de Monte Carlo, un générateur aléatoire appliqué au système de chauffage. L'objectif est de déterminer le planning de chauffage optimal, qui respecte les trois contraintes suivantes: - Le confort thermique des résidents; - La minimisation de l'énergie consommée / du budget; - Le déplacement de la charge. De plus, pour tester cette méthode, l'identification du comportement thermique du bâtiment a été requise. De ce fait, un modèle thermique du bâtiment a été développé. Ce modèle a été volontairement simplifié afin de l'intégrer plus simplement dans le processus de contrôle. De plus, une nouvelle approche d'identification thermique du bâtiment aussi bien qu'une nouvelle méthode de contrôle en temps réel ont été présentées
With the highly developing concerns about the future of energy resources, the optimization of energy consumption becomes a must in all sectors. A lot of research was dedicated to buildings regarding that they constitute the highest energy consuming sector mainly because of their heating needs. Many proposals of new strategies to minimize building consumption were done. These proposals vary between recommending better insulation, advising change in occupants' behavior and changing the heating control management. This thesis proposes a new control method that helps minimizing the heating consumption and expenses. This method generates an energy plan over a defined prediction horizon respecting the occupants’ thermal comfort. It is based on the application of Monte Carlo method, i.e., a random generator for the heating system scenarios. The aim is to determine the optimal heating plan for the prediction horizon that fulfills the constraints regarding the following three factors: • The thermal comfort of occupants; • The minimization of the energy consumption/expenses; • Load shifting. However, to test this method, an identification of the building thermal behavior was needed. Thus, a building thermal model to simulate the building behavior was developed. This model was meant to be simplified in order to better integrate it in the control process. Furthermore, a new parameter estimation approach as well as a real time temperature control method are presented to ensure the implementation of the optimal predicted plan

Au cours des dernières années, l'informatique “green” est devenue un sujet important dans le calcul intensif. Cependant, les plates-formes informatiques continuent de consommer de plus en plus d'énergie en raison de l'augmentation du nombre de noeuds qui les composent. Afin de minimiser les coûts d'exploitation de ces plates-formes de nombreuses techniques ont été étudiées, parmi celles-ci, il y a le changement de la fréquence dynamique des processeurs (DVFS en anglais). Il permet de réduire la consommation d'énergie d'un CPU, en abaissant sa fréquence. Cependant, cela augmente le temps d'exécution de l'application. Par conséquent, il faut trouver un seuil qui donne le meilleur compromis entre la consommation d'énergie et la performance d'une application. Cette thèse présente des algorithmes développés pour optimiser la consommation d'énergie et les performances des applications parallèles avec des itérations synchrones et asynchrones sur des clusters ou des grilles. Les modèles de consommation d'énergie et de performance proposés pour chaque type d'application parallèle permettent de prédire le temps d'exécution et la consommation d'énergie d'une application pour toutes les fréquences disponibles.La contribution de cette thèse peut être divisé en trois parties. Tout d'abord, il s'agit d'optimiser le compromis entre la consommation d'énergie et les performances des applications parallèles avec des itérations synchrones sur des clusters homogènes. Deuxièmement, nous avons adapté les modèles de performance énergétique aux plates-formes hétérogènes dans lesquelles chaque noeud peut avoir des spécifications différentes telles que la puissance de calcul, la consommation d'énergie, différentes fréquences de fonctionnement ou encore des latences et des bandes passantes réseaux différentes. L'algorithme d'optimisation de la fréquence CPU a également été modifié en fonction de l'hétérogénéité de la plate-forme. Troisièmement, les modèles et l'algorithme d'optimisation de la fréquence CPU ont été complètement repensés pour prendre en considération les spécificités des algorithmes itératifs asynchrones.Tous ces modèles et algorithmes ont été appliqués sur des applications parallèles utilisant la bibliothèque MPI et ont été exécutés avec le simulateur Simgrid ou sur la plate-forme Grid'5000. Les expériences ont montré que les algorithmes proposés sont plus efficaces que les méthodes existantes. Ils n’introduisent qu’un faible surcoût et ne nécessitent pas de profilage au préalable car ils sont exécutés au cours du déroulement de l’application
In recent years, green computing has become an important topic in the supercomputing research domain. However, the computing platforms are still consuming more and more energy due to the increase in the number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them. It can be used to reduce the power consumption of the CPU while computing, by lowering its frequency. However, lowering the frequency of a CPU may increase the execution time of the application running on that processor. Therefore, the frequency that gives the best trade-off between the energy consumption and the performance of an application must be selected.This thesis, presents the algorithms developed to optimize the energy consumption and theperformance of synchronous and asynchronous message passing applications with iterations runningover clusters or grids. The energy consumption and performance models for each type of parallelapplication predicts its execution time and energy consumption for any selected frequency accordingto the characteristics of both the application and the architecture executing this application.The contribution of this thesis can be divided into three parts: Firstly, optimizing the trade-offbetween the energy consumption and the performance of the message passing applications withsynchronous iterations running over homogeneous clusters. Secondly, adapting the energy andperformance models to heterogeneous platforms where each node can have different specificationssuch as computing power, energy consumption, available frequency gears or network’s latency andbandwidth. The frequency scaling algorithm was also modified to suit the heterogeneity of theplatform. Thirdly, the models and the frequency scaling algorithm were completely rethought to takeinto considerations the asynchronism in the communication and computation. All these models andalgorithms were applied to message passing applications with iterations and evaluated over eitherSimGrid simulator or Grid’5000 platform. The experiments showed that the proposed algorithms areefficient and outperform existing methods such as the energy and delay product. They also introducea small runtime overhead and work online without any training or profiling

