All electric power utility companies are faced with yearly peak demands. They must supply sufficient generating capacity along with transmission and distribution facilities to carry these loads. In the past, peaking requirements of many utilities have been met by the use of gas turbines, which are not as efficient as base load plants, but require substantially lower capital investments. However, the fuel supplies used for gas turbines are becoming extremely difficult and expensive to procure and as a result, other means for meeting peak demands are being examined. Energy management attempts to modify the power systems' load requirements to fit the systems' generating capacity, rather than supplying the generating capacity to meet the systems' load. Irrigation loads comprise one of the largest single demands placed upon the Utah Power & Light (U.P.& L.) system. Because of their seasonal nature, they contribute extensively to the system load, representing nearly 30% of the peak demand, but only 5% of the kilowatt-hour sales. In Idaho alone last year, irrigation consumed 47% of U.P.& L. 's capital investment while returning only 28% to the Company's revenue. This study examines the potential s for implementing energy management programs in pump testing, irrigation water management , and irrigation load management within U.P.& L. 's service area which might be used to help alleviate these peak demand problems .

The environmental and economic consequences of climate change are severe and are being exacerbated by increased global carbon emissions. In the United States, buildings account for over 40% of all domestic and 7.4% of all global CO2 emissions and therefore represent an important target for energy conservation initiatives. Even marginal energy savings across all buildings could have a profound effect on carbon emission mitigation. In order to realize the full potential of energy savings in the building sector, it is essential to maximize the energy efficiency of both buildings and the behavior of occupants who occupy them. In this vein, systems that collect and communicate building energy-use information to occupants (i.e. eco-feedback systems) have been demonstrated to motivate building occupants to significantly reduce overall building energy consumption. Furthermore, advancements in building sensor technologies and data processing capabilities have enabled the development of advanced eco-feedback systems that also allow building occupants to share energy-use data with one another and to collectively act to reduce energy consumption. In addition to monitoring building occupant energy-use, these systems are capable of collecting data about specific conservation actions taken by occupants and their interactions with different features of the eco-feedback system. However, despite recent advancements in eco-feedback and building sensor technologies, very few systems have been specifically designed to enable research on the effectiveness of different behavior-based energy conservation strategies in commercial buildings. Consequently, very little research has been conducted on how access to such systems impacts the energy-use behavior of building occupants. In this dissertation, I describe how my research over the past three years has advanced an understanding of how eco-feedback systems can impact the energy-use behavior of commercial building occupants. First, I present a novel eco-feedback system that I developed to connect building occupants over energy-use data and empower them to conserve energy while also collecting data that enables controlled studies to quantify the impacts of a wide variety of energy conservation strategies. Next, I present a commercial building study in which this eco-feedback system was used to investigate the effects of organizational network dynamics on the energy-use of individuals. I then introduce a new set of metrics based on individual energy-use data that enables the classification of individuals and building occupant networks based on their energy-use efficiency and predictability. I describe the principles behind the construction of these metrics and demonstrate how these quantitative measures can be used to increase the efficacy of behavior-based conservation campaigns by enabling targeted interventions. I conclude the dissertation with a discussion about the limitations of my research and the new research avenues that it has enabled. / Ph. D.

Energy usage within the world is increasing at a drastic rate. Buildings currently consume a major amount of the total energy used within the United States, and most of this energy usage supports heating and cooling. This demand shows that new passive energy management systems are needed. The use of Increased Thermal Capacitance (ITC) is proposed as a new passive energy management system. To increase thermal capacitance, a piping system is either added into a building’s walls or ceiling. In this paper, a building with ITC added is compared to a similar building without ITC using the simulation program TRNSYS. Along with a comparison between the walls and ceiling, several parameters are analyzed for their effect on the performance of the ITC. ITC was found to be effective especially when located in the ceiling, with the location, specific heat and tank size being the most important factors.

This thesis proposes a novel framework for solving the energy management problem of Hybrid Electric Vehicles (HEVs). We aim to establish a practical and effective approach targeting an optimal Energy Management Strategy (EMS). A situation-specific Equivalent Consumption Minimization Strategy (ECMS) is developed to minimize fuel consumption and improve battery charge sustainability while maintaining an acceptable drive quality. The investigated methodology will be broadly applicable to all HEV applications; however, it will be well-suited for hybrid electric delivery applications. / Thesis / Master of Applied Science (MASc)

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Personal vehicles have made great contributions to our life and satisfy our daily mobility needs. However, they have also caused societal issues, such as air pollution and global warming. Further to the recent attention to low-carbon energy technologies and environmentally friendly mobility, hybrid electric vehicles play an important role in the current automotive industry. As a leading center and an educational institution in Canada, McMaster University wants to build a Hybrid Electric Vehicle Powertrain Laboratory for introducing undergraduate students to hybrid powertrain architectures, instrumentation and control. A phased development of the hybrid powertrain teaching laboratory is being pursued. The first phase is to design a electric motor laboratory, as a platform for demonstrating motor characteristics. A LabVIEW based interface is designed to enable electric motor characterization tests. This laboratory set-up is still under construction. Real experiments would be implemented, once finishing the utility connections. For the hybrid powertrain laboratory, an innovative design architecture is proposed to enable different hybrid architectures, such as series, parallel, and power-split modes to be investigated. Instead of a planetary gearbox, bevel gearboxes with a continuous variable transmission (CVT) are used for making the laboratory more compact and flexible for demonstrating hybrid functionalities. The additional generator provides the ability of input power-split for allowing the engine to operate at a narrow high efficiency region. After designing the hybrid laboratory, a novel rule-based energy management strategy is applied to a simplified simulation model. / Thesis / Master of Applied Science (MASc)

