<p>An increasing concern over environmental impacts of fossil fuels and sustainability of energy resources is leading to significant changes in the electric power systems. Decentralized power generation, in particular, is emerging as one of the most effective and promising tools in addressing these concerns.</p> <p>Microgrids are small-scale electricity grids with elements of load, generation and storage. Microgrids have emerged as an essential building block of a future smart grid, and an enabling technology for distributed power generation and control. This thesis presents an optimization-based approach for the design and control of energy management systems (EMS) for electric microgrids. A linear programming formulation of power/energy management is proposed to minimize energy cost for a microgrid with energy storage and renewable energy generation, by taking advantage of time-of-use (TOU) pricing. The thesis also addresses the issue of sizing of the battery storage and solar power generation capacity by formulating and solving a mixed integer linear programming (MILP) problem. The aim of the optimization is to minimize the combined capital and electricity usage cost subject to applicable physical constraints. Several case scenarios are analyzed for grid-connected microgrids in residential, commercial and industrial settings, as well as a case of an islanded microgrid intended for a remote community.</p> <p>Finally, the thesis investigates circuit level control of a microgrid with EMS. A finite state machine based control logic is proposed that enables outage ride through and smooth transition between islanded and grid connected operation. Simulation results are provided to demonstrate the effectiveness of the proposed controller under various possible scenarios.</p> / Master of Applied Science (MASc)

The behavior of all cyber-systems in a data center or an enterprise system largely depends on their configuration which describes the resource allocations to achieve the desired goal under certain constraints. Poorly configured systems become a bottleneck for satisfying the desired goal and add to unnecessary overheads such as under-utilization, loss of functionality, poor performance, economic burden, energy consumption, etc. Ill-effects related to system misconfiguration are well documented with quantifiable metrics showing their impact on the economy, security incidents, service recovery time, loss of confidence, social impact, etc. However, configuration modeling and diagnosis of data center systems is challenging because of the complexities of subsystem interactions and the many (known and unknown) parameters that influence the behavior of the system. Further, a configuration is not a static object - but a dynamically evolving entity that requires changes (either automatically or manually) to address the evolving state of the system. We believe that a well-defined approach for configuration modeling is important as it paves a path to keeps the systems functioning properly in spite of the dynamic changes to configurations. Proper configuration of large systems is difficult because interdependencies between various configuration parameters and their impact on performance or other attributes of the system are generally poorly understood. Consequently, properly configuring a system or a subsystem/device within it is largely dependent on expert knowledge developed over time. In this work, we attempt to formalize some approaches to configuration management, particularly in the area of network devices and Cloud/Edge storage solutions. In particular, we address the following aspects in this study: (i) impact of resource allocation on the energy-performance trade-off, with a network topology as an example, (ii) prediction of performance of a complex IT system (such as Cloud Storage Gateway or an Edge Storage Infrastructure) under given conditions, (iii) development of a data-driven method to efficiently configure (allocate resources) to satisfy required QoS levels under constrained conditions, and (iv) a model to express configuration health as a quantifiable metric. With increasing stress on data center networks and correspondingly increasing energy consumption, we propose a method to simultaneously configure routing and energy management related parameters to ensure that the network can both avoid congestion and maximize opportunities for putting network ports in lower power mode. We also study the problem of choosing hardware and resource settings to minimize cost and achieve a given level of performance. Because of the complexity of the problem, we explored machine learning (ML) based techniques. For concreteness, we studied the problem in the context of configuring a cloud storage gateway (CSG) that involves such parameters as speed and number of CPU cores, memory size, and bandwidth, IO size and bandwidth, data and metadata cache size, etc. It turns out that it is very difficult to obtain a reliable ML model for this, and instead our approach is to use a model for the opposite problem (predicting optimal cost or performance for a given configuration) along with meta-heuristic such as genetic algorithm or simulated annealing. We show that an intelligent grouping of configuration parameters based on expected relationships between parameters and relative importance of the groups