Open Theses & Dissertations

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Now showing 1 - 20 of 2041

Feasibility of producing non-structural wood products using trembling aspen lumber
(University of New Brunswick, 2025-01) Zhang, Mengyuan; Gong, Meng; Chui, Ying-Hei
Trembling aspen (Populus tremuloides) is abundant in Canada but underutilized in non-structural solid wood products. This study was aimed at evaluating the feasibility of using aspen lumber to produce flooring, moulding, and siding. The shrinkage, surface roughness, and wettability of aspen wood were tested. The surface hardness, screw withdrawal resistance, colour change, and dimensional stability of three products fabricated were examined. The wood yield of each product was analyzed. It was found that aspen showed superior machinability and wettability to silver maple and yellow poplar. Aspen flooring had a Brinell hardness of 13.47 MPa, 60% lower than silver maple. Aspen moulding exhibited a screw withdrawal resistance of 23.42 MPa, 15% higher than eastern white pine. Aspen siding showed comparable colour and dimensional stability to spruce-pine-fir wood. The aspen wood yields were estimated to be 38.25%, 25.4%, and 49.2% for flooring, moulding, and siding, suggesting its potential for non-structural applications.
Case studies on the life cycle assessment of lumber production and of tall wood buildings
(University of New Brunswick, 2025-01) Zahabi, Nadia; Gong, Meng; Gu, Hongmei
Mass timber provides a low-carbon alternative to steel and concrete, reducing global warming potential (GWP) and non-renewable energy use while acting as a carbon sink. Life cycle assessment (LCA) evaluates environmental impacts, supporting sustainable construction practices. This research includes three LCA case studies. The first examined softwood lumber production in New Brunswick, Canada, with emissions of 43 kg CO₂ eq/m³, relying on 58% renewable energy from woody biomass during kiln drying. The second analyzed hardwood lumber, emitting 41 kg CO₂ eq/m³ up to sawing, using 98% non-renewable grid energy. Both softwood and hardwood offset their emissions with stored carbon, achieving negative GWP. The third study compared cradle-to-grave impacts of hybrid mass timber-steel, full mass timber, steel, and concrete designs in the Bakers Place project, USA. Mass timber significantly reduced GWP and non-renewable energy but showed higher acidification and eutrophication impacts due to transportation and landfill decomposition.
Exploiting temporal dynamics to improve the robustness of continuous myoelectric control
(University of New Brunswick, 2025-01) Tallam Puranam Raghu, Shriram; Scheme, Erik J.; MacIsaac, Dawn T.
Myoelectric control based on Surface electromyography pattern recognition (sEMG-PR) offers intuitive and dexterous control of powered prostheses for people with limb differences. However, conventional sEMG-PR systems often struggle with transitions between movements, impacting online usability. In this thesis, we investigated these transition-specific challenges and proposed novel approaches to enhance the performance and user experience of sEMG-PR systems. We first established a comprehensive framework for evaluating classifier performance during transitions, incorporating transition-specific metrics and continuous dynamic datasets. This framework represents an improvement over conventional evaluation methods, which often focus primarily on steady-state performance and neglect transitions. Our analysis, utilizing this enhanced framework, revealed that classifiers, even with similar steady-state performance, can differ substantially in their ability to handle transitions. This finding underscores the limitations of conventional evaluation methods. Next, we systematically investigated various error-mitigation strategies, including existing and novel post-processing techniques. While some techniques showed promise, particularly those based on rejection, our findings suggest that relying solely on post-hoc error correction may not be sufficient to address the challenges of transitions effectively. Finally, we explored incorporating continuous dynamic data, inclusive of transitions, into the training process. Our results demonstrated the advantages of leveraging Long Short-Term Memory (LSTM) networks, which can effectively capture the dynamic nature of transitions. Furthermore, we pioneered the use of self-supervised learning for sEMG-PR, and demonstrated its effectiveness in learning meaningful and robust representations from unlabeled continuous dynamic data, leading to enhanced performance both offline and online. Our findings underscore the crucial role of temporal information, dynamic training data, and appropriate model selection, particularly temporal models like LSTMs, in achieving robust and reliable sEMG-PR based myoelectric control. The proposed approaches have the potential to significantly enhance the usability and effectiveness of these systems, paving the way for more intuitive and user-friendly prosthetic devices.