Energy issues in the building sector become more and more important nowadays. Although the technology improves, the energy consumption remains the same because of people’s way of living. To reduce the energy consumption, it is possible to improve the technical components that form the building envelope and to change people’s habits. This report aims at determining the best measurement methodology of the heating and hot water consumption of a building to insure real-time visualization and evaluating the energy savings that could be made by changing people habits. To do so, an existing measurement methodology is analyzed by making error calculations and computer-based modeling and simulations are carried out to determine the heating consumption of the building under different conditions. The program DesignBuilder is used to assess the energy consumption of the building. The study shows that a consequent reduction of the heating consumption is possible by only changing people’s habits. Real-time visualization would be really helpful but it needs very accurate measurements that are almost impossible if they are not integrated in the first stages of the building process.

The management and delivery of water and wastewater consume significant amounts of energy, mostly in the form of electricity. With increasing populations, climate change, water quality issues and increasing energy prices, it is more important than ever to understand energy consumption patterns. Energy usually represents the largest operational cost in water utilities around the world, yet there is limited work aiming to quantify the specific relationship between water and its associated energy, and understand its implications for future decision-making. This thesis presents variousmethodological approachesto quantify and understand energy use in water infrastructure systems, as well as how to incorporate them in decision-making processes. The main hypotheses are as follows: firstly, a detailed understanding of the use of energy in water infrastructure systems can facilitate more efficient and sustainable water infrastructure systems and, secondly, that incorporating energy into planning for water and wastewater resources can help understand the impacts of decisions and establish trade-offs between actions. To test these hypotheses, the thesis presents an analytical approach to various areas. Firstly, it identifies, maps and quantifies the energy consumption patterns within a water infrastructure system. This is then used to identify inefficiencies and areas of potential energy saving. Secondly, it incorporates detailed energy costs into short and long-term water resources management and planning. Thirdly, it evaluates trade-offs between energy costs and changing effluent quality regulations in wastewater resources. The Thames River basin, in the south-east of England, is used as a case study to illustrate the approach. The results demonstrate that a systematic approach to the quantification of energy use in a water infrastructure system can identify areas of inefficiencies that can be used to make decisions with regards to infrastructure planning. For example, water systems have significant geo-spatial variations in energy consumption patterns that can be addressed specifically to reduce negative trade-offs. The results also show that incorporating detailed energy information into long-term water resources planning can alter the choices made in water supply options, by providing more complete information. Furthermore, methodologically, they show how several methodological approaches can be used to support more complete decision-making in water utilities to reduce short and long-term costs. In this particular case study, the results show that there are important differences in energy consumption by region, and significant differences in the seasonal and energy patterns of water infrastructure systems. For example, water treatment was shown to be the largest consumer of energy within the whole system, compared with pumping or wastewater treatment; but wastewater treatment energy consumption was shown to be the fastest growing over time due to changes in water quality regulatory frameworks. The results show that more stringent effluent standards could result in at least a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional million tonnes of CO2 a year from treating wastewater in large works in the UK. These are projected to continue to increase if the decarbonisation of the electricity grid does not occur fast enough. Finally, the thesis also shows that daily energy consumption can be reduced by up to 18% by optimally routing water through a water network. optimization of water networks, and that a change in discount rates could change the daily operating costs by 19%, that in turn leads to a resulting different set of optimal investment options in a water supply network.

Un impianto energetico ibrido consiste in una combinazione di diversi sistemi energetici alimentati da diversi fonti energetiche, i quali quando vengono integrati, consistono di superare i limiti dei singoli sistemi. Diversi sistemi energetici possono essere integrati in un unico impianto ibrido a seconda della disponibilità delle diverse fonti energetiche. Diversi studi presenti in letteratura affermano che gli impianti energetici ibridi hanno la potenzialità di fornire energia con migliore qualità ed affidabilità rispetto ad un sistema alimentato da una singola fonte energetica. I benefici energetici ed ambientali degli impianti energetici ibridi destinati ad uso civile sono legati al dimensionamento e controllo di questi sistemi. In altre parole, i fattori fondamentali per un risparmio di energia e per la riduzione delle emissioni sono il dimensionamento ed il controllo ottimizzati dei vari sistemi che compongo l’impianto energetico ibrido. Inoltre, l’ottimizzazione di un impianto energetico ibrido deve basarsi sulla corrispondenza tra l’energia prodotta dai vari sistemi e la richiesta energetica dell’edificio. L’ottimizzazione degli impianti energetici ibridi viene solitamente condotta considerando gli impatti ambientali durante la vita utile. Tuttavia, questo approccio, che tiene conto solo dell’impatto ambientale, del costo o del consumo di energia primaria legato al funzionamento dell’impianto, può far sì che gli impatti ambientali legati alle altre fasi del ciclo di vita (i.e. la fase di costruzione e di smaltimento) non vengono presi in considerazione. Data la complessità legata al numero di variabili coinvolte, il fatto che le fonti di energia disponibili sono molteplici, la scelta dei sistemi di conversione dell’energia e l’integrazione del ciclo di vita in processi di ottimizzazione, la soluzione di tale problema richiede la disponibilità dei metodi e delle linee guida per l’ottimizzazione degli impianti energetici ibridi al fine di ottenere un risultato ottimale in termini di risparmio energetico e di conseguenza riduzione dell’impatto ambientale durante il ciclo di vita dell’impianto. Perciò, il lavoro di questa tesi di dottorato si concentra sullo sviluppo dei metodi e delle linee guida per l’ottimizzazione di impianti energetici ibridi minimizzando l’energia primaria consumata durante il ciclo di vita dell’impianto. Questo lavoro presenta un nuovo metodo, basato sulle tecniche di programmazione dinamica, per l’ottimizzazione di impianti energetici ibridi minimizzando il consumo di energia primaria durante il funzionamento. La metodologia sviluppata in questo lavoro estende l’uso del metodo di programmazione dinamica per risolvere dei problemi legati al dimensionamento e controllo ottimizzati di impianti complessi. Questo metodo è veloce, facile da implementare e tiene conto anche della non-linearità dei sistemi ibridi. Inoltre, questo lavoro affronta la valutazione del ciclo di vita di sistemi alimentati da fonti rinnovabili e non-rinnovabili destinati ad uso residenziale mediante un approccio “cradle-to-gate” applicato ai vari sistemi energetici. Inoltre, si affronta il problema del calcolo dell’inventario dei vari sistemi per diverse taglie e si illustrano i vari coefficienti usati per il calcolo dell’inventario in funzione della taglia. La procedura sviluppata consente di ottenere delle curve di impatto che possono essere usate per l’ottimizzazione dei sistemi energetici. Infine, viene sviluppata una metodologia per l’integrazione dell’analisi del ciclo di vita nel processo di ottimizzazione di impianti energetici ibridi. La metodologia viene applicata ad un caso studio, che consiste in un impianto energetico ibrido costituito da sistemi alimentati da energia rinnovabile e non-rinnovabile. L’ottimizzazione viene condotta minimizzando il consumo di energia primaria durante la fase di costruzione, trasporto e funzionamento dell’impianto.
Hybrid energy plants may be a solution to overcome the limitations of a single source of energy, both based on renewable and non-renewable energy sources. A hybrid energy plant consists in a combination of two or more energy conversion systems which use different energy sources, that, when integrated, overcome the respective limitations. Several energy systems could be integrated in a hybrid energy plant depending on the availability of their primary energy resources. Hybrid energy plants have the potential to provide higher quality and better reliability of energy supply compared to a system based on a single source of energy. The promising energy and environmental benefits of hybrid energy plants for building applications are greatly dependent upon their design and operation strategy. In other words, the key factors for the achievement of an as high as possible primary energy saving and greenhouse gas emission reduction are the correct sizing and operation of the hybrid energy plant. Moreover, the optimization process of a hybrid energy plant must be based on the efficient match between building energy demand and supply. The optimal design of hybrid energy plants is commonly achieved by accounting for their environmental impacts during their useful life. However, this common approach, which only accounts for on-site environmental impacts, costs or primary energy consumption, may lead to burden shifting by ignoring the upstream life cycle of the hybrid energy plant. Given the complexity to deal with the number of variables involved, the multiple sources of energy that can be used, the choice of energy converters, the integration of life cycle assessment in system’s design and operation, procedures ad guidelines are needed for the solution of such complex problem, i.e. the optimization of hybrid energy plants in order to achieve an optimal result in term of primary energy saving and consequently environmental impacts reduction over the life cycle of the plant. For these reasons, the work of this thesis focuses on the development of original methods and procedures for the optimization of hybrid energy plants by accounting for the on-site and off-site energy consumption or environmental impacts calculated throughout the various stages of the life cycle of the energy plant. This work provides a new dynamic programming based optimization method to solve the optimization problem of hybrid energy plants by minimizing the on-site primary consumption. The proposed methodology extends the use of the dynamic programming method and attempts to apply it to solve both the sizing and operating optimization problems. Moreover, the presented method is fast, easy to implement and also addresses the nonlinearity associated with the characteristics of a hybrid energy plant. In addition, this work, investigates the life cycle assessment of renewable and non-renewable energy systems which can be employed for residential applications. For each system a cradle-to-gate life cycle assessment is carried out. The considered impact parameter is the cumulative energy demand. Furthermore, the problem of life cycle inventory scaling is addressed and appropriate scaling factors and their relevance for calculating environmental impacts are presented. The scaling procedure used in this work allows to obtain impact curves which can be used for optimization purposes. Finally, a general procedure for the integration of life cycle assessment into system’s design and optimization is developed. A case study consisting of a hybrid energy plant, which is composed of renewable and non-renewable energy systems, is considered to demonstrate the proposed approach. The optimization is carried out by taking into account the non-linear life cycle inventory scaling of energy systems and is conducted with the aim of minimizing the primary energy consumed during the manufacturing, transportation and operation phases.

