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Integration of electric vehicles into power systems
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electronic Systems Engineering, University of Regina. xxv, 243 p.The power system is continuously evolving and facing various challenges, such as increasing demand, and large-scale electric vehicle (EV) penetration. These challenges have a negative impact on the reliability and sustainability of the power system. For instance, EV charging represents an intensive electric load. Their penetration into the power system poses significant challenges to the operation and control of the power distribution system. Therefore, grid operators need to prepare for high-level EV penetration into the power system. On the other hand, EVs can be used as mobile energy storage systems for frequency regulation, which refers to vehicle-to-grid (V2G) applications.
Since power generation must be controlled to continuously meet demand, and any imbalance between supply and demand will cause voltage and frequency deviation, short-term load forecasting becomes increasingly important. In this study, we developed a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The proposed approach decreases MAPE by 20% compared to the traditional neural network methods.
To tackle the frequency deviation issue, this study proposes centralized and distributed optimization models for V2G applications to provide frequency regulation to power systems. The centralized model has limitations which are addressed by developing a distributed model. The distributed model is solved iteratively with the alternating direction method of multipliers (ADMM). Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards.
A data-driven and parameterized EV charging model is proposed to evaluate the impact of EV penetration on urban residential power distribution. Characteristics of EV charging are analyzed using actual profiles in Saskatchewan, Canada and a location-based algorithm identifies residential EV charging data. Model parameters are modeled by using statistical methods and aggregated using the Monte Carlo method. The results show that the proposed models are valid, accurate, and robust.
The impact of EV penetration on power distribution systems is evaluated by integrating EV charging profiles and base demand into a load flow model based on transformer loading and voltage drop at customers' houses. Simulation results show that the 15-house distribution system can incorporate up to 22 EVs during on-peak demand days, while the 22-house system cannot handle more than 11 EVs. The observed trend can be attributed to the rise in on-peak demand as the number of houses in the distribution system increases, thereby necessitating a reduction in the critical number of EVs.
The study proposes an optimal EV charging model that is considered as an elastic demand under the concept of demand response. The model schedules and controls EV charging to minimize peak demand and shift load off from the peak demand period. Results demonstrate the effectiveness of the model in reducing peak demand and deferring infrastructure investment.Studentye
Synthesis of ferrocenyl and 1-methylpyrrolyl bisphophines via electrophilic addition and substitution reactions of tungsten-coordinated phosphenium ions and phosphine triflates
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Chemistry, University of Regina. xviii, 160 p.New symmetrical and unsymmetrical bisphosphine complexes were synthesized using electrophilic substitution reactions of tungsten pentcarbonyl-coordinated phosphenium ions [W(CO)5(PRRʹ)]+, phosphine triflates [W(CO)5{PRRʹ(OSO2CF3)}], [W(CO)5{PRCl(OSO2CF3)}] and protonated amino phosphine [W(CO)5{PCl2NH(Et)2}]+ (38) with N-methylpyrrole and ferrocene. Chloride abstractions from [W(CO)5{PPh2Cl}] (1) and [W(CO)5{PPhCl2}] (2) were carried out using aluminum chloride or silver trifluormethanesulfonate (AgOTf) to form [W(CO)5(PPh2)]+ (4), [W(CO)5{PPh2(OSO2CF3)}] (5), and [W(CO)5{PPhCl(OSO2CF3)}] (6). Trifilic acid (HOTf) was added to [W(CO)5{P(NEt)2Cl2}] (3) to form compound 38.