Learning dynamic regimes of event-based substructures in EEG data using Graph Kernel Koopman Embedding
(University of New Brunswick, 2025-01) Nagawara Muralinath, Rashmi; Mahanti, Prabhat K.
Understanding brain activity requires analyzing EEG data, which is challenging due to the high noise levels, non-linearity, non-stationarity, and individual variability. This thesis introduces a novel methodology using Graph Kernel Koopman Embedding (GKKE) methodology by representing time-evolving brain connectivity as low-dimensional, meta-stable regimes. The study focuses on two critical applications: detecting epileptic seizures (CHB-MIT dataset) and assessing cognitive workload (Cognitive Mental Workload dataset). This research attempts to classify cognitive and neurological states using various combinations of connectivity measures, graph kernels, and classifiers. The results demonstrate that the method has a good classification accuracy of above 85% for both datasets, thus demonstrating its potential to identify intricate patterns. The suggested method involves preprocessing the raw EEG data through which the connectivity matrix is obtained by calculating correlation coefficients and generating gram matrices. Next, we use kernel PCA to simplify the graph features by reducing their dimensions. After that, we test how well they work with machine learning classifiers.
Genetic variation in adventitious rooting, seed germination, and berry phenolic content of black elderberry (Sambucus canadensis) in New Brunswick
(University of New Brunswick, 2025-01) Germaine, Tanya Rae; Sacobie, Charles; Smith, Ron
Black elderberry (Sambucus canadensis), a North American shrub valued for its ecological and medicinal properties, remains underexplored compared to its European counterpart, Sambucus nigra. This study investigates genetic variation in seed germination, adventitious rooting, and phenolic content (chlorogenic acid and rutin) among wild S. canadensis populations in New Brunswick, Canada. Ten populations from diverse biogeographic zones were sampled. Germination success varied significantly (59%–78%), with coastal populations germinating faster. Phenolic concentrations ranged widely (chlorogenic acid: 487–1825 ng; rutin: 884–2404 ng), showing strong correlation (β = 0.735, p < 0.001). Root development showed limited site variability and no correlation with plant size. Results highlight substantial genetic and phenotypic diversity, underscoring the species’ potential for ecological restoration, sustainable agriculture, and bioactive compound production. This research informs population selection for adaptability and enhanced bioactive compound production.
Explainable decision-making framework concealed in graph statistical models of chemistry
(University of New Brunswick, 2025-01) El-Samman, Amer; De Baerdemacker, Stijn
Recent advancements in probabilistic models in chemistry have unlocked ground-breaking potential, yet these innovations come with heightened caution. Decisions made by techniques, such as neural network models, are seldom fully understood, even by developers themselves, making it difficult to integrate these models into an established scientific discourse. Nevertheless, their use remains widespread and likely to increase, as they generate predictions that rival or surpass traditional chemical models in efficiency. This great potential combined with a lack of explainability has placed these models under increasing scrutiny, leading to the field of explainable artificial intelligence. This thesis investigates graph probabilistic models of chemistry, particularly graph neural nets, to develop an explanatory framework of decision-making that can be quantitatively blueprinted and replicated. We probe the cryptic high-dimensional nature of the feature space of these models, compacting their dimensions to elucidate a decision-making framework based on the molecular substructures of chemistry. We then demonstrate that the decision-making framework of these models is organized around chemical formula language/syntax from which the hidden framework can be replicated, while also providing a novel way of exploring reactions. Finally, we show the completeness of these models by transferring their capabilities to solve a wide range of chemical problems, from predicting pKa values and NMR data to modeling electron density and solubility.