Traffic signal cycle lengths are traditionally optimized to minimize vehicle delay at intersections using the Webster formulation. This thesis includes two studies that develop new formulations to compute the optimum cycle length of isolated intersections, considering measures of effectiveness such as vehicle delay, fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model against simulated data. The microscopic simulation software, INTEGRATION, was used to simulate two-phase and four-phase isolated intersections over a range of cycle lengths, traffic demand levels, and signal timing lost times. Intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) were derived from the simulation software. The cycle lengths that minimized the various measures of effectiveness were then used to develop the proposed formulations. The first research effort entailed recalibrating the Webster model to the simulated data to develop a new delay, fuel consumption, and emissions formulation. However, an additional intercept was incorporated to the new formulations to enhance the Webster model. The second research effort entailed updating the proposed model against four study intersections. To account for the stochastic and random nature of traffic, the simulations were then run with twenty random seeds per scenario. Both efforts noted its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths optimized for vehicle delay only. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle demands.
Master of Science

This dissertation provides one methodology to determine potential energy savings of buildings with limited information. This methodology is based upon the simplified energy analysis procedure of HVAC systems and the control of the comfort conditions. Numerically, the algorithm is a tailored exhaustive search over all the independent variables that are commonly controlled for a specific type of HVAC system. The potential energy savings methodology has been applied in several buildings that have been retrofitted and/or commissioned previously. Results from the determined savings for the Zachry building at Texas A&M after being commissioned show a close agreement to the calculated potential energy savings (about 85%). Differences are mainly attributed to the use of simplified models. Due to the restriction of limited information about the building characteristics and operational control, the potential energy savings method requires the determination of parameters that characterize its thermal performance. Thus, a calibrated building is needed. A general procedure has been developed to carry out automated calibration of building energy use simulations. The methodology has been tested successfully on building simulations based on the simplified energy analysis procedure. The automated calibration is the minimization of the RMSE of the energy use over daily conditions. The minimization procedure is fulfilled with a non-canonical optimization algorithm, the Simulated Annealing, which mimics the Statistical Thermodynamic performance of the annealing process. That is to say, starting at a specified temperature the algorithm searches variable-space states that are steadier, while heuristically, by the Boltzmann distribution, the local minima is avoided. The process is repeated at a new lower temperature that is determined by a specific schedule until the global minimum is found. This methodology was applied to the most common air-handler units producing excellent results for ideal cases or for samples modified with a 1% white noise.

Gas supplying process for consumers needs sufficient share of energy for upstream, midstream and downstream purposes. In spite of a huge amount of great investments into the industry it is still available to improve the efficiency of energy usage inside the industry. The biggest share of energy consumption is within transportation sector. Optimization of operating conditions of gas pipeline is a one of the cheapest ways for reducing energy consumption. Optimization doesn’t need any investments into the industry. It works only within operating parameters. Adjustable operating parameters of a gas pipeline are operative pressure, rotation speed of compressors, amount of operating units, gas temperature after a compressor station and others. The energy consumption depends on the combination of the parameters which determine an appropriate operation mode to provide the particular gas flow through a pipeline, the maximum capacity, the minimum energy consumption and others. From energy saving point of view it is possible to reduce energy demand in the gas industry due to optimization of the operation mode. A few approaches to achieving energy reduction through optimization are investigated in this work and presented in this article, such as saving energy through changing of loading between compressor stations, varying the depth of gas cooling and changing the loading of gas pumping units. The results of analyzing inside the study model reflect the possibility for improving efficiency of gas trunk pipelines.

This thesis deals with the modeling, the design and the control of mild hybrid electric vehicles. The main goal is to develop accurate design tools and methodologies for preliminary system and component level analysis. Particular attention is devoted to the configuration in which an electric machine is mounted on the rear axle of a passenger car. The use of such a machine in parallel with the internal combustion engine allows one to exploit different functionalities that are able to reduce the overall fuel consumption of the vehicle. In addition, the indirect coupling between the thermal and the electric machine, realized through the road and not by means of mechanical couplers, together with the position of the latter in the overall vehicle chassis system, enables such an architecture to be efficient both from the energy recovery and the full electric driving point of view. Chapter 1 introduces the problem of fuel consumption and emissions reduction in the overall world context and presents the main hybrid architectures available. Chapter 2 is devoted to the study of the influence of the electric machine position in the powertrain regarding the regenerative braking potentialities concerned. The model considered for the analysis will be described on each of its subcomponents. The braking performance of the vehicle in electric mode is presented considering no losses in the electric powertrain (electric motor, battery, inverter). Chapter 3 is dedicated to the design of an electric machine for a rear axle powertrain. The specifications of such machine are optimized considering both the vehicle and the application under analysis. The design takes into account analytical techniques for the computation of electrical parameters (such as phase and DC currents) and the torque - speed map, as well as numerical ones for its thermal behavior. In Chapter 4 the electrical and thermal characteristics of the designed electric motor are implemented in the model presented in Chapter 2. The overall vehicle model is therefore used both to assess a simple torque split strategy between thermal and electric machine and to perform an optimal sizing of the battery considering all the limitations imposed by the electric powertrain (e. g. maximum currents, maximum temperatures). Chapter 5 makes a step forward and analyzes the different implications that the use of the rear axle electric motor to brake the vehicle has on the vehicle dynamics. Open loop analysis will present a degradation of the vehicle handling comfort caused by the introduction of an oversteering moment to the vehicle. Through the use of a simplified vehicle model, the introduced oversteering yaw moment is evaluated, while a control strategy based on a new stability detector will show how to find a trade off between handling comfort and regenerable energy. At last, Chapter 6 deals with the problem of longitudinal driving comfort. Drivelines and chassis are lightly damped systems and the application of an impulsive torque imposed by the driver can cause the vehicle longitudinal acceleration (directly perceived by the driver) to be oscillating and non smooth. A sensitivity analysis on a conventional powertrain is presented demonstrating which of the different components are more influential in the different modes of vibration, and possible solutions to improve the driveability are proposed. One of these relates to the use of the rear axle electric machine in order to give more responsiveness to the vehicle. Finally, concluding remarks are given in Chapter 7.

