THE IMPACT OF UNDERGRADUATE MATHEMATICS COURSES ON COLLEGE STUDENT’S GEOMETRIC REASONING STAGES

The purpose of this study is to investigate possible effects of different college level mathematics courses on college students’ van Hiele levels of geometric understanding. Particularly, since logical reasoning is an important aspect of geometric understanding, it would be interesting to see whether there are differences in van Hiele levels of students who have taken non-geometry courses that emphasize or focus on logic and proofs (Category I) and those that don’t (Category II). We compared geometric reasoning stages of students from the two categories. One hundred and forty nine college students taking various courses from the two categories have been involved in this study. The Van Hiele Geometry Test designed to find out students’ van Hiele levels was used to collect data. After the collection and analysis of the quantitative data, the participants’ van Hiele levels are reported and the reasoning stages of two groups are compared. The results show that students taking logic/proof based courses attain higher reasoning stages than students taking other college level mathematics courses, such as calculus. The results may have implications that are of particular interest to teacher education programs. Finally, the results also confirm a previous assertion about correlation between van Hiele levels and proof writing

EV specific time-of-use rates analysis for workplace charging.

EV specific time-of-use rate plans have been recently introduced by several utilities to overcome the demand charge issue that is the main barrier impeding EV growth in the commercial and industrial sector. This study analyses two EV specific TOU rates in place from a customer and the grid perspectives. The analysis relies on a developed optimal cost model with coordinated charging strategies that minimizes the total cost of a workplace charging station over its lifetime. From a customer perspective, it is shown that the cost benefits are not always achievable and depends on the rates provided. From the grid perspective, the peak demand is found to be increased. Thus, the EV specific rates may not always provide an efficient use of the grid assets

EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition.

As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence-based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R2 value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition

A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system.

This study focuses on the potential role of plug-in electric vehicles (PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid (V2G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2G process is first formulated as an optimal control problem. Then, a two-stage V2G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm

A multi-objective optimization model for EVSE deployment at workplaces with smart charging strategies and scheduling policies.

This study proposes a multi-objective optimization model to determine the optimal charging infrastructure for a transition to plug-in electric vehicles (PEVs) at workplaces. The developed model considers all cost aspects of a workplace charging station, i.e. daily levelized electric vehicle supply equipment (EVSE) infrastructure cost, PEV energy and demand charges. These single-objective functions are aggregated in a multi-objective optimization framework to find the Pareto optimal solutions. Smart charging strategies with interrupted and uninterrupted power profiles are proposed to maximize the use of EVSE units. The charging behavior model is developed based on collected workplace charging data. The model is tested with various scheduling policies to investigate their impact on the behaviors of EVSE types from different perspectives. Finally, a sensitivity analysis is performed to assess the impacts of battery sizes and onboard charger ratings on cost behavior. It is shown that the proposed model can achieve up to 7.8% and 14.6% cost savings as compared to single-objective optimal models and the current charging practice, respectively. The unit cost is found to be more sensitive to scheduling policies than the charging strategies. It is also found that the flexibility ratio policy gives the best PEV scheduling with the lowest unit cost and the most efficient use of the grid assets

Adaptive V2G peak shaving and smart charging control for grid integration of PEVs.

The stochastic nature of plug-in electric vehicle (PEV) driving behavior and distribution grid load profile make it challenging to control vehicle-grid integration in a mutually beneficial way. This article proposes a new adaptive control strategy that manages PEV charging/discharging for peak shaving and load leveling in a distribution grid. For accurate and high fidelity transportation mobility modeling, real vehicle driving test data are collected from the field. Considering the estimated total required PEV battery charging energy, the vehicle-to-grid capabilities of PEVs, and the forecasted non-PEV base load, a reference operating point for the grid is estimated. This reference operating point is updated once at the end of peak hours to guarantee a full final state-of-charge to each PEV. Proposed method provides cost-efficient operation for the utility grid, utmost user convenience free from range anxiety, and ease of implementation at the charging station nodes. It is tested on a real residential transformer, which serves approximately one thousand customers, under various PEV penetration levels and charging scenarios. Performance is assessed in terms of mean-square-error and peak shaving index. Results are compared with those of various reference operating point choices and shown to be superior

Designing laboratory experiments for electricity grid integration of renewable energy using microgrid, test-rig emulators and real time simulation tools.

