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Browsing by Author "Siegel, Howard Jay, committee member"

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    Aggregator-based residential demand response applications and carbon tax imposition on fossil-fuel generators
    (Colorado State University. Libraries, 2020) Algarni, Abdullah, author; Suryanarayanan, Siddharth, advisor; Maciejewski, Tony, committee member; Collins, George J., committee member; Siegel, Howard Jay, committee member; Bell, Michael, committee member
    Smart Grid Initiative started after realizing the urge for changes in conventional electric power grids. These changes should be made in response to a number of emerging issues in the electricity industry. The increasing involvement of renewable energy technologies, either as large- scale generators or as small-rated distributed generators (DGs), poses a challenge for the grid. The renewable energy generators being intermittent and uncontrollable brings worrying uncertainty at the supply side of the grid. This uncertainty makes the grid's operators anxious about balancing generation with load, which is a necessary condition for the security of the power system. Demand-side management (DSM) offers a promising solution for the uncontrollability of renewable energy. Residential customers, through new entities called demand response (DR) aggregators, can bring DR services for addressing the aforementioned intermittency in supply. A cost-minimization framework is set for power supply-demand adjustment with the involvement of variable resources (i.e., renewable energy generators). The resources in the power supply-demand adjustment problem are demand reduction through aggregators, power flow exchange between areas, and balancing generators' services. The method is simulated in the IEEJ East 30-machine test system after dividing it into 4 areas. The results of the followed method show a lower cost than the traditional method of using only balancing generators' services. This work builds on a previous work of researchers from Keio Univ in Japan. DR aggregators also use the Smart Grid Resource Allocation (SGRA) approach, which is a load shifting technique done by a DR aggregator. The DR aggregator performs a heuristic optimization in order to move part of residential appliances from peak to off-peak times. The effects of integrating multiple aggregators into the transmission level power grid are studied and simulated in the Roy Billinton test system (RBTS) after dividing it into 2 areas. The results show peak demand reductions, electricity prices reduction, and a lower peak-to-average ratio (PAR) for the system under consideration. In line with integrating DR aggregators, a carbon tax function from the work of Prof. W. Nordhaus, a Nobel Memorial Prize winner in economics sciences, is adopted to design a carbon emission-based tax function and apply it to the fossil-fueled generators in the system. The adopted carbon tax leads to less dispatch of coal and natural gas-based generators. As a result, CO2 emissions reduction is achieved and calculated using the set math models. The DR applications prove to represent a complementary element to the imposition of carbon taxation in achieving emissions-reduction. That is, imposing carbon taxation drives increases in electricity prices while applying DR reduces the mean electricity price by lowering the PAR of the system load profile. In addition, a testbed is designed to find a relationship between the aggregator's performance and utility pricing mechanisms. The experiment aims to find how utility pricing mechanisms affect the profitability of the aggregators and peak load shifting. These pricing mechanisms include fixed tariff, time-of-use (TOU) pricing, and real-time pricing (RTP). The simulation-based study shows that aggregators make the highest profits when run in parallel with utilities applying fixed tariffs, while they make the highest shifted peak load when run in parallel with utilities applying RTPs. Furthermore, survey-based data about the use patterns of three smart home appliances are incorporated in the SGRA approach. These three appliances include dishwashers, washing machines, and dryers. Besides using data about these appliances, additional rescheduling constraints are proposed to improve the comfort of participating customers. The results show profitability for the aggregator by using actual data of home appliances in tandem with additional rescheduling constraints to increase the comfort level of participating customers.
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    Design and implementation of a compact highly efficient 472kHz radio frequency generator for electrosurgery
    (Colorado State University. Libraries, 2011) Eberhardt, Gerald M., author; Collins, George J., advisor; Siegel, Howard Jay, committee member; Chen, Thomas Wei, committee member; Sakurai, Hiroshi, committee member
    This thesis explores the utilization of modern design practices and advance technologies to reduce the size of traditional 472kHz radio frequency generators used for electrosurgery. Achieving the reduced size requires an innovative approach to increase the overall efficiency to lower the internal heat dissipation allowing the overall package size to shrink. This thesis covers the selection and design process to achieving the final topology of an innovative approach utilizing a variation of the Class-D amplifier to produce a resonance type power saturation amplifier. While using a high-efficiency power source to control the amplifier voltage rails, and to control the amplitude of the output signal will produce a sinusoidal power source capable of driving a radio frequency surgical scalpel.
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    ItemEmbargo
    Energy-aware workload management for geographically distributed data centers
    (Colorado State University. Libraries, 2023) Hogade, Ninad, author; Pasricha, Sudeep, advisor; Siegel, Howard Jay, committee member; Maciejewski, Anthony, committee member; Anderson, Chuck, committee member
    Cloud service providers are distributing data centers globally to reduce operating costs while also improving the quality of service by using intelligent cloud management strategies. The development of time-of-use electricity pricing and renewable energy source models has provided the means to reduce high cloud operating costs through intelligent geographical workload distribution. However, neglecting essential considerations such as data center cooling power, interference effects from workload co-location in servers, net-metering, peak demand pricing of electricity, data transfer costs, and data center queueing delay has led to sub-optimal results in prior work because these factors have a significant impact on cloud operating costs, performance, and carbon emissions. This dissertation presents a series of critical research studies addressing the vital issues of energy efficiency, carbon emissions reductions, and operating cost optimization in geographically distributed data centers. It scrutinizes different approaches to workload management, considering the diverse, dynamic, and complex nature of these environments. Starting from an exploration of energy cost minimization through sophisticated workload management techniques, the research extends to integrate network awareness into the problem, acknowledging data transfer costs and queuing delays. These works employ mathematical and game theoretic optimization to find effective solutions. Subsequently, a comprehensive survey of state-of-the-art Machine Learning (ML) techniques utilized in cloud management is discussed. Then, the dissertation traverses into the realm of Deep Reinforcement Learning (DRL) based optimization for efficient management of cloud resources and workloads. Finally, the study culminates in a novel game-theoretic DRL method, incorporating non-cooperative game theory principles to optimize the distribution of AI workloads, considering energy costs, data transfer costs, and carbon footprints. The dissertation holds significant implications for sustainable and cost-effective cloud data center workload management.
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    Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems
    (Colorado State University. Libraries, 2015) Tarplee, Kyle M., author; Maciejewski, Anthony A., advisor; Siegel, Howard Jay, committee member; Chong, Edwin, committee member; Bates, Dan, committee member
    As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs inherent in the system, and determine which machines to provision. The extreme scale of these newer systems requires unique task scheduling algorithms that are capable of handling millions of tasks and thousands of machines. A highly scalable scheduling algorithm is developed that computes high quality schedules, especially for large problem sizes. Large-scale computing systems also consume vast amounts of electricity, leading to high operating costs. Through the use of novel resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. Trading-off energy and makespan is often difficult for companies because it is unclear how each affects the profit. A monetary-based model of high performance computing is presented and a highly scalable algorithm is developed to quickly find the schedule that maximizes the profit per unit time. As more high performance computing needs are being met with cloud computing, algorithms are needed to determine the types of machines that are best suited to a particular workload. An algorithm is designed to find the best set of computing resources to allocate to the workload that takes into account the uncertainty in the task arrival rates, task execution times, and power consumption. Reward rate, cost, failure rate, and power consumption can be optimized, as desired, to optimally trade-off these conflicting objectives.
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    Multi-criteria analysis in modern information management
    (Colorado State University. Libraries, 2010) Dewri, Rinku, author; Whitley, L. Darrell, advisor; Ray, Indrajit, 1966-, advisor; Ray, Indrakshi, committee member; Siegel, Howard Jay, committee member
    The past few years have witnessed an overwhelming amount of research in the field of information security and privacy. An encouraging outcome of this research is the vast accumulation of theoretical models that help to capture the various threats that persistently hinder the best possible usage of today's powerful communication infrastructure. While theoretical models are essential to understanding the impact of any breakdown in the infrastructure, they are of limited application if the underlying business centric view is ignored. Information management in this context is the strategic management of the infrastructure, incorporating the knowledge about causes and consequences to arrive at the right balance between risk and profit. Modern information management systems are home to a vast repository of sensitive personal information. While these systems depend on quality data to boost the Quality of Service (QoS), they also run the risk of violating privacy regulations. The presence of network vulnerabilities also weaken these systems since security policies cannot always be enforced to prevent all forms of exploitation. This problem is more strongly grounded in the insufficient availability of resources, rather than the inability to predict zero-day attacks. System resources also impact the availability of access to information, which in itself is becoming more and more ubiquitous day by day. Information access times in such ubiquitous environments must be maintained within a specified QoS level. In short, modern information management must consider the mutual interactions between risks, resources and services to achieve wide scale acceptance. This dissertation explores these problems in the context of three important domains, namely disclosure control, security risk management and wireless data broadcasting. Research in these domains has been put together under the umbrella of multi-criteria decision making to signify that "business survival" is an equally important factor to consider while analyzing risks and providing solutions for their resolution. We emphasize that businesses are always bound by constraints in their effort to mitigate risks and therefore benefit the most from a framework that allows the exploration of solutions that abide by the constraints. Towards this end, we revisit the optimization problems being solved in these domains and argue that they oversee the underlying cost-benefit relationship. Our approach in this work is motivated by the inherent multi-objective nature of the problems. We propose formulations that help expose the cost-benefit relationship across the different objectives that must be met in these problems. Such an analysis provides a decision maker with the necessary information to make an informed decision on the impact of choosing a control measure over the business goals of an organization. The theories and tools necessary to perform this analysis are introduced to the community.