Energy Storage can Increase the Value and Flexibility of a Nuclear Power Plant but Makes Plant Economics More Subject to Uncertainty in Electricity Price

Regional electricity markets currently rely heavily on coal and natural gas resources to meet electricity demand. Intermittent renewable resources like wind and solar power are location-dependent and are not dispatchable resources. Flexible nuclear power, achieved through the use of integrated thermal energy storage, may be able to complement these renewable resources to provide clean, predictable power in an evolving electricity grid. This study considers such a flexible nuclear plant and seeks to address a fundamental modeling question: which uncertain model inputs have a significant effect on the economics of the system, and to what degree? These questions are addressed by conducting a sensitivity analysis using electricity price data from two regional US electricity markets

A Memory Controller for FPGA Applications

As designers and researchers strive to achieve higher performance, field-programmable gate arrays (FPGAs) become an increasingly attractive solution. As coprocessors, FPGAs can provide application specific acceleration that cannot be matched by modern processors. Most of these applications will make use of large data sets, so achieving acceleration will require a capable interface to this data. The research in this thesis describes the design of a memory controller that is both efficient and flexible for FPGA applications requiring floating point operations. In particular, the benefits of certain design choices are explored, including: scalability, memory caching, and configurable precision. Results are given to prove the controller\u27s effectiveness and to compare various design trade-offs

Data-Driven Techno-Economic Analysis, Optimization, And Uncertainty Quantification of Integrated Energy Systems in Deregulated Electricity Markets

Electricity is a ubiquitous energy source in daily life, powering everything from stovetops and cellphones to vehicles and industrial processes. While wind and solar power have become increasingly common sources of electricity, the majority of electricity is still produced by burning fossil fuels, releasing greenhouse gases and propelling climate change. Wind and solar power cannot economically replace these fossil fuel energy sources on their own because they do not produce consistent power; the wind must be blowing, and the sun must be shining for them to make electricity. Nuclear power is a reliable source of energy that does not generate greenhouse gases when generating electricity but is not flexible enough to directly replace existing traditional generators. Pairing these nuclear power plants with energy storage technologies like “thermal batteries” could help them find this needed flexibility, but the economics of these plants are not well-understood. In particular, there is significant uncertainty in construction costs, operating costs, and what revenue these plants would bring in. This dissertation first puts a number to these uncertainties. While the construction costs for advanced nuclear power plants are the greatest source of uncertainty, the uncertainty in revenue is also significant for some markets, and many past studies have not considered this source of uncertainty. One way to measure the effect of variation in quantities which change over time like the electricity demand, renewable energy production, and electricity price is to use a statistical model that describes how these values change over time, then use that model to create many scenarios over which the energy system can be modeled. Previous studies have used models which are either not very well suited for modeling these quantities or use models which are not interpretable. Both having realistic scenarios and having an understanding of how these values relate to each other over time is important for understanding the electricity markets that energy systems operate in. Neural stochastic differential equations are used for the first time in energy systems studies for this purpose in this dissertation, and they are shown to perform comparably to state-of-the-art machine learning models while being more interpretable. The neural stochastic differential equation model developed here is used to optimize a nuclear power plant with thermal energy storage in the ERCOT market in Texas, which was the most sensitive to time series uncertainty of the markets considered earlier. A neural network model is used to estimate the price of electricity from the electricity demand, renewable energy generation, the amount of each type of generator in the market, and the price of natural gas, and this model is used to estimate how much the flexible nuclear plant will decrease the price of electricity and therefore the plant revenue. This analysis is performed for various cases of plant price and system sizes. The energy storage system makes the plant more profitable in almost all cases, but no benefit was seen for very expensive plants in small markets. This shows that adding energy storage to make nuclear power plants more flexible can make them more cost-competitive in electricity markets in many cases, though this should be evaluated on a market-by-market behavior

Developing New Catalysts and Methods for Catalyst-transfer Polycondensations (CTP).