The role of landscape features on the distribution of freshwater mussels in the Lower Wolastoq, New Brunswick
(University of New Brunswick, 2025-01) Cusack, Sarah; Gray, Michelle
Freshwater mussels provide habitat, structural stream bed support, nutrient cycling and act as an indication of ecosystem health. There are eight species in the lower Wolastoq: Alewife Floater (Utterbackiana implicata), Eastern Floater (Pyganodon cataracta), Triangle Floater (Alasmidonta undulata), Eastern Elliptio (Elliptio complanata), Eastern Lampmussel (Lampsilis radiata), Tidewater Mucket (Atlanticoncha ochracea), Eastern Pearlshell (Margaritifera margaritifera), and Yellow Lampmussel (Lampsilis cariosa). This study assessed the distribution of available habitat for in relation to the landscape controls producing differences in regional energetic gradients. Species-specific habitat distribution models were validated using snorkel surveys at 34 sites. This study determined that landscape features can be employed to identify suitable habitat across the Wolastoq. Suitable habitat characteristics did not always guarantee rare species presence but identified areas for future conservation work. Lower-gradient habitats supported higher species richness, while higher gradient habitats hosted fewer specialist species. Sediment characterization revealed species-specific preferences for depositional or erosional habitat.
Towards a psychological understanding of police officer use of force decision-making
(University of New Brunswick, 2025-01) Canales-Portillo, Donaldo David; Campbell, Mary Ann
One of the most controversial aspects of policing is an officer’s legal authority to use coercion and physical force to overcome resistance and gain compliance from citizens. Research examining predictors of use of force have identified several police-citizen encounter characteristics, and suspect sociodemographics and behaviours, that are associated with use of force. Less empirical attention, however, has been given to the role of psychological factors in use of force decision-making. The current dissertation examined the relationship between two psychological constructs, decision-making styles and executive functioning, with police officer actions in a use of force simulation. Sworn police officers (N = 101) completed measures of decision-making styles (i.e., rational, intuitive, dependent, spontaneous, and avoidant) and executive functions (i.e., behavioural regulation, emotional regulation, and metacognition). Afterwards, officers participated in a use of force simulation requiring them to resolve a scenario with a passively resistant and noncompliant mock suspect. Primary outcome measures were officers’ performance quality and appropriateness of force used in the simulation, rated by expert use of force trainers from video recordings of the simulation. Participants predominantly endorsed rational and intuitive decision-making styles and had normative executive function abilities. Correlational analyses revealed that rational decision-making was associated with greater executive function capabilities, whereas avoidant decision-making was most strongly associated with poorer executive functions. Latent profile analysis identified three profiles of officers, labelled rational/high self-regulation, decisional-balance/moderate self-regulation, and decisional-avoidance/low self-regulation. Relative to the rational/high self-regulation profile, the latter profiles endorsed greater use of dependent, spontaneous, and avoidant decision-making styles, and had more problems with executive functions. No between-profile differences emerged with respect to performance quality and appropriateness of force in the simulation. Decision-making styles and executive function did not predict performance quality or inappropriate use of force; however, avoidant decision-making was significantly correlated with inappropriate force. Overall, the current findings can be used to inform the development of explanatory models of use of force decision-making rooted in psychological theory. Furthermore, the broad pattern of results can be used to inform training to minimize officer use of force, particularly inappropriate force, and enhance safety to officers and citizens alike.
Potential of using trembling aspen to make structural engineered wood products
(University of New Brunswick, 2025-01) Wang, Dawei; Gong, Meng; Chui, Ying-Hei
Trembling aspen (Populus tremuloides) is abundant in Canada but is widely considered an underutilized species. This study was aimed at evaluating the potential of using aspen lumber to produce cross-laminated timber (CLT), glued-laminated timber (glulam), and wood I-joists. The key mechanical properties of these engineered wood products, which were fabricated using a modified grading criterion, were examined. It was found that 1) The mean effective bending stiffness and characteristic bending moment resistance of 5-layer CLT specimens in the major-strength direction were 5,069×10⁹ N·mm²/m and 97.65×106 N·mm/m; 2) The mean apparent MOE and characteristic modulus of rupture of glulam specimens were 12,315 MPa and 27.00 MPa; and 3) The mean effective stiffness and characteristic bending moment resistance of wood I-joist specimens were 899×106 kN·mm2 and 9,730 kN·mm. It could be concluded that properly sorted aspen lumber could be used in the production of CLT, glulam, and wood I-joists for specific applications.
