ScholarWolf
Welcome to ScholarWolf, the institutional repository for the University of Nevada, Reno. Managed by the University Libraries, ScholarWolf is an open access database and the home of scholarly works by University members, including the electronic theses and dissertations of our graduate students, journal articles, conference presentations, and more.
Learn more about ScholarWolf and the submission process.
Recent Submissions
Item AIEI Newsletter, November 2024 (Quarter Three)(2024-11-05)Item Unraveling an uncertain future for the Great Basin: how climate, grass invasion, and wildfire interact to drive ecosystem function(2024)Drylands are the earth's largest terrestrial biome and are rapidly experiencing change. In drylands of the western United States, three of the most influential global change processes are shifting precipitation regimes, annual grass invasion, and wildfire. Understanding how these drivers interact to influence ecosystem function, and how such interactions may change in the future, can provide critical insight for building ecological resilience. This dissertation explores how changing moisture regimes interact with grass invasion and wildfire to modify three key cycles in the Great Basin: carbon (C), nitrogen (N), and water. This work bridges multiple spatial and temporal scales using empirical studies conducted in the field, greenhouse, and laboratory, along with simulation modeling. The work in chapter one used a greenhouse and laboratory experiment to identify how soil aridity influences root exudation by cheatgrass (Bromus tectorum), an invasive annual grass, alters the cycling of carbon (C) and nitrogen (N) in the soil. We observed that cheatgrass was able to uniquely access N in dry soils, and that changes in soil condition that implicate increased C and N cycling were caused when cheatgrass root exudates were experimentally added to soils. This work shows that root exudation by cheatgrass accelerates soil C cycling and suggests that it modifies N cycling and provides cheatgrass with unique access to N under dry conditions. When combined with seasonal dynamics and annual grass phenology, these mechanisms may provide cheatgrass with greater access to soil N compared to native species. In chapter two, I used simulation modeling to mechanistically test the effects of changing precipitation regimes and grass invasion on water and N dynamics at larger, watershed scales. Using the Regional Hydro-Ecologic Simulation System (RHESSys)�"a process-based ecohydrologic model that couples the cycling of water, C, and N�"I examined how precipitation variability and invasion influence stream discharge and N export. I simulated four climate scenarios with three levels of cheatgrass invasion in a factorial design. Streamflow decreased with both climate change and grass invasion, both of which increased rates of evapotranspiration. Watershed N export increased with climate change and invasion. This work can help us understand how change and annual grass invasion in the Great Basin may interact to influence ecosystem water supply and water quality. Chapter three investigated the role of aridity and burn severity on N retention after wildfire using a field and laboratory approach. I quantified how burn severity and aridity influence some of the key factors driving N uptake by plants and soil microbes after fire. Plant N uptake exhibited a non-monotonic relationship with burn severity, where sites that burned severely had suppressed N uptake for up to two years following fire. Dry conditions muted plant and microbial N uptake for all burn severities. N uptake was lowest after severe fire and when soils were relatively dry. Soil N that exceeded plant and microbial demand when N uptake is suppressed is likely more vulnerable to export. Using a conceptual framework, I hypothesize that postfire N loss is likely to occur at a lower burn severity when conditions are dry after fire. This hypothesis expands existing N saturation theory to better capture the role of fire and its interactions with soil aridity in drylands.Item Data-Driven Voltage Control of DERs Integrated Distribution Grids Using Deep Reinforcement Learning(2024)The proliferation of Distributed Energy Resources (DERs) in modern power distribution grids presents a significant challenge for voltage control and power loss due to their intermittent nature and diverse operational characteristics. Although different control strategies have been developed to improve the voltage profile of the distribution networks, Volt-VAR control (VVC) is one of the efficient tools that can be used to control the reactive power set-points of legacy reactive power resources or newly added inverters to regulate the voltage of distribution networks. VVC methods regulate the voltage by exploiting the reactive power absorption/injection capabilities of capacitor banks, and smart inverters. Although existing classical VVC control/ optimization based approaches can control the set-point of the slow responding legacy devices and fast responding smart inverters to control the the voltage of the distribution network, machine Learning (ML) based VVC/VVO approaches especially deep reinforcement learning (DRL) based VVC approaches has become popular for real time control of the voltage. DRL can optimally control the reactive power set-point of the inverter to minimize the voltage fluctuation and avoid the voltage violation. This dissertation investigates the application of Deep Reinforcement Learning (DRL) techniques for voltage control in distribution grids with high penetration of DERs. Leveraging the capabilities of artificial intelligence, specifically deep neural networks and reinforcement learning algorithms, our approach addresses the complexity of managing voltage levels in the presence of decentralized energy sources. It presents a comprehensive analysis of the challenges posed by high DER penetration and demonstrate the effectiveness of DRL-based control strategies in optimizing grid voltage profiles. By training deep reinforcement learning agents on real-time data from distribution networks with a diverse array of DERs, we achieve precise and adaptive voltage control. The proposed methodology not only ensures the stability and reliability of the distribution grid under varying operating conditions but also maximizes the utilization of renewable energy sources. Existing DRL based approaches do not consider the topology information of the network and they don't consider the consequence of missing/unobservable data. Therefore, incorporating graph convolution layers and generative adversarial network with DRL algorithm, this research address these problems. The implementation of the existing DRL based approaches in real power grids has been impeded by the absence of clear assurances regarding stability and safety. Therefore, this research utilizes a monotonically decreased neural network that can guarantee the stability of the policy during training and execution processes. The capability of the DRL based approaches in real-time implementation in tested using real-time digital simulator (RTDS). Through extensive simulations and comparative analyses, we showcase the superior performance of our DRL-based voltage control approach in comparison to traditional methods. The results underscore the potential of advanced machine learning techniques in enhancing the reliability and efficiency of distribution grids amidst the rapid integration of DERs. This research contributes valuable insights and practical solutions for utilities and researchers aiming to optimize voltage control strategies in distribution systems with a high penetration of renewable energy resources.
Communities in ScholarWolf
Select a community to browse its collections.