Reactions of 4 or 5 with N-methylpyrrole led to regioisomers [W(CO)5{PPh2(2-C5H6N)}] (7) and [W(CO)5{PPh2(3-C5H6N)}] (8). Compound 6 reacted with N-methylpyrrole to form regioisomers [W(CO)5{PPhCl(2-C5H6N)}] (9) and [W(CO)5{PPhCl(3-C5H6N)}] (10). Electrophilic substitution of 4, 5, or 6 with the monosubstituted phosphine complexes 8 and 10 led to installation of another phosphino group onto the pyrrole ring to form 2,4-bis(phosphino)N-methylpyrrole complexes [(CO)10W2{μ-2,4-C5H5N)(PPh2)2}] (11), [W2(CO)10{μ-2,4-C5H5N(PPhCl)(PPh2)}] (12), and [W2(CO)10{μ-2,4-(PClPh)2C5H5N}] (26). Chloride abstractions from 12 and 26 using AgOTf led to [W2(CO)10{μ-C5H5N(PPh2)(PPhOSO2CF3)}] (13) and [W2(CO)10{μ-C5H5N(P(Ph)OTf)2}] (27). Electrophilic substitutions of 13 and 27 with H-R led to bisphosphines [W2(CO)10{μ-2-(PPh2)-4-(PPhR)C5H5N}] and [W2(CO)10{μ-C5H5N(PPhR)2}] (R = allyl, phenylalkynyl). Compound 26 was converted to the monotriflate complex [W2(CO)10{μ-2-(PPh(OSO2CF3)-4-(PClPh)C5H5N}] (28) using one equivalent of AgOTf. Addition of H-R to 28 led to monosubstituted products [W2(CO)10{μ-2-(PPhR)-4-(PClPh)C5H5N}] (R = allyl or phenylalkynyl). Chloride abstraction from monosubstituted bisphosphines with AgOTf led to [W2(CO)10{μ-2-(PPhR)-4-(PPh(OSO2CF3))C5H5N}] (R = allyl or phenylalkynyl). Addition of H-R led to [W2(CO)10{μ-2- (PPhR)-4-(PPhRʹ)C5H5N}] (R = allyl; Rʹ = phenylalkynyl or R = phenylalkynyl; Rʹ = allyl).
Electrophilic addition reactions were used to install two diphenylphosphino groups onto ferrocene. Addition of 4 or 5 to [W(CO)5(PPh2(C10H9Fe)] (39) led to the known complex [(CO)10W2{μ-C10H8Fe(PPh2)2}] (40). Alternately, 2 equivalents of compound 5 or 4 were added to ferrocene to form 40. Electrophilic addition of 6 to 39 led to the unsymmetrical bisphosphine complex [W2(OC)10{μ-C10H8Fe(PPhCl)(PPh2)}] (41). Addition of 2 equivalents of 6 to ferrocene led to [W2(CO)10{μ-C10H8Fe(PPhCl)2}] (46). Chloride abstraction from 41 and 46 using silver triflate led to [W2(OC)10{μ-C10H8Fe(PPh(OSO2CF3))(PPh2)}] (42) and [W2(CO)10{μ-C10H8Fe(PPh(OSO2CF3))2}] (47). Reactions of 42 and 47 with nucleophiles H-R led to mono and disubstituted bisphosphine complexes [W2(OC)10{μ-C10H8Fe(PPhR)(PPh2)}] and [W2(OC)10{μ-C10H8Fe(PPh(R))2}] (R = allyl, phenylalkynyl, ferrocenyl). Sequential addition of two different groups to phosphorus was done by abstracting chloride from 46 using AgOTf to form [W2(CO)10{μ-C10H8Fe(PPhCl)(PPh(OSO2CF3f)}] (48). Reaction of 48 with H-R led to monosubstituted bisphosphine complex [W2(CO)10{μ-C10H8Fe(PPhCl)(PPh(R)}] (R = allyl, phenylalkynyl, ferrocenyl). Chloride abstraction from monosubstituted bisphosphines using AgOTf formed triflate complexes [W2(CO)10{μ-C10H8Fe(PPh(OSO2CF3))(PPh(R)}] (R = allyl, phenylalkynyl, ferrocenyl). Addition of H-R led to [W2(CO)10{μ-C10H8Fe(PPh(Rʹ))(PPh(R)}] (R = allyl; Rʹ = phenylalkynyl). Addition of 1/2 an equivalent of ferrocene to 38 led to [W2(CO)10{μ-C10H8Fe(PCl2)2}] (55). Compound 55 was converted into [W2(CO)10{μ-C10H8Fe(P(OTf)2(PCl(OTf))}] (56) by addition of AgOTf. Addition of H-R led to tetra-substituted products [W2(CO)10{μ-C10H8Fe(P(R)2)2}] (R = allyl, phenylakynyl, and indolyl).Studentye
Co-Creating Socio-Culturally-Appropriate Virtual Geriatric Care for Older Adults Living With HIV: A Community-Based Participatory, Intersectional Protocol
No full textThe aging cohort of persons living with human immunodeficiency virus (HIV) in Canada has reached a critical point, with nearly half now 50 years age or older. Older persons living with HIV have specific needs which can be effectively addressed by geriatric specialists. However, the recognition of HIV care as a domain of geriatrics is recent, resulting in a lack of clinical recommendations and modern care models for delivering geriatric care to this population. Virtual care has been demonstrated to reduce existing barriers to accessing HIV care in some populations but before it can be adapted to geriatric HIV care a critical