Patients’ and families’ perceptions of privacy of health information and data sharing in New Brunswick
(University of New Brunswick, 2024-12) Seeley, Joanna; Balcolm, Sarah; Durepos, Pam
Information sharing between different interoperable health databases is called health information exchange (HIE). Although the HIE of personal health information (PHI) can improve patient safety for Canadians, some provinces do not disclose PHI about patient safety incidents with federal and pan-Canadian patient safety surveillance systems. A frequently cited barrier to HIE by healthcare organizations is patients’ and families’ concerns for their privacy. This study, guided by qualitative descriptive design, explored patients’ and families’ perceptions of privacy and the secondary use of PHI collected, used, disclosed, and retained on patient safety events. Participants identified privacy criteria, conditions for the access of PHI, types of data for HIE, and the purpose of HIE. There is a significant opportunity for data custodians to use the research findings to create a patient-centric framework for the HIE of PHI.
Development of deep learning-based classification and unsupervised clustering methods for mineral mapping using remotely sensed hyperspectral data
(University of New Brunswick, 2024-12) Peyghambari, Sima; Zhang, Yun
Hyperspectral remotely sensed imagery is a powerful tool for mineral mapping. It captures detailed spectral information across hundreds of contiguous and narrow spectral bands to enable precise identification of various geological materials. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. However, traditional methods face significant challenges in effectively handling hyperspectral data's high dimensionality, nonlinear spectral features, and low signal-to-noise ratio (SNR). These challenges limit the accuracy of traditional machine-learning algorithms in mapping the spectral variations of minerals. This PhD research addresses these limitations through a comprehensive literature review and the development of new methods. It has resulted in two published journal papers and one submitted journal paper, presented across three chapters of this dissertation. The third chapter of this dissertation (published review paper) provides an updated systematic overview of hyperspectral missions, diagnostic minerals' spectral properties, and various geologic information extraction techniques, including preprocessing, dimension reduction, endmember retrieval, and important image classification methods from spaceborne/airborne HSI. It evaluates the advantages and limitations of the existing conventional methods of processing HSIs with the aim of geological mapping. The fourth chapter (published paper) aims to improve the accuracy of spectral-spatial deep learning extractors in classifying HSI datasets. While traditional deep learning methods such as fully connected neural networks (FCNN), convolutional neural networks (CNNs), and hybrid CNNs like mixed convolutions and covariance pooling (MCNN-CP) algorithms have shown promise, they face limitations in robustness and accuracy. This proposes an integrated 1D, 2D, and 3D CNN architecture to enhance the capability of spectral-spatial extractors, significantly improving classification accuracy and resilience. The fifth chapter (submitted) explores deep learning-based clustering methods for unsupervised mineral mapping, which are valuable in remote areas where ground truth data is scarce. These methods leverage HSIs' high-dimensional and redundant spectral features, using advanced clustering techniques to generate accurate mineral maps without requiring extensive labelled data. This research proposes a hybrid 3D-2D convolutional autoencoder to capture HSI's spatial and spectral diversity. The anticipated outcomes include enhanced accuracy and computational efficiency, ultimately improving the utility of HSI for geological studies and resource exploration.
foreglow
(University of New Brunswick, 2024-12) Johnson, Colin Uriah; Crawford, Lucas; Sinclair, Sue
foreglow is a long lyric poem investigating the intricate and often mercurial interplay between poetics, queer theory, and utopian potentiality. Over nine multi-register, hybrid genre sections, a menagerie of domestic and mundane scenes frame its critique of the human desire to impose order while acknowledging (and, yes, even longing for) the existential ambiguity that follows a collapse of spatiotemporal certainty. The poem resists stable narratives about desire as individual and collective histories intertwine with environmental and temporal dislocation, dissolving boundaries between internal reflection and external reality. The critical introduction to foreglow posits that lyric poetry can function as a dynamic, ritualistic site of encounter that challenges traditional narrative authority and interprets individual subjectivity through a critical lens. Drawing on Jonathan Culler's concept of the "iterable now" and Anne Carson's insights on erotic paradox, the introduction examines how lyric poetry creates a temporality that simultaneously acknowledges and resists discrete ontological categories such as presence and absence. By integrating José Esteban Muñoz's notion of queer futurity, the project highlights how aesthetic practice can offer alternative modes of temporality and subjectivity that transcend linear narratives. Through a close reading of foreglow and its graphic codes, as well as comparative analyses with poets such as John Wieners and Cody-Rose Clevidence, the dissertation investigates how disidentificatory practices and the subversion of conventional poetics facilitate new forms of relationality and pleasure. The final section of the introduction considers Lisa Robertson's influence on foreglow, emphasizing how unconventional approaches to temporality and language contribute to the poem's exploration of utopian potentialities. This research underscores the potential of lyric poetry to both reflect and reshape the experiences of desire, subjectivity, and community.
The “drive-through province” problem: New Brunswick’s transit space tourism, 1929-1999
(University of New Brunswick, 2024-12) Cox, Sean Christopher; Mullally, Sasha
This dissertation examines New Brunswick’s multi-decade change from a destination space to a transit space for modern leisure motorists. New Brunswick’s recreational reputation as a rustic sporting destination was originally manufactured by provincial authorities in the late 19th and early 20th century. This successful marketing scheme failed to keep pace with popular tourism trends, however, especially as automobility reshaped consumer experiences of travel time and relationships to destination spaces. Modernizing automobility and expanding road systems compressed time-space across North America, and the small province of New Brunswick struggled to maintain a competitive tourism identity. In response to these forces, aggressive provincial investments to improve infrastructure and launch marketing campaigns reframed the province as an attractive location easily reached and enjoyed by car. While proactive and innovative, many of these efforts only temporarily relieved what I describe as the province’s “transit space problem.” New Brunswick’s complex tourism history has been overshadowed by the perception that it is largely a “drive-through province.” Yet, archival evidence demonstrates a long-developing transit space problem was neither inevitable nor passively accepted. As a contribution to the historiography of tourism, this dissertation captures a rare transit space case-study applicable beyond Atlantic Canada and demonstrates that even a “drive-through province” deserves sustained historical examination.
Mechanical performance of Laser Powder Bed Fused maraging steel microlattices: Experimental and numerical evaluations
(University of New Brunswick, 2024-12) Behboodi, Behrang; Mohammadi, Mohsen
This study examines the design, fabrication, and mechanical performance of maraging steel BCCZ microlattices produced using Laser Powder Bed Fusion (LPBF) for energy absorption applications. Four lattice structures—rigid plate (RP), full strut plate (FSP), half strut plate (HSP), and parallel strut plate (PSP)—were analyzed for their compressive properties through uniaxial testing and finite element simulations using ABAQUS. The impact of plate configurations on mechanical behavior was evaluated, with simulation results calibrated and validated against experimental data, showing a deviation of only 1.1% in energy absorption. Among the designs, the rigid plate structure exhibited superior energy absorption capabilities in simulations, warranting further investigation of this design with one and three plates. These findings provide valuable insights into optimizing microlattice structures for improved mechanical performance, highlighting their potential in advanced energy absorption applications in additive manufacturing.
Pressure-based gait recognition: Feature extraction techniques for footwear-agnostic identity recognition
(University of New Brunswick, 2024-12) Salehi, Ala; Scheme, Erik
This research explores the development of a robust pressure-based gait recognition system, with a focus on reducing the impact of changes in footwear. Using two datasets; CASIA-D and a newly collected UNB dataset, we compare traditional and deep learning methods, including two novel architectures: UMAPNet for spatial feature learning and FootPart, a comprehensive spatiotemporal model. FootPart integrates local spatial partitioning with dynamic temporal modelling, achieving significant improvements in both closed-set and open-set verification tasks. Results show that FootPart maintains high accuracy under variable conditions, outperforming baseline models in identification tasks and demonstrating resilience to unseen footwear. This work underscores the importance of detailed spatial and temporal features in robust gait recognition, with implications for security, healthcare, and smart environments.