substantially outperforms the standard meta-heuristic based exploration of the state space. Our work in the configuration space revealed a dominant void, we noticed the absence of common vocabulary or quantifiable metric to clearly and unambiguously express the quality of the configuration. In our diagnosis work, we designed a model to define a simple, reproducible, and verifiable metric that allows users to express the quality of device configuration as a health score. Our configuration diagnosis model expresses the strength (or weakness) of a configuration as a ‘Health Index’, a vector of dimensions like performance, availability, and security. This health index will help users/administrators to identify the weak configuration objects and take remedial actions to rectify the configurations. Our work on Configuration Modeling and Diagnosis addresses an important topic in this vast chaotic space. Using industry-driven problems and empirical data, we bring in some meaning to this complex problem. Though our research and experiments involved specific devices (network topology, Cloud Gateway, Edge Storage, network routers, etc.) - we show that the proposed solution is generic and can be adequately applied to other domains. We hope that this work will encourage other communities to explore new 'configuration' challenges in a rapidly changing IT landscape. / Computer and Information Science

The accurate and reliable estimation of the State-of-Charge (SOC) and State-of-Health (SOH) of Li-ion batteries is paramount to the safe and reliable operation of any electrified vehicle. Not only is accuracy and reliability necessary, but these estimation techniques must also be practical and intelligent since their use in real world applications can include noisy input signals, varying ambient conditions and incomplete or partial sequences of measured battery data. To that end, a novel framework, utilizing deep learning techniques, is considered whereby battery modelling and state estimation are performed in a single unified step. For SOC estimation, two different deep learning techniques are used with experimental data. These include a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) and a Deep Feedforward Neural Network (DNN); each one possessing its own set of advantages. The LSTM-RNN achieves a Mean Absolute Error (MAE) of 0.57% over a fixed ambient temperature and a MAE of 1.61% over a dataset with ambient temperatures increasing from 10°C to 25°C. The DNN algorithm, on the other hand, achieves a MAE of 1.10% over a 25°C dataset while, at -20°C, a MAE of 2.17% is obtained. A Convolutional Neural Network (CNN), which has the advantage of shared weights, is used with randomized battery usage data to map raw battery measurements directly to an estimated SOH value. Using this strategy, average errors of below 1% are obtained when using fixed reference charge profiles. To further increase the practicality of this algorithm, the CNN is trained and validated over partial reference charge curves. SOH is estimated with a partial reference profile with the SOC ranging from 60% to 95% and achieves a MAE of 0.81%. A smaller SOC range is then used where the partial charge profile spans a SOC of 85% to 95% and a MAE of 1.60% is obtained. Finally, a fused convolutional recurrent neural network (CNN-RNN) is used to perform combined SOC and SOH estimation over constant charge profiles. This is performed by feeding the estimated SOH from the CNN into a LSTM-RNN, which, in turn, estimates SOC with a MAE of less than 0.5% over the lifetime of the battery. / Thesis / Doctor of Philosophy (PhD)

This thesis mainly focuses on parallel hybrid electric vehicle energy management problems considering fueling control and air path dynamics of a diesel engine. It aims to explore the concealed fuel-saving potentials in conventional energy management strategies, by employing detailed engine models. The contributions of this study lie on the following aspects: 1) Fueling control consists of fuel injection mass and timing control. By properly selecting combinations of fueling control variables and torque split ratio, engine efficiency is increased and the HEV fuel consumption is further reduced. 2) A transient engine model considering air path dynamics is applied to more accurately predict engine torque. A model predictive control based energy management strategy is developed and solved by dynamic programming. The fuel efficiency is improved, comparing the proposed strategy to those that ignore the engine transients. 3) A novel adaptive control-step learning model predictive control scheme is proposed and implemented in HEV energy management design. It reveals a trade-off between control accuracy and computational efficiency for the MPC based strategies, and demonstrates a good adaptability to the variation of driving cycle while maintaining low computational burden. 4) Two methods are presented to deal with the conjunction between consecutive functions in the piece-wise linearization for the energy management problem. One of them shows a fairly close performance with the original nonlinear method, but much less computing time. / Thesis / Doctor of Philosophy (PhD)