The high costs and high risks of transporting fuel to combat zones make fuel conservation a dire need for the US military. A towable hybrid electric system can help relieve these issues by replacing less fuel efficient standalone diesel generators to deliver power to company encampments. Currently, standalone generators are sized to meet peak demand, even though peak demand only occurs during short intervals each day. The average daily demand is much less, meaning generators will be running inefficiently most of the day. In this thesis, a simulation is created to help determine an optimal system design given a load profile, size and weight constraints, and relocation schedule. This simulation is validated using test data from an existing system. After validation, many hybrid energy components are considered for use in the simulation. The combination of components that yields the lowest fuel consumption is used for the optimal design of the system. After determining the optimal design, a few design parameters are varied to analyze their effect on fuel consumption. The model presented in this thesis agrees with the test data to 7% of the measured fuel consumption. Sixteen system configurations are run through the simulation and their results are compared. The most fuel efficient system is the system that uses a 3.8kW diesel engine generator with a 307.2V, maximum capacity LiFeMgPO? battery pack. This system is estimated to consume 21% less fuel than a stand-alone generator, and up to 28% less when solar power is available.
Master of Science

This thesis consists of two different research problems. In the first one, the aim is to model and simulate a solar-powered, single-effect, absorption refrigeration system using a flat-plate solar collector and LiBr-H2O mixture as the working fluid. The cooling capacity and the coefficient of performance of the system are analyzed by varying all independent parameters, namely: evaporator pressure, condenser pressure, mass flow rate, LiBr concentration, and inlet generator temperature. The cooling performance of the system is compared with conventional vapor-compression systems for different refrigerants (R-134a, R-32, and R-22). The cooling performance is also assessed for a typical year in Tampa, Florida. Higher COP values are obtained for a lower LiBr concentration in the solution. The effects of evaporator and condenser pressures on the cooling capacity and cooling performance are found to be negligible. The LiBr-H2O solution shows higher cooling performance compared to other mixtures under the same absorption cooling cycle conditions. For typical year in Tampa, Florida, the model shows a constant coefficient of performance of 0.94. In the second problem, a numerical model is developed for a typical food retail store refrigeration/HVAC system to study the effects of indoor space conditions on supermarket energy consumption. Refrigerated display cases are normally rated at a store environment of 24ºC (75ºF) and a relative humidity of 55%. If the store can be maintained at lower relative humidity, significant quantities of refrigeration energy, defrost energy and anti-sweat heater energy can be saved. The numerical simulation is performed for a typical day in a standard store for each month of the year using the climate data for Tampa, Florida. This results in a 24 hour variation in the store relative humidity. Using these calculated hourly values of relative humidity for a typical 24 hour day, the store relative humidity distribution is calculated for a full year. The annual average supermarket relative humidity is found to be 51.1%. It is shown that for a 5% reduction in store relative humidity that the display case refrigeration load is reduced by 9.25%, and that results in total store energy load reduction of 4.84%. The results show good agreement with available experimental data.

One of the greatest challenges that will face humanity in the 21st century is the issue of climate change brought about by emissions of greenhouse gases. Energy use is one of the primary sources of greenhouse gas emissions. However, it is also one of the most important contributors to improved human welfare over the past two centuries and will continue to be so for years to come. This quandary has led a number of researchers to suggest that geoengineering may be required in order to allow for continued use of fossil fuels while at the same time mitigating the effects of the associated greenhouse gas emissions on the global climate. The goal of this research was to develop a model that would allow decision-makers and policy analysts to assess the optimal mix of energy and geoengineering resources needed to meet global or regional energy demand at the lowest cost while accounting for appropriate emissions, greenhouse gas concentration, or temperature rise constraints. The resulting software model is called the Combined Energy and Geoengineering Optimization Model (CEAGOM). CEAGOM was then used to analyze the recently announced U.S.-China emissions agreement and to assess what the optimal global energy resource mix might be over the course of the 21st century, including the associated potential need for geoengineering. These analyses yielded optimal mixes of energy and geoengineering resources that could be used to inform regional and global energy and climate management strategies.