Floral trait evolution: Insights from bee-pollinated Penstemon to maternal effects in Brassica rapa
(University of New Brunswick, 2024-12) Rathnayake, Manoj Kaushalya; Parachnowitsch, Amy L.
My thesis explores the evolution of floral traits in angiosperms, focusing on phenotypic selection and maternal effects. Floral traits are influenced by both pollinators and abiotic factors, making it essential to study how these multiple selection agents shape various traits related to attraction, efficiency, and less-explored reward traits like nectar and pollen. Chapter 1 examines phenotypic selection in two bee-pollinated Penstemon species, analyzing 15 traits related to size, visual signals, pollinator fit, and rewards. Despite floral similarities, each species experienced different selection pressures, underlining the need to measure multiple functional traits to fully understand evolutionary dynamics. Chapter 2 explores how climate change-induced drought affects floral traits and pollinator interactions in Brassica rapa. A common garden experiment showed that drought reduced most floral traits except nectar concentration, which increased. Larger flowers were favored in drought conditions highlighting the context-dependent nature of maternal effects. While pollen limitation wasn't observed, water availability altered selection on plant height. Chapter 3 investigates how maternal effects and plasticity influence floral traits in Brassica rapa under different water conditions. Maternal drought stress significantly impacted offspring traits related to pollinator attraction and efficiency, particularly under continued drought. This highlights the importance of considering both maternal effects and environmental context in predicting plant responses to climate change. Overall, my work emphasizes the need for integrating studies on maternal effects and phenotypic selection to better understand how plants adapt to environmental stressors, enhancing knowledge of the evolutionary processes driving floral diversity and plant fitness.
Regional assessment of Atlantic salmon (Salmo salar) smolt resource use and body size in Eastern Canada
(University of New Brunswick, 2024-12) McCavour, Erin; Sacobie, Charles; Gillis, Carole-Anne
Atlantic salmon (Salmo salar) are ecologically, economically, and culturally significant, particularly for many Indigenous Peoples, providing sustenance and holding spiritual, ceremonial, and relational importance. They play a vital role in linking freshwater and marine ecosystems through nutrient transport and trophic interactions. Populations across Eastern Canada are at risk, with many designated as endangered, threatened, or of special concern. This thesis examines resource use and body size relationships of smolts from multiple Eastern Canadian rivers, using archival samples (2000-2016) and new accidental mortalities (2022-2023) collected collaboratively. Carbon (δ¹³C) and nitrogen (δ¹⁵N) stable isotopes were analyzed to assess resource use prior to migration, across sites, and as a predictor of smolt body size. Tissue analyses revealed individual dietary specialization, with a decrease in δ¹⁵N from long to short-term diets. Resource use was generally consistent across rivers within the same assigned population and was found to have a weak influence on body size.