There has been an increased necessity to search for alternative transportation methods, mainly driven by limited fuel availability and the negative impacts of climate change and exhaust emissions. These factors have lead to increased regulations and a societal shift towards a cleaner and more e cient transportation system. Automotive and technology companies need to be looking for ways to reshape mobility, enhance safety, increase accessibility, and eliminate the ine ciencies of the current transportation system in order to address such a shift. Hybrid vehicles are a popular solution that address many of these goals. In order to fully realize the bene ts of hybrid vehicle technology, the power distribution decision needs to be optimized. In the past, global optimization techniques have been dismissed because they require knowledge of the journey of the vehicle in advance, and are generally computationally extensive. Recent advancements in technologies, like sensors, cameras, lidar, GPS, Internet of Things, and computing processors, have changed the basic assumptions that were made during the vehicle design process. In particular, it is becoming increasingly possible to know future driving conditions. In addition to this, autonomous vehicle technology is addressing many safety and e ciency concerns. This thesis considers and integrates recent technologies when de ning a new approach to hybrid vehicle supervisory controller design and optimization. The dynamic programming algorithm has been systematically applied to an autonomous vehicle with a power-split hybrid electric powertrain. First, a more realistic driving cycle, the Journey Mapping cycle, is introduced to test the performance of the proposed control strategy under more appropriate conditions. Techniques such as vectorization and partitioning are applied to improve the computational e ciency of the dynamic programming algorithm, as it is applied to the hybrid vehicle energy management problem. The dynamic programming control algorithm is benchmarked against rule-based algorithms to substantively measure its bene ts. It is proven that the DP solution improves vehicle performance by at least 9 to 17% when simulated over standard drive cycles. In addition, the dynamic programming solution improves vehicle performance by at least 32 to 39% when simulated over more realistic conditions. The results emphasize the bene ts of optimal hybrid supervisory control and the need to design and test vehicles over realistic driving conditions. Finally, the dynamic programming solution is applied to the process of adaptive control calibration. The particle swarm optimization algorithm is used to calibrate control variables to match an existing controller's operation to the dynamic programming solution. / Thesis / Master of Applied Science (MASc)

Recent advances in low-power electronics, energy harvesting, and sensor technologies are poised to revolutionize mobile and embedded computing, by enabling networks of mobile sensor devices that are long-lived and self-managing. When realized, this new generation of perpetual systems will have a far-reaching and transformative impact, improving scientists’ ability to observe natural phenomena, and enabling many ubiquitous computing applications for which regular maintenance is not feasible. In spite of these benefits, perpetual systems face many programming and deployment challenges. Conditions at runtime are unknown and highly variable. Variations in harvested energy and energy consumption, as well as mobility-induced changes in network connectivity and bandwidth require systems that are able to adapt gracefully at run-time to meet different circumstances. However, when programmers muddle adaptation details with application logic, the resulting code is often difficult to both understand and maintain. Relying on system designers to correctly reason about energy fluctuations and effectively harness opportunities for cooperation among mobile nodes, is not a viable solution. This dissertation demonstrates that perpetual systems can be designed and deployed without sacrificing programming simplicity. We address the challenges of perpetual operation and energy-aware data delivery in the context of several applications, including in situ wildlife tracking and vehicular networks. Specifically, we focus on two specific systems. Eon, the first energy-aware programming language, allows programmers to simply express application specific energy policies and then delegate the complexities of energy-aware adaptation to the underlying system. Eon automatically manages application energy in order to indefinitely extend a device’s operating lifetime, requiring only simple annotations from the programmer. The second system, Tula, is a system that automatically balances the inherently dependent activities of data collection and data delivery, while also ensuring that devices have fair access to network resources. In our experiments, Tula performs within 75% of the optimal max-min fair rate allocation.

An energy management strategy for a prototype P4 parallel hybrid Chevrolet Blazer is developed for the EcoCAR Mobility Challenge. The objective of the energy management strategy is to reduce energy consumption while maintaining the drive quality targets of a conventional vehicle. A comprehensive model of the hybrid powertrain and vehicle physics is constructed to aid in the development of the control strategy. To improve fuel efficiency, a Willans line model is developed for the conventional powertrain and used to develop a rule-based torque split strategy. The strategy maximizes high efficiency engine operation while reducing round trip losses. Calibratable parameters for the torque split operating regions allow for battery state of charge management. Torque request and filtering algorithms are also developed to ensure the hybrid powertrain can smoothly and reliably meet driver demand. Vehicle testing validates that the hybrid powertrain meets acceleration response targets while delivering an enjoyable driving experience. Simulation testing shows that the energy management strategy improved fuel economy in most drive cycles with improvements of 8.8% for US06, 9.8% for HWFET, and 0.1% for the EcoCAR Mobility Challenge Cycle. Battery state of charge management behavior is robust across a variety of drive cycles using inputs from both simulated and test drivers. The resulting energy management strategy delivers an efficient, responsive, and reliable hybrid electric vehicle. / Master of Science / A control strategy for a hybrid vehicle is developed to improve fuel efficiency without sacrificing vehicle responsiveness. Efficiency improvements are achieved by the strategy intelligently selecting to use the engine, motor, or a combination of the two to minimize fuel consumption. The strategy also handles the important tasks of maintaining the battery pack charge and smoothly transitioning between the engine and motor power. All together, this results in a hybrid vehicle with both improved fuel economy and an enjoyable driving experience.