This dissertation introduces a hierarchical optimization framework for power converter-based energy management systems, with a primary focus on weight minimization. Emphasizing modularity and scalability, the research systematically tackles the challenges in optimizing these systems, addressing complex design variables, couplings, and the integration of heterogeneous models. The study begins with a comparative evaluation of various metaheuristic optimization methods applied to power inductors and converters, including genetic algorithm, particle swarm optimization, and simulated annealing. This is complemented by a global sensitivity analysis using the Morris method to understand the impact of different design variables on the design objectives and constraints in power electronics. Additionally, a thorough evaluation of different modeling methods for key components is conducted, leading to the validation of selected analytical models at the component level through extensive experiments. Further, the research progresses to studies at the converter level, focusing on a weight-optimized design for the thermal management systems for silicon carbide (SiC) MOSFET-based modular converters and the development of a hierarchical digital control system. This stage includes a thorough assessment of the accuracy of small-signal models for modular converters. At this point, the research methodically examines various design constraints, notably thermal considerations and transient responses. This examination is critical in understanding and addressing the specific challenges associated with converter-level design and the implications on system performance. The dissertation then presents a systematic approach where design variables and constraints are intricately managed across different hierarchies. This strategy facilitates the decoupling of subsystem designs within the same hierarchy, simplifying future enhancements to the optimization process. For example, component databases can be expanded effortlessly, and diverse topologies for converters and subsystems can be incorporated without the need to reconfigure the optimization framework. Another notable aspect of this research is the exploration of the scalability of the optimization architecture, demonstrated through design examples. This scalability is pivotal to the framework's effectiveness, enabling it to adapt and evolve alongside technological advancements and changing design requirements. Furthermore, this dissertation delves into the data transmission architecture within the hierarchical optimization framework. This architecture is not only critical for identifying optimal performance measures, but also for conveying detailed design information across all hierarchy levels, from individual components to entire systems. The interrelation between design specifications, constraints, and performance measures is illustrated through practical design examples, showcasing the framework's comprehensive approach. In summary, this dissertation contributes a novel, modular, and scalable hierarchical optimization architecture for the design of power converter-based energy management systems. It offers a comprehensive approach to managing complex design variables and constraints, paving the way for more efficient, adaptable, and cost-effective power system designs. / Doctor of Philosophy / This dissertation introduces an innovative approach to designing energy control systems, inspired by the creativity and adaptability of a Lego game. Central to this concept is a layered design methodology. The journey begins with power components, the fundamental 'Lego bricks'. Each piece is meticulously optimized for compactness, forming the robust foundation of the system. Like connecting individual Lego bricks into a module, these power components come together to form standardized power converters. These converters offer flexibility and scalability, similar to how numerous structures can be built from the same set of Lego pieces. The final layer involves assembling these power converters in order to construct comprehensive energy control systems. This mirrors the process of using Lego subassemblies to build larger, more intricate structures. At this system-level design, the standardized converters are integrated to optimize overall system performance. Key to this dissertation's methodology is an emphasis on modularity and scalability. It enables the creation of diverse energy control systems of varying sizes and functionalities from these fundamental units. The research delves into the intricacies of design variables and constraints, ensuring that each 'Lego piece' contributes optimally to the bigger picture. This includes exploring the scalability of the architecture, allowing it to evolve with technological advancements and design requirements, as well as examining data transmission within the system to ensure efficient data communication across all levels. In essence, this dissertation is about recognizing the potential in the smallest components and understanding their role in the grand scheme of the system. It is akin to playing a masterful game of Lego, where building something greater from small, well-designed parts leads to more efficient, adaptable, and cost-effective energy control system designs. This approach is particularly relevant for applications in transportation systems and renewable energy in remote locations, showcasing the universal applicability of this 'Lego game' to energy management.