Remote sensing to measure the physiology and foraging ecology of North Atlantic right whales in the Gulf of St. Lawrence, Canada
(University of New Brunswick, 2024-12) Lonati, Gina Lynn; Davies, Kimberley
Despite modern-day conservation efforts, many populations of baleen whales have not fully recovered since exploitation by commercial whaling. A better understanding of the physiology and foraging ecology of baleen whales would improve population monitoring, and the development of remote sensing technology offers non-invasive tools for collecting pertinent datasets on wide-ranging whales and their prey. My thesis used remote sensing to measure the physiology and foraging ecology of critically endangered North Atlantic right whales (Eubalaena glacialis, NARWs) in the southwestern Gulf of St. Lawrence (swGSL), Canada, where occupancy by NARWs increased around 2011 following an ocean regime shift. First, I developed, calibrated, and applied a method using drone-based infrared thermography to assess the internal body temperatures of NARWs. With this method, I established the first baselines of intranasal temperature for free-swimming baleen whales: 26.9 ± 1.7ºC in NARWs (n = 21). Second, I evaluated several methods for conducting drone-based photogrammetry with suboptimal photographs of NARWs. This helped me produce the first analysis of NARW body condition in the swGSL (summertime), which was significantly greater than in Cape Cod Bay (springtime) (p < 0.001). Across habitats, standardized widths of adult males (0.166 ± 0.012) and calves (0.170 ± 0.010) were significantly greater than those of lactating females (0.139 ± 0.001) (p < 0.024). Meanwhile, adult females in the Bay of Fundy two decades ago had considerably higher standardized widths (0.18 ± 0.02). Third, I provided context for this variation in body condition by describing prey field conditions associated with groups of foraging NARWs in the swGSL. Conditions were diverse, explained by the diel behaviors, life histories, and relative concentrations of three copepod prey species, including Calanus hyperboreus, which is less abundant in more southerly foraging habitats. Maximum prey concentrations occurred anywhere from 18 m deep to just above the seafloor, implying that NARWs likely alter their dive behavior to target different prey layers in the swGSL depending on the time of day and year. Overall, my thesis provides information for assessing change to the NARW population over time, which is essential for forecast modelling and effective management of this critically endangered species.
Dynamic volt-watt control strategy to improve fairness while mitigating overvoltage in distribution system due to high penetration of PV
(University of New Brunswick, 2024-12) Ahmed, Shafait; Diduch, Chris; Cardenas Barrera, Julian
The risk of overvoltage problems due to high penetration of distributed generation is a growing issue in low-voltage distribution networks. The use of Smart Inverters (SI) in the distribution system can help regulate voltage by controlling active and reactive power generation through volt-watt and volt-var droop control strategies. Conventional volt-watt and volt-var control methods use static parameters, which can lead to unnecessary curtailment of photovoltaics (PV) power, lower power factor, and/or reduction in PV hosting capacity. We propose two different algorithms that dynamically shape the volt-watt curve based on the voltage sensitivity of the PV nodes. Unlike centralized approaches, we adopt a distributed control strategy that minimizes reliance on extensive communication infrastructure, thereby improving system resilience. The proposed methods are simple to implement and require minimal communication among system components, enabling effective local control without the complexity of centralized coordination. To assess the performance of the proposed algorithms, we used the IEEE 37-bus system as a test network. Simulation results confirm the effectiveness of these strategies in enhancing fairness in PV curtailment and reducing overall curtailment levels. The proposed methods were implemented and evaluated through a co-simulation platform integrating the OpenDSS power simulator and Python, demonstrating their practical applicability and robustness in a simulated distribution system environment.
Modeling and aggregated control of residential electric thermal storage units
(University of New Brunswick, 2024-12) Yan, Hao; Diduch, Chris; Kaye, Mary Elizabeth
Electric thermal storage (ETS) plays an important role in the growth of thermal energy storage market. A large number of ETS devices can accomplish tasks of demand-side management (DSM) such as peak shaving to decrease the demand of electricity during peak hours. This thesis proposes four contributions to enable power system operators to issue an appropriate dispatch instruction to an ETS aggregator to accomplish DSM tasks over the control time horizon in the future. Firstly, a method to determine the forecasted lower bound of brick temperature of an ETS physical system (ETSPS) is derived when the zone temperature is regulated over a control time horizon. Secondly, a slope-based simplified model is derived to determine the forecasted brick temperature and help determine the forecasted reserve capacity of an ETSPS during the aggregated control over a control time horizon in the future. Thirdly, forecasted bounds of aggregated power shifted upward and downward from a forecasted baseline are formulated to allow system operators to select achievable dispatch instructions for tracking over a control time horizon in the future. Fourthly, the maximum time duration of tracking any achievable dispatch instruction in the future is forecasted with the application of the sloped-based simplified model. The system operators can issue an appropriate dispatch instruction at the current time for execution over a future control time horizon.

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