Chapter 1 provides a general introduction and brief history of the controlled synthesis of pi-conjugated polymers via catalyst-transfer polycondensation (CTP). We focus on the mechanistic underpinnings, as well as controversial hypotheses supporting information. The monomer scope is investigated, illustrating the current limitation of primarily electron-rich monomers. We also examine new materials that have been accessed via CTP. Chapter 2 discusses the impact of an associative intermediate in Ni-catalyzed Kumada cross-couplings and CTP. We observed preferential intramolecular oxidative addition even when a stoichiometric amount of competitive agent was present. At higher concentrations of competitive agent, we observed electron-rich bidentate phosphines showed higher amounts of intramolecular oxidative addition compared to electron-poor ligand analogues. Further study illustrated that these trends were also present in CTP polymerizations stylizing Ni catalysts. Chapter 3 describes an N-heterocyclic carbene-ligated catalyst as a new route for CTP. Using a Pd-NHC catalyst, we observed the controlled polymerization of both phenylene and thiophene monomers. Additionally, this catalyst was able to synthesize block copolymers of thiophene and phenylene, regardless of addition order, indicating more complicated block structures could be achieved. Chapter 4 examines a new approach to try and address the limited monomer scope. We developed a small molecule model system for screening new CTP conditions for the synthesis of poly(2,5-bis(hexyloxy)phenylene ethynylene) (PPE). We specifically targeted conditions that showed preferential multi-functionalization under sub-stoichiometric quantities. Hundreds of screens lead to several conditions that favored multi-functionalization, but unfortunately these conditions exhibited step-growth behavior when PPE monomer was used. Further investigation revealed the intermediates in the small molecule model system were significantly more reactive than the starting materials, leading to preferential multi-functionalization without the presence of intramolecular oxidative addition. Comparison to Kumada CTP catalysts illustrated the need for small molecule screens to be tested over a range of starting material ratios. Chapter 5 describes our efforts at understanding the CTP mechanism and applying them towards new polymerization conditions. Future directions are outlined for each chapter, highlighting areas of needed research to address limited monomer scope of CTP. Additionally, relevant external papers that have been influenced by our work are also briefly discussed.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113350/1/zjbryan_1.pd

Emerging targeted strategies for the treatment of autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a widespread genetic disease that leads to renal failure in the majority of patients. The very first pharmacological treatment, tolvaptan, received Food and Drug Administration approval in 2018 after previous approval in Europe and other countries. However, tolvaptan is moderately effective and may negatively impact a patient's quality of life due to potentially significant side effects. Additional and improved therapies are still urgently needed, and several clinical trials are underway, which are discussed in the companion paper Müller and Benzing (Management of autosomal-dominant polycystic kidney disease-state-of-the-art) Clin Kidney J 2018; 11: i2-i13. Here, we discuss new therapeutic avenues that are currently being investigated at the preclinical stage. We focus on mammalian target of rapamycin and dual kinase inhibitors, compounds that target inflammation and histone deacetylases, RNA-targeted therapeutic strategies, glucosylceramide synthase inhibitors, compounds that affect the metabolism of renal cysts and dietary restriction. We discuss tissue targeting to renal cysts of small molecules via the folate receptor, and of monoclonal antibodies via the polymeric immunoglobulin receptor. A general problem with potential pharmacological approaches is that the many molecular targets that have been implicated in ADPKD are all widely expressed and carry out important functions in many organs and tissues. Because ADPKD is a slowly progressing, chronic disease, it is likely that any therapy will have to continue over years and decades. Therefore, systemically distributed drugs are likely to lead to potentially prohibitive extra-renal side effects during extended treatment. Tissue targeting to renal cysts of such drugs is one potential way around this problem. The use of dietary, instead of pharmacological, interventions is another

Chemical nonlinearities in relating intercontinental ozone pollution to anthropogenic emissions

Model studies typically estimate intercontinental influence on surface ozone by perturbing emissions from a source continent and diagnosing the ozone response in the receptor continent. Since the response to perturbations is non-linear due to chemistry, conclusions drawn from different studies may depend on the magnitude of the applied perturbation. We investigate this issue for intercontinental transport between North America, Europe, and Asia with sensitivity simulations in three global chemical transport models. In each region, we decrease anthropogenic emissions of NOx and nonmethane volatile organic compounds (NMVOCs) by 20% and 100%. We find strong nonlinearity in the response to NOx perturbations outside summer, reflecting transitions in the chemical regime for ozone production. In contrast, we find no significant nonlinearity to NOx perturbations in summer or to NMVOC perturbations year-round. The relative benefit of decreasing NOx vs. NMVOC from current levels to abate intercontinental pollution increases with the magnitude of emission reductions

User-Relative Names for Globally Connected Personal Devices

Nontechnical users who own increasingly ubiquitous network-enabled personal devices such as laptops, digital cameras, and smart phones need a simple, intuitive, and secure way to share information and services between their devices. User Information Architecture, or UIA, is a novel naming and peer-to-peer connectivity architecture addressing this need. Users assign UIA names by "introducing" devices to each other on a common local-area network, but these names remain securely bound to their target as devices migrate. Multiple devices owned by the same user, once introduced, automatically merge their namespaces to form a distributed "personal cluster" that the owner can access or modify from any of his devices. Instead of requiring users to allocate globally unique names from a central authority, UIA enables users to assign their own "user-relative" names both to their own devices and to other users. With UIA, for example, Alice can always access her iPod from any of her own personal devices at any location via the name "ipod", and her friend Bob can access her iPod via a relative name like "ipod.Alice".Comment: 7 pages, 1 figure, 1 tabl