The control center is a critical location in the power system infrastructure. Decisions regarding the power system’s operation and control are often made from the control center. These control actions are made possible through SCADA communication. This capability however makes the power system vulnerable to cyber attacks. Most of the decisions taken by the control center dwell on the measurement data received from substations. These measurements estimate the state of the power grid. Measurement-based cyber attacks have been well studied to be a major threat to control center operations. Stealthy false data injection attacks are known to evade bad data detection. Due to the limitations with bad data detection at the control center, a lot of approaches have been explored especially in the cyber layer to detect measurement-based attacks. Though helpful, these approaches do not look at the physical layer. This study proposes an anomaly detection system for the control center that operates on the laws of physics. The system also identifies the specific falsified measurement and proposes its estimated measurement value. / United States Department of Energy (DOE) National Renewable Energy Laboratory (NREL) / Master of Science / Electricity is an essential need for human life. The power grid is one of the most important human inventions that fueled other technological innovations in the industrial revolution. Changing demands in usage have added to its operational complexity. Several modifications have been made to the power grid since its invention to make it robust and operationally safe. Integration of ICT has significantly improved the monitoring and operability of the power grid. Improvements through ICT have also exposed the power grid to cyber vulnerabilities. Since the power system is a critical infrastructure, there is a growing need to keep it secure and operable for the long run. The control center of the power system serves mainly as the decision-making hub of the grid. It operates through a communication link with the various dispersed devices and substations on the grid. This interconnection makes remote control and monitoring decisions possible from the control center. Data from the substations through the control center are also used in electricity markets and economic dispatch. The control center is however susceptible to cyber-attacks, particularly measurement-based attacks. When attackers launch measurement attacks, their goal is to force control actions from the control center that can make the system unstable. They make use of the vulnerabilities in the cyber layer to launch these attacks. They can inject falsified data packets through this link to usurp correct ones upon arrival at the control center. This study looks at an anomaly detection system that can detect falsified measurements at the control center. It will also indicate the specific falsified measurements and provide an estimated value for further analysis.

This dissertation studies the problem of increasing battery life-time and reducing CPU heat dissipation without degrading system performance in commodity-OS-based general-purpose (GP) mobile computers using the dynamic voltage scaling (DVS) function of modern CPUs. The dissertation especially focuses on the impact of task scheduling on the effectiveness of DVS in achieving this goal. The task scheduling mechanism used in most contemporary general-purpose operating systems (GPOS) prioritizes tasks based only on their CPU occupancies irrespective of their deadlines. In currently available autonomous DVS schemes for GP mobile systems, the impact of this GPOS task scheduling is ignored and a DVS scheme merely predicts and enforces the lowest CPU speed that can meet tasks' deadlines without meddling with task scheduling. This research, however, shows that it is impossible to take full advantage of DVS in balancing energy/power and performance in the current DVS paradigm due to the mismatch between the urgency (i.e., having a nearer deadline) and priority of tasks under the GPOS task scheduling. This research also shows that, consequently, a new DVS paradigm is necessary, where a "DVS-friendly" task scheduling assigns higher priorities to more urgent tasks. The dissertation begins by showing how the mismatch between the urgency and priority of tasks limits the effectiveness of DVS and why conventional real-time (RT) task scheduling, which is intrinsically DVS-friendly cannot be used in GP systems. Then, the dissertation describes the requirements for "DVS-friendly GP" task scheduling as follows. Unlike the existing GPOS task scheduling, it should prioritize tasks by their deadline. But, at the same time, it must be able to do so without a priori knowledge of the deadlines and be able to handle the various tasks running in today's GP systems, unlike conventional RT task scheduling. The