Cette thèse propose un concept d'optimisation de la consommation d'énergie dans les maisons intelligentes basées sur la gestion de la demande qui repose sur l'utilisation de système d e gestion de l'énergie à la maison (HEMS) qui est en mesure de contrôler les appareils ménagers. L'avantage de ce concept est l'optimisation de la consommation d'énergie sans réduire les utilisateurs vivant confort. Un mécanisme adaptatif pour une croissance intelligente système de gestion de l'énergie de la maison qui a composé des algorithmes qui régissent l'utilisation des différents types de charges par ordre de priorité pré-sélectionné dans la maison intelligente est proposé. En outre, une méthode pourl'optimisation de la puissance générée à partir d'un hybride de systèmes d'énergie renouvelables (HRES) afin d'obtenir la demande de charge. particules technique d'optimisation essaim (PSO) est utilisé comme l'optimisation algorithme de recherche en raison de ses avantages par rapport à d'autres techniques pour réduire le coût moyen actualisé de l'énergie (LCE) avec une plage acceptable de la production en tenant compte des pertes entre la production et la demande. Le problème est défini et la fonction objective est introduite en tenant compte des valeurs de remise en forme de sensibilité dans le processus d’essaim de particules. La structure de l'algorithme a été construite en utilisant un logiciel MATLAB et Arduino 1.0.5 du logiciel.Ce travail atteint le but de réduire la charge de l'électricité et la coupure du rapport pic-moyenne (PAR)
This thesis proposes a concept of power consumption optimization in smart homes based on demand side management that reposes on using Home Energy Management System (HEMS) that is able to control home appliances. The advantage of the concept is optimizing power consumption without reducing the users living comfort. An adaptive mechanism for smart home energy management system which composed of algorithms that govern the use of different types of loads in order of pre-selected priority in smart home is proposed. In addition a method for the optimization of the power generated from a Hybrid Renewable Energy Systems (HRES) in order to achieve the load demand. Particle Swarm Optimization Technique (PSO) is used as optimization searching algorithm due to its advantages over other techniques for reducing the Levelized Cost of Energy (LCE) with an acceptable range of the production taking into consideration the losses between production and demand sides. The problem is defined and the objective function is introduced taking into consideration fitness values sensitivity in particle swarm process. The algorithm structure was built using MATLAB software and Arduino 1.0.5 Software. This work achieves the purpose of reducing electricity expense and clipping the Peak-toAverage Ratio (PAR). The experimental setup for the smart meter implementing HEMS is built relying on the Arduino Mega 2560 board as a main controller and a web application of URL http://www.smarthome-em.com to interface with the proposed smart meter using the Arduino WIFI Shield

The global energy demand in residential sector is increasing steadily every year due to advancement in technologies. The present electricity grid is designed to support peak demand rather than Peak to Average (PAR) demand. Utilities are investigating the residential Demand Response (DR) to lower the (PAR) ratio and eliminate the need of building new power infrastructure. This requires Home Energy Management System (HEMS) at grid edge to manage and control the energy demand. In this thesis, we presented an MDPSO based DR enabled HEMS model for optimal allocation of energy resources in a smart dwelling. The algorithm is designed to lower peak energy demand as well as encourage the active participation of customers by offering a reward to comply with DR request. We categorized appliances as elastic non-deferrable loads and inelastic deferrable loads based on their DR potential and operating characteristics. The scheduling of elastic and inelastic class of appliances is performed separately using canonical and binary version of PSO given how we expressed out load categories. We performed use case simulation to validate the performance of MDPSO for combination of different tariffs: Time of Use (TOU), TOU and Critical peak rebate signal (CPR), TOU and upper demand limit. Simulation results show that algorithm can reduce the electricity cost in range of 28% to 7% under increasing comfort conditions in response to TOU prices and Peak demand reduction of about 24% under TOU pricing and medium comfort conditions for single household. Under CPR DR requests, with respect to TOU pricing, there is effectively no change in the peak under the minimum comfort scenario. Furthermore, algorithm is able to suppress the peak upto 25% under combination of TOU and hard constraint on maximum power withdrawn from grid with no change in the electricity cost. Scheduling of multiple houses under TOU pricing results in peak reduction of 7 % as compared to baseline state. Under combination of TOU and CPR the aggregate peak energy demand of multiple households during DR activation time intervals is reduced by 32 %. The algorithm can suppress the peak demand by 27% under TOU and hard constraint on maximum power withdrawn from grid by multiple houses.

The thesis project explores the use of an optimization methodology for selecting the lowest monetary cost combinations of technologies to meet a set operational energy efficiency targets for buildings. The optimization approach, which is operated on a normative energy model, is compared with existing prescriptive methodologies for selecting technology combinations and a metric is developed for ranking their effectiveness; the E/C Ratio. The energy savings/ cost ratio is also the objective function that the optimization algorithm is set to maximize. The optimization routine is coded in to a custom MATLAB script and is used in two case studies to optimize a proto-typical Korean apartment and office building. The optimization methodology finds technology combinations that are much more cost effective than the prescriptive methodology at meeting an energy savings target and can generically be applied to other buildings given a palette of technology alternatives and the corresponding cost data.