This paper describes efforts to integrate advanced approaches in microgrid, test-rig emulators and real time simulation into early postgraduate and undergraduate engineering education. It describes two experiments designed for groups of early stage researchers and postgraduate students in the field of Offshore Renewable Energy (ORE). These electrical laboratory experiments are part of a H2020-funded weeklong training course and focus on a medium speed rotary emulator for wave energy applications, and a wind turbine emulator that demonstrated the operation of a Doubly-Fed Induction Generator (DFIG) in a two-machine coupled rig. This paper also discusses some initial reviews of the training course. These reviews noted that students had a desire for more hands-on experimental work, and that the requirements to cover electrical safety material limited the amount of effective time available for experimental work. Finally, the paper outlines some approaches for improving the design of future laboratory experiments in this area

Performance and energy modelling for a low energy acoustic network for the underwater Internet of Things.

As the Internet of Things (IoT) continues to find new applications, there is academic and industrial interest in expanding these concepts to the oceanic environment where data is traditionally challenging to communicate wirelessly, establishing an Underwater Internet of Things. One of the main challenges is rendering the network energy efficient to avoid regular retrievals for battery recharging processes, which can be expensive. The work proposes using low powered networks with multiple hops to reduce cost by avoiding the need for large transmitters, transformers and high rated components, thus rendering the technology cheaper and more accessible. The investigation found that even at low transmission powers a robust underwater acoustic network can be developed over hundreds of meters distance between hops, capable of carrying small packets of sensor data commonly used in Internet of Things applications. The successful delivery ratio and the signal-to-noise ratio metrics are used to assess the robustness of the network as a function of power. The analysis demonstrated that lower power levels exhibit higher energy efficiency when compared to their counterparts employing higher powers, aligning with the trends observed in commercial products, consuming significantly less energy than current single hop networks potentially allowing for longer life Underwater Wireless Sensor Networks

Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland.

Offshore wind energy takes up an important place in Ireland’s renewable generation portfolio thanks to its abundant offshore wind resource. Optimal offshore site selection and developing site-specific energy policy instruments are of key importance to the success of offshore wind energy investments. In this respect, this study aims at developing a multi-criteria decision-making (MCDM) model considering technical, economic, environmental and social criteria to assess Ireland’s most promising offshore wind sites in terms of their sustainable development. An interval type-2 fuzzy sets based MCDM model is developed that integrates the score function with positive and negative solutions to achieve better results. Moreover, advanced energy economic metrics such as levelized cost of electricity with higher resolution are integrated into the decision-making process to make more precise decisions. Case studies are conducted for the five of the offshore sites in development pipeline. Results are compared to those of other state-of-the-art MCDM methods. It is found that Arklow Bank-2 is the most favorable site while Sceirde is the least site. The ranking of other sites is found to be Oriel>Dublin Array>Codling Park. It is shown that the proposed approach is superior in terms of stability and implementation as compared to its counterparts

A hybrid power heronian function-based multi-criteria decision-making model for workplace charging scheduling algorithms.

This study proposes a new multi-criteria decision-making model to determine the best smart charging scheduling that meets electric vehicle (EV) user considerations at work-places. An optimal charging station model is incorporated into the decision-making for a quantitative evaluation. The proposed model is based on a hybrid Power Heronian functions in which the linear normalization method is improved by applying the inverse sorting algorithm for rational and objective decision-making. This enables EV users to specify and evaluate multi-criteria for considering their aspects at workplaces. Five different charging scheduling algorithms with AC dual port L2 and DC fast charging electric vehicle supply equipment (EVSE) are investigated. Based on EV users from the field, the required charging time, EVSE occupancy, the number of EVSE units, and user flexibility are found to have the highest importance degree, while charging cost has the lowest importance degree. The experimental results show that, in terms of meeting EV users' considerations at workplaces, scheduling EVs based on their charging energy needs performs better as compared to scheduling them by their arrival and departure times. While the scheduling alternatives display similar ranking behavior for both EVSE types, the best alternative may differ for the EVSE type. To validate the proