A scheduling model for the charging of electric vehicles in photovoltaic powered smart microgrids

Electric vehicles (EVs) have emerged as a viable option to advance sustainable mobility, but adoption is still relatively low. This has been largely due to the limited range one can travel on a single charge, leading to range anxiety, longer charge cycles and long wait times at charging stations. One solution to range anxiety is to erect charging stations on major roads and urban centres. There is also a lack of real-time information regarding the state of charging stations and charging ports in existing charging infrastructure. To increase the benefit of using EVs, using renewable energy sources, such as photovoltaics (PV) to power EVs, can further increase the benefit of reduced carbon footprint. The main research objective was to design a Charge Scheduling Model for charging EVs using a PV-powered smart microgrid (SMG). The model addresses the lack of an integrated platform where EV drivers can schedule when and where to charge their EVs. The model also reduces the negative effects of the adoption of EVs, including range anxiety. The Charge Scheduling Model was developed using the Design Science Research (DSR) methodology and was the main artefact of the study. A literature study was conducted of research related to SMGs, renewable energy, EVs and scheduling, to identify shortcomings that currently exist in EV charge scheduling (EVCS), and to identify the requirements of a potential solution. The literature study also identified the hard and soft constraints that are unique to EVCS, and the available energy in the SMG was identified as one of the hard constraints. Therefore, an Energy Forecasting Model for forecasting energy generated in PV-powered SMGs was required before the Charge Scheduling Model could be designed. During the first iteration of the design and development activities of DSR, four models were designed and implemented to evaluate their effectiveness in forecasting the energy generated in PV-powered SMGs. The models were Support Vector Regression (SVR), K-Nearest Neighbour (KNN), Decision Trees, and Multilayer Perceptron. In the second iteration, the Charge Scheduling Model was designed, consisting of a Four Layered Architecture and the Three-Phase Data Flow Process. The Charge Scheduling Model was then used to design the EVCS prototype. The implementation of the EVCS prototype followed the incremental prototyping approach, which was used to verify the effectiveness of the model. An artificial-summative evaluation was used to evaluate the design of the Charge Scheduling Model, whereas iterative formative evaluations were conducted during the development of the EVCS prototype. The theoretical contribution of this study is the Charge Scheduling Model, and the EVCS prototype is the practical contribution. The results from both evaluations, i.e. the Energy Forecasting Model and the Charge Scheduling Model, also make a contribution to the body of knowledge of EVs

A scheduling model for the charging of electric vehicles in photovoltaic powered smart microgrids

Electric vehicles (EVs) have emerged as a viable option to advance sustainable mobility, but adoption is still relatively low. This has been largely due to the limited range one can travel on a single charge, leading to range anxiety, longer charge cycles and long wait times at charging stations. One solution to range anxiety is to erect charging stations on major roads and urban centres. There is also a lack of real-time information regarding the state of charging stations and charging ports in existing charging infrastructure. To increase the benefit of using EVs, using renewable energy sources, such as photovoltaics (PV) to power EVs, can further increase the benefit of reduced carbon footprint. The main research objective was to design a Charge Scheduling Model for charging EVs using a PV-powered smart microgrid (SMG). The model addresses the lack of an integrated platform where EV drivers can schedule when and where to charge their EVs. The model also reduces the negative effects of the adoption of EVs, including range anxiety. The Charge Scheduling Model was developed using the Design Science Research (DSR) methodology and was the main artefact of the study. A literature study was conducted of research related to SMGs, renewable energy, EVs and scheduling, to identify shortcomings that currently exist in EV charge scheduling (EVCS), and to identify the requirements of a potential solution. The literature study also identified the hard and soft constraints that are unique to EVCS, and the available energy in the SMG was identified as one of the hard constraints. Therefore, an Energy Forecasting Model for forecasting energy generated in PV-powered SMGs was required before the Charge Scheduling Model could be designed. During the first iteration of the design and development activities of DSR, four models were designed and implemented to evaluate their effectiveness in forecasting the energy generated in PV-powered SMGs. The models were Support Vector Regression (SVR), K-Nearest Neighbour (KNN), Decision Trees, and Multilayer Perceptron. In the second iteration, the Charge Scheduling Model was designed, consisting of a Four Layered Architecture and the Three-Phase Data Flow Process. The Charge Scheduling Model was then used to design the EVCS prototype. The implementation of the EVCS prototype followed the incremental prototyping approach, which was used to verify the effectiveness of the model. An artificial-summative evaluation was used to evaluate the design of the Charge Scheduling Model, whereas iterative formative evaluations were conducted during the development of the EVCS prototype. The theoretical contribution of this study is the Charge Scheduling Model, and the EVCS prototype is the practical contribution. The results from both evaluations, i.e. the Energy Forecasting Model and the Charge Scheduling Model, also make a contribution to the body of knowledge of EVs

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Analyzing Data-center Application Performance Via Constraint-based Models

Hyperscale Data Centers (HDCs) are the largest distributed computing machines ever constructed. They serve as the backbone for many popular applications, such as YouTube, Netflix, Meta, and Airbnb, which involve millions of users and generate billions in revenue. As the networking infrastructure plays a pivotal role in determining the performance of HDC applications, understanding and optimizing their networking performance is critical. This thesis proposes and evaluates a constraint-based approach to characterize the networking performance of HDC applications. Through extensive evaluations conducted in both controlled settings and real-world case studies within a production HDC, I demonstrated the effectiveness of the constraint-based approach in handling the immense volume of performance data in HDCs, achieving tremendous dimension reduction, and providing very useful interpretability.Doctor of Philosoph