first step is to acknowledge and understand disparities in socioeconomic circumstances, technology access and ability and cultural differences in experiences. This protocol marks the initial step in a comprehensive program of research aimed at co-designing, implementing, and evaluating culturally-appropriate virtual geriatric care for diverse older adults living with HIV. The study employs qualitative methods with older adults living with HIV to lay the groundwork, to inform the future development of a virtual model of geriatric care. We will explore the perspectives of diverse groups of older persons with HIV on (1) The value and necessity of culturally-tailored virtual interventions for geriatric HIV care; and (2) Recommendations on how best to engage older persons with HIV in the future co-design of a virtual model of geriatric HIV care. Ultimately, a more culturally-appropriate approach to care will foster a more inclusive and supportive healthcare system for all individuals affected by HIV including those who are aging. Researchers can utilize this research protocol to employ qualitative co-design and participatory methods with diverse older adults living with HIV.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the AGE-WELL Network of Centres of Excellent (NCE) (AW-CAT-2023-03) Inc and the Canadian Frailty Network’s (CFN) Catalyst Funding Program in Healthy Aging. The AGE-WELL NCE and CFN are funded by the Government of Canada through the Networks of Centres of Excellence program
Design and validation of a data-driven infrastructure decision and planning framework for improving climate resiliency of small municipalities and rural communities
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Environmental Systems Engineering, University of Regina. xi, 89 p.The Intergovernmental Panel for Climate Change calls for local governments to be the drivers toward resilient communities concerning climate change. Resilient municipalities must make decisions incorporating climate change to reinvest in an aging infrastructure network. The challenge is that small municipalities need access to information or the capacity to make informed decisions. Expenses related to specialized engineering for asset management and prioritization are a high cost for the municipality, where the municipality may see the value in prioritizing the funds toward operations and maintenance practices.
In response to this challenge, a framework is created and presented to summarize this concept and tested in a case study using a small municipality in Saskatchewan. The framework includes the multi-criteria decision analysis, national guidelines for infrastructure design and engineering in the context of changing climate conditions, a political lens, and a case study method to calibrate and validate the data-driven decision and planning model. The case study location was chosen for logistical and geographical purposes, being close to Regina, SK, and involving town leadership interested in asset inventory, management, and planning exercises. Furthermore, relatively good meteorological data is available to connect to the framework and software platform developed through this project.
In generalizing infrastructure into groups, understanding the effects of climate conditions, and requesting preferences of leaders and subject matter experts, an accessible application is available for municipalities to rank the sensitivity of their infrastructure to determine the starting point of building a resilient municipality. The framework demonstrates high-quality outputs and guidance tied directly to timely and appropriately scaled climate data, infrastructure information, and local conditions and political choices through the calibration and validation processes. The selected multi-criteria decision-analysis method for the framework is the PROMETHEE model. The model enables the use of qualitative and quantitative values that can address the chosen inputs.