various tasks include sporadic tasks such as user-interactive tasks and tasks having dependencies on each other such as a family of threads and user-interface server/clients tasks. Therefore, the first major result of this research is to propose a new DVS paradigm for commodity-OS-based GP mobile systems in which DVS is performed under a DVS-friendly GP task scheduling that meets these requirements. The dissertation then proposes GPSched, a DVS-friendly GP task scheduling mechanism for commodity-Linux-based GP mobile systems, as the second major result. GPSched autonomously prioritizes tasks by their deadlines using the type of services that each task is involved with as the indicator of the deadline. At the same time, GPSched properly handles a family of threads and user-interface server/clients tasks by distinguishing and scheduling them as a group, and user-interactive tasks by incorporating a feature of current GPOS task scheduling — raising the priority of a task that is idle most of the time — which is desirable to quickly respond to user input events in its prioritization mechanism. The final major result is GPScheDVS, the integration of GPSched and a task-based DVS scheme customized for GPSched called GPSDVS. GPScheDVS provides two alternative modes: (1) the system-energy-centric (SE) mode aiming at a longer battery life-time by reducing system energy consumption and (2) the CPU-power-centric (CP) mode focusing on limiting CPU heat dissipation by reducing CPU power consumption. Experiments conducted under a set of real-life usage scenarios on a laptop show that the best, worst, and average reductions of system energy consumption by the SE mode GPScheDVS were 24%, -1%, and 17%, respectively, over the no-DVS case and 11%, -1%, and 5%, respectively, over the state-of-the-art task-based DVS scheme in the current DVS paradigm. The experiments also show that the best, worst, and average reductions of CPU energy consumption by the SE mode GPScheDVS were 69%, 0%, and 43% over the no-DVS case and 26%, -1%, and 13% over the state-of-the-art task-based DVS scheme in the current DVS paradigm. Considering that no power management was performed on non-CPU components for the experiments, these results imply that the system energy savings achievable by GPScheDVS will be increased if the non-CPU components' power is properly managed. On the other hand, the best, worst, and average reductions of average CPU power by the CP mode GPScheDVS were 69%, 49%, and 60% over the no-DVS case and 63%, 0%, and 30% over the existing task-based DVS scheme. Furthermore, oscilloscope measurements show that the best, worst, and average reduction of peak system power by the CP mode GPScheDVS were 29%, 10%, and 23% over the no-DVS case and 28%, 6%, and 22% over the existing task-based DVS scheme signifying that GPScheDVS is effective also in restraining the peak CPU power. On the top of these advantages in energy and power, the experimental results show that GPScheDVS even improves system performance in either mode due to its deadline-based task scheduling property. For example, the deadline meet ratio on continuous videos by GPScheDVS was at least 91.2%, whereas the ratios by the no-DVS case and the existing task-based DVS scheme were down to 71.3% and 71.0%, respectively. / Ph. D.

Balancing indoor comfort and energy consumption is crucial to building energy efficiency. Occupancy information is a vital aspect in this process, as it determines the energy demand. Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a powerful deep learning algorithm that has been utilized in occupancy estimation. However, recently, an algorithm named Attention has emerged with improved performance. The study proposes a more effective model for occupancy level estimation by incorporating Attention into the existing Long Short-Term Memory algorithm. The results show that the proposed model is more accurate than using a single algorithm and has the potential to be integrated into building energy control systems to conserve even more energy. / Master of Science / The motivation for energy conservation and sustainable development is rapidly increasing, and building energy consumption is a significant part of overall energy use. In order to make buildings more energy efficient, it is necessary to obtain information on the occupancy level of rooms in the building. Environmental sensors are used to measure factors such as humidity and sound to determine occupancy information. However, the relationship between sensor readings and occupancy levels is complex, making it necessary to use machine learning algorithms to establish a connection. As a subfield of machine learning, deep learning is capable of processing complex data. This research aims to utilize advanced deep learning algorithms to estimate building occupancy levels based on environmental sensor data.

Digital twins (DT) describe the integration of the physical and digital worlds with the aim of optimizing real world operations and functions. The digital twin concept has gained increasing attention across industries in the past decade including the building sector. However digital twins remain ambiguous with various existing definitions and characteristics. While DTs include all life cycle phases, ultimately their goal is optimization of operations during the use phase. Of the building life cycle phases, building facilities management (FM) is responsible for considerable costs and energy consumption and has potential for improvement through DT implementation. Along with increased building information modeling (BIM) implementation, recent advances in data driven technologies have encouraged the exploration of DT in the building sector. BIM has been coupled with technologies such as internet of things (IoT), data analytics, and cloud computing to optimize various FM functions often resembling DT. This study has reviewed existing literature on digital twins in facilities management using a structured literature review and characterized similar characteristics and definitions by different authors. Additionally, DT implementation in different FM application areas was quantified and analyzed. Results show that DT implementation in FM is still at nascent stages with major challenges surrounding standardization and data integration. / Master of Science / Digital twins (DT) describe the integration of the physical and digital worlds with the aim of optimizing real world operations and functions. The digital twin concept has gained increasing attention across industries in the past decade including the building sector. However digital twins remain ambiguous with various existing definitions and characteristics. DTs include all building life cycle phases from design, construction, to operation and maintenance. Ultimately their goal is optimization of operations also referred to as facilities management during the use phase. Of the building life cycle phases, building facilities management (FM) is responsible for considerable costs and energy consumption and has potential for improvement through DT implementation. Building information modeling (BIM) describes geometric and semantic information of physical assets and has been used to optimize operations in FM. Along with increased BIM implementation, recent advances in data driven technologies have encouraged the exploration of DT in the building sector. BIM has been coupled with technologies such as internet of things (IoT), data analytics, and cloud computing to optimize various FM functions often resembling DT. This study has reviewed existing literature on digital twins in facilities management using a structured literature review and characterized similar characteristics and definitions by different authors. Additionally, DT implementation in different FM application areas was quantified and analyzed. Results show that DT implementation in FM is still at nascent stages with major challenges surrounding standardization and data integration.

Many buildings underperform leading to up to 20% energy waste. Case studies on monitoring-based commissioning (MBCx) have shown that using energy management and information systems (EMIS) for continuous energy monitoring and analysis enables the identification of issues that cause energy waste and verifies energy conservation measures. However, MBCx is underutilized by organizations leading to an energy efficiency gap between the energy saving potential of technologies like EMIS and observed savings. This energy efficiency gap can be attributed to general barriers to MBCx and barriers caused specifically by cognitive bias in the decision-making process. Using qualitative data from over 40 organizations implementing and practicing MBCx, this manuscript provides a better understanding of these barriers. Chapter 1 synthesizes and codes the qualitative data to develop a framework of variables acting as barriers and enablers to MBCx. The framework highlights commonly experienced barriers like data configuration, and also variables with conflicting results like payback/return on investment, which was experienced as a barrier to some organizations and enabler to others. Chapter 2 examines the barriers to MBCx through a behavioral decision science lens and finds evidence of cognitive biases, specifically, risk aversion, social norms, choice overload, status quo bias, information overload, professional bias, and temporal discounting. The success of choice architecture in other energy efficiency decisions is used to offer suggestions for ways to overcome these cognitive biases. This manuscript can be used by practitioners to better understand potential barriers to MBCx and by researchers to prioritize gaps and find methods to overcome the barriers to MBCx. / Master of Science / Buildings have the potential to save 20% of their energy use through the practice of monitoring-based commissioning (MBCx). MBCx involves continuous monitoring and analysis of a buildings energy use to quickly identify and resolve issues that cause energy waste. However, MBCx is underutilized due to technical and non-technical barriers. This manuscript uses qualitative data from over 40 organizations implementing and practicing MBCx to provides a better understanding of these barriers. Chapter 1 synthesizes and codes the qualitative data to develop a framework of variables acting as barriers and enablers to MBCx. The framework highlights commonly experienced barriers like data configuration, and also variables with conflicting results like payback/return on investment, which was experienced as a barrier to some organizations and enabler to others. Chapter 2 examines the barriers to MBCx through a behavioral decision science lens and finds evidence of cognitive biases, specifically, risk aversion, social norms, choice overload, status quo bias, information overload, professional bias, and temporal discounting. The success of choice architecture in other energy efficiency decisions is used to offer suggestions for ways to overcome these cognitive biases. This manuscript can be used by practitioners to better understand potential barriers to MBCx and by researchers to prioritize gaps and find methods to overcome the barriers to MBCx.