In recent years, many strides have been achieved in the area of Wireless Sensor Networks (WSNs), which is leading to constant innovations in the types of applications that WSNs can support. Much advancement has also been achieved in the area of smart homes, enabling its occupants to manually and easily control their utility expenses. In this thesis, both areas of research will be colluded for a simple, yet critical application: efficient and economical comfort control in smart residential spaces. The goal is to design a central, modular energy consumption control system for residential spaces, which manages energy consumption in all aspects of a typical residence. This thesis is concerned with two facets of energy consumption in residences. The first facet is concerned with controlling when the heating, ventilating, and air conditioning unit (HVAC) operates for each room separately. This is in contrast to a typical HVAC system where comfort is provided across the floor as a whole. The second facet is concerned with controlling the lighting in each room so as to not exceed a certain input value. The communication network that supports the realization of these coveted goals is based on Zigbee interconnected sensor nodes which pour data unto a smart thermostat which does all the required calculations and activates the modules required for comfort control and energy management, if needed. A Java-based discrete event simulator is then written up to simulate a floor of a typical Canadian single-family dwelling. The simulation assumes error-less communication and proceeds to record certain room variables and the ongoing cost of operation periodically. These results from the simulator are compared to the results of the well known simulator, created by DesignBuilder, which describes typical home conditions. The conclusion from this analysis is that the Comfort Control and Energy Management Algorithms (CEMA) are feasible, and that their implementation incurs significant monetary savings.

In recent year, wireless sensor networks have gradually become an indispensable part of people's daily lives. Energy consumption and energy harvesting play an important role in these systems. In outdoor, there is no doubt that solar energy is more suitable to powering the wireless sensor nodes. Although the energy consumption of these systems has been greatly reduced and the lifetime of sensor nodes also be improved through the larger capacity of supercapacitor or larger size of solar panel. But it will generate another kind of squander, how to choose a suitable solar panel and supercapacitor is appearance in our view. In this paper, I optimized the solar energy harvesting system from two aspects of capacity of supercapacitor and size of solar panel. The objective of this thesis has shown that as small solar panel and supercapacitor as possible for a given load of these systems under low consumption condition. Here, I establish the simulation in Simulink of Matlab, and build a low-power consumption; high-security solar energy harvesting hardware system for monitoring environment in Sundsvall, Sweden. Through the comparison between the simulation and real monitor to verify the feasibility

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 172-178).
With the rapid rise in the use of air conditioning systems and technological advancements, there is an ever-increasing need for optimizing the HVAC systems for energy efficiency while maintaining adequate occupant thermal comfort. HVAC systems in buildings alone contribute to almost 15% of the overall energy consumption across all sectors in the world and optimizing this would contribute positively towards overcoming climate change and reducing the global carbon footprint. A relatively modern solution is to implement a smart building-based control system and one of the objectives of this study is to understand the physical phenomenon associated with workspaces conditioned by chilled beams and evaluated the methods to reduce energy consumption.
Building upon the initial work aimed at creating a workflow for a smart building, this thesis presents the results of both experimental and computational studies of occupant thermal comfort with chilled beams (primarily in conference rooms) and the various inefficiencies associated. Results from these studies have helped to inform an optimum location for the installation of a chilled beam to counter the effects of incoming solar irradiation through an external window while keeping the energy consumption low. A detailed understanding of the various parameters influencing the temperature distribution in a room with chilled beams is achieved using CFD studies and data analysis of experimental data logging.
The work converges into a fundamental question of where, how, and what to measure to best monitor and control the human thermal comfort, and a novel technique was presented using the existing sensors which would provide a significant improvement over other existing methods in practice. This technique was validated using a series of experiments. The thesis concludes by presenting early works on hybrid HVAC systems including chilled beams and ceiling fans for higher economic gains. Future work should seek to perform CFD simulations for a better understanding of hybrid HVAC systems, both in conference rooms and open-plan office spaces, and also to design a new sensor that could better estimate human thermal comfort.
by Nikhilesh Ghanta.
S.M.
S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on January 3, 2008) Includes bibliographical references.

Because of the needs for producing highly reliable products and reducing product development time, Accelerated Life Testing (ALT) has been widely used in new product development as an alternative to traditional testing methods. The basic idea of ALT is to expose a limited number of test units of a product to harsher-than-normal operating conditions to expedite failures. Based on the failure time data collected in a short time period, an ALT model incorporating the underlying failure time distribution and life-stress relationship can be developed to predict the product reliability under the normal operating condition. However, ALT experiments often consume significant amount of energy due to the harsher-than-normal operating conditions created and controlled by the test equipment used in the experiments. This challenge may obstruct successful implementations of ALT in practice. In this dissertation, a new ALT design methodology is developed to improve the reliability estimation precision and the efficiency of energy utilization in ALT. This methodology involves two types of ALT design procedures - the sequential optimization approach and the simultaneous optimization alternative with a fully integrated double-loop design architecture. Using the sequential optimum ALT design procedure, the statistical estimation precision of the ALT experiment will be improved first followed by energy minimization through the optimum design of controller for the test equipment. On the other hand, we can optimize the statistical estimation precision and energy consumption of an ALT plan simultaneously by solving a multi-objective optimization problem using a controlled elitist genetic algorithm. When implementing either of the methods, the resulting statistically and energy efficient ALT plan depends not only on the reliability of the product to be evaluated but also on the physical characteristics of the test equipment and its controller. Particularly, the statistical efficiency of each candidate ALT plan needs to be evaluated and the corresponding controller capable of providing the required stress loadings must be designed and simulated in order to evaluate the total energy consumption of the ALT plan. Moreover, the realistic physical constraints and tracking performance of the test equipment are also addressed in the proposed methods for improving the accuracy of test environment. In this dissertation, mathematical formulations, computational algorithms and simulation tools are provided to handle such complex experimental design problems. To the best of our knowledge, this is the first methodological investigation on experimental design of statistically precise and energy efficient ALT. The new experimental design methodology is different from most of the previous work on planning ALT in that (1) the energy consumption of an ALT experiment, depending on both the designed stress loadings and controllers, cannot be expressed as a simple function of the related decision variables; (2) the associated optimum experimental design procedure involves tuning the parameters of the controller and evaluating the objective function via computer experiment (simulation). Our numerical examples demonstrate the effectiveness of the proposed methodology in improving the reliability estimation precision while minimizing the total energy consumption in ALT. The robustness of the sequential optimization method is also verified through sensitivity analysis.

Environmental awareness, production costs and operating expenses have provided a large incentive for the investigation of novel and more efficient fluid power technologies for decades. In the earth-moving sector, hydraulic hybrids have emerged as a highly efficient and affordable choice for the next generation hydraulic systems. Displacementcontrolled (DC) actuation has demonstrated that, when coupled with hydraulic hybrids, the engine power can be downsized by up to 50% leading to substantial savings. This concept has been realized by the authors‘ group on an excavator prototype where a secondary-controlled hydraulic hybrid drive was implemented on the swing. Actuatorlevel controls have been formulated by the authors‘ group but the challenge remains to effectively manage the system on the supervisory-level. In this paper, a power management controller is proposed to minimize fuel consumption while taking into account performance. The algorithm, a feedforward and cost-function combination considers operator commands, the DC actuators‘ power consumption and the power available from the engine and hydraulic hybrid as metrics. The developed strategy brings the technology closer to the predicted savings while achieving superior operability.

Buildings contribute a significant part to the electricity demand profile and peak demand for the electrical utilities. The addition of renewable energy generation adds additional variability and uncertainty to the power system. Demand side management in the buildings can help improve the demand profile for the utilities by shifting some of the demand from peak to off-peak times. Heating, ventilation and air-conditioning contribute around 45% to the overall demand of a building. This research studies two strategies for reducing the peak as well as shifting some demand from peak to off-peak periods in commercial buildings: 1. Use of gas heat pumps in place of electric heat pumps, and 2. Shifting demand for air conditioning from peak to off-peak by thermal energy storage in chilled water and ice. The first part of this study evaluates the field performance of gas engine-driven heat pumps (GEHP) tested in a commercial building in Florida. Four GEHP units of 8 Tons of Refrigeration (TR) capacity each providing air-conditioning to seven thermal zones in a commercial building, were instrumented for measuring their performance. The operation of these GEHPs was recorded for ten months, analyzed and compared with prior results reported in the literature. The instantaneous COPunit of these systems varied from 0.1 to 1.4 during typical summer week operation. The COP was low because the gas engines for the heat pumps were being used for loads that were much lower than design capacity which resulted in much lower efficiencies than expected. The performance of equivalent electric heat pump was simulated from a building energy model developed to mimic the measured building loads. An economic comparison of GEHPs and conventional electrical heat pumps was done based on the measured and simulated results. The average performance of the GEHP units was estimated to lie between those of EER-9.2 and EER-11.8 systems. The performance of GEHP systems suffers due to lower efficiency at part load operation. The study highlighted the need for optimum system sizing for GEHP/HVAC systems to meet the building load to obtain better performance in buildings. The second part of this study focusses on using chilled water or ice as thermal energy storage for shifting the air conditioning load from peak to off-peak in a commercial building. Thermal energy storage can play a very important role in providing demand-side management for diversifying the utility demand from buildings. Model of a large commercial office building is developed with thermal storage for cooling for peak power shifting. Three variations of the model were developed and analyzed for their performance with 1) ice storage, 2) chilled water storage with mixed storage tank and 3) chilled water storage with stratified tank, using EnergyPlus 8.5 software developed by the US Department of Energy. Operation strategy with tactical control to incorporate peak power schedule was developed using energy management system (EMS). The modeled HVAC system was optimized for minimum cost with the optimal storage capacity and chiller size using JEPlus. Based on the simulation, an optimal storage capacity of 40-45 GJ was estimated for the large office building model along with 40% smaller chiller capacity resulting in higher chiller part-load performance. Additionally, the auxiliary system like pump and condenser were also optimized to smaller capacities and thus resulting in less power demand during operation. The overall annual saving potential was found in the range of 7-10% for cooling electricity use resulting in 10-17% reduction in costs to the consumer. A possible annual peak shifting of 25-78% was found from the simulation results after comparing with the reference models. Adopting TES in commercial buildings and achieving 25% peak shifting could result in a reduction in peak summer demand of 1398 MW in Tampa.

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.