The framework is appropriate for smaller municipalities that may not have the capacity or financial resources to invest in larger-scale analyses and may also, in fact, be a valuable resource for larger municipalities and urban centers in the decision and planning for resilient infrastructure design under a changing climate.Studentye
Key essential oil components delocalize Candida albicans Kar3p and impact microtubule structure
No full textThis is a final peer-reviewed accepted manuscript. The final published version is available
online at https://doi.org/10.1016/j.micres.2023.127373
© 2023 Elsevier GmbH. This manuscript version is made available under the Creative Commons
Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) 4.0 International License
(http://creativecommons.org/licenses/by-nc-nd/4.0/)Treatment of Candida albicans associated infections is often ineffective in the light of resistance, with an urgent need to discover novel antimicrobials. Fungicides require high specificity and can contribute to antifungal resistance, so inhibition of fungal virulence factors is a good strategy for developing new antifungals.Natural Science and Engineering Research Council Discovery Grant (06649-2018) and Canada Foundation for Innovation grants to TESD. RMY was funded by the UGC-Basic Science Research Startup Grant, and the Government of India University Grants Commission (F.30-561/2021(BSR)) and National Agricultural Science Fund-Indian Council of Agricultural Research (F. No. NASF/SUTRA-02/2022–23/50).Facultyye
Classification of soil surface texture using high-resolution RGB images captured under uncontrolled field conditions
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Electronic Systems Engineering, University of Regina. xiv, 116 p.Understanding the properties of soil and its impact on the environment and farming
practices requires accurately classifying its texture. Accurate soil texture classification can
optimize soil nutrient levels and improve land management. This study proposes a framework
that uses images captured under Uncontrolled Field Conditions (UFC) to classify
soil texture for farmlands accurately. UFC images are captured in varying ambient light
and environmental conditions, which can introduce unwanted elements such as shadows
and varying lighting. Our framework uses image-processing techniques, texture-enhancing
methods, and deep learning to process and classify these soils accurately. First, we process
the soil using semantic segmentation to eliminate all non-soil pixels. We compare Segmentation
Network (SegNet), U-shaped Neural Network (UNet), Pyramid Scene Parsing
Network (PSPNet), and DeepLab v3+ models to choose the best for semantic segmentation.
The trained segmentation model produces masks used to eliminate non-soil pixels from
the images. This process produces new images with random 0 pixel clusters that would
negatively disrupt texture information, and so next, we split the new images to eliminate all
0 pixel clusters while preserving only soil pixels. We then perform texture enhancement
on the images before feeding them into the classification network. We design and use an
improved network called EfficientCNN for classification to use a reduced number of parameters
while producing maximum accuracy. We also compare this model with Residual
Network (ResNet 50), EfficientNetB7 and Inception v3. EfficientCNN architecture uses
just 5.9 million parameters and produces an accuracy of 84.783%, while Inception v3 uses
21.7 million parameters and produces an accuracy of 85.621%. EfficientCNN produces
only 0.838% less accuracy than Inception v3. Our results contribute to agriculture and soil
science studies.Studentye
A decision-making system for medical transportation mode using machine learning methods
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. x, 124 p.There is number of complicated operations in freight transportation system to cover customer demands in the world. Nowadays, companies have huge competition to fulfill customer needs and get a higher level of performance in freight transportation. Transportation mode has been considered as one of the components that has influence on service levels of freight transportation. Road, sea, air are popular modes of transportation which have different features and unique benefits. They also have various costs, different emissions in environment and risks in society. People use these transportation modes based on their needs, but they do have some advantages and disadvantages.
When we are dealing with a lot of shipment transactions in a company, making decision for choosing the best option will not be easy. Companies face with challenges since there are numerous factors effecting shipment mode selection. Moreover, the number of low-volume and high-frequency shipments has also increased due to increased demand diversity, shorter product life cycles, and increased agile customer response. Consequently, logistics costs are increasing for those shippers who need to export a small number of products abroad. As a result, researchers have been actively focusing on this matter, which has a significant impact on a country's social and economic situation.
This research aims to develop a hybrid approach to create a shipment selection model with a case study of pharmaceutical drugs by machine learning algorithms, checking the accuracy of predictions and using multi-criteria decision-making methods (MCDM) for validation of our work.
Several different features of the dataset including shipping cost, country of origin and destination, cargo weight, cargo dimensions, etc., are given to decision tree, Random forest, logistic regression, XGboost and SVM machine learning algorithms so that we can predict the best shipping method by land, air, or sea. Then, using different criteria F1 score, Recall and precision, accuracy score we measured the accuracy of the
forecast and finally, we validated the research method by MCDM methods SAW, MARCOS, TOPSIS, MULTIMOORA and VIKOR.
After being familiar with all important factors, tools, research gap in literature review, we realized that choosing one machine algorithm is not enough to get an accurate result and we used the most popular ones. data science scored important features influencing transportation modes and used machine learning techniques to learn the factors and the relationships between them to increase the accuracy of the pharmaceuticals drugs shipment selection system. By MCDM we found XGboost as the best machine learning algorithm to predict the shipment mode with the average performance evaluation of 84 percentage then random forest, decision tree, SVM and LR respectively.Studentye
Inclusive internal governance policies to support women’s representation: A case study of three mid-sized municipalities in Canada
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Public Policy, University of Regina. ix, 112 p.This thesis explores the recent phenomenon of inclusive internal governance policies (IIGPs) for municipal councils. Prior research urges the adoptions of IIGPs as they may help mitigate one of the biggest barriers to women’s representation – the negative political environment on municipal councils (Federation of Canadian Municipalities [FCM], 2018; Seasonova, 2022; Seiferling, 2016). IIGPs are needed to act as safeguards enabling a safer, more respectful and inclusive council workspace environment. Municipalities only recently started to adopt IIGPs, therefore, there has not been much research about these policies.
The objective of this research is to expand the knowledge of IIGPs and to analyze the patterns of how IIGPs are developed. Three mid-sized cities in different parts of Canada were chosen as case studies – Yellowknife, NWT; Kamloops, BC; and Moncton, NB. There were two stages of data collection: a documentary review of research literature, policy documents, and newspapers; and 10 semi-structured interviews with former and current mayors, councillors, city staffers, and committee members. The collected data was analyzed using the inductive outcome explaining process-tracing methodology and multiple streams theory.
This research collected detailed information about different types of IIGPs, discovering several new IIGPs (e.g., online meetings, gender-neutral language use, and online parental and caregiver leave). The IIGPs were summarized into a four-category system. By studying the journey of each case, from the first to the latest IIGP developed, I traced the process that led to the creation of these IIGPs. The biggest drivers of IIGP creation were the turnover of people with different lived experiences (younger people, women, and people knowledgeable about social issues), COVID-19, public scandals, and provincial or territorial government mandates.
Through data collection, I amassed important new knowledge about the little-studied area of IIGPs for council. The research results help expand the literature on methods to increase women’s political representation. The collected data will be used by municipal organizations like the Federation of Canadian Municipalities (FCM) to create resources for municipalities on the best practices for the development of IIGPs.Studentye
Carbon Pricing Costs for Households and the Progressivity of Revenue Recycling Options in Canada
No full textCanadian federal policy mandates a floor price on greenhouse gas emissions in all provinces and territories or
an equivalent quantity instrument. Provinces that implement a system consistent with the federal benchmark
maintain control of revenues. Provinces that do not implement a carbon price are subject to a federally
administered pricing system, with revenue recycling via lump-sum household rebates. Using rich synthetic household microdata, we quantify the direct and indirect tax burden on households and carbon pricing revenues
in each province. We also calculate carbon pricing revenue available to each province. Using these data, we
measure the net cost to households and the overall progressivity of carbon pricing under four revenue recycling
scenarios: (a) a means-tested sales tax credit increase, (b) a lump-sum dividend, (c) a sales tax rate reduction,
and (d) a personal income tax basic exemption increase. We find that the carbon tax is generally progressive
even without revenue recycling, the sales tax credit and lump-sum rebate are progressive, the sales tax rate
reduction is mostly regressive, and the income tax change is regressive. We also show that Canada’s output based pricing system for large emitters helps to mitigate indirect carbon pricing costs with a notable effect in
reducing household costs.We gratefully acknowledge that this project has been
supported in part through the Smart Prosperity Institute
Research Network and its Greening Growth Partnership,
which is supported by a Social Sciences and Humanities
Research Council of Canada Partnership Grant (No. 895-
2017-1018), as well as the Economics and Environmental
Policy Research Network. We acknowledge financial
support for open access funding from the University of
Regina President’s Publication Fund and the University
of Regina Faculty of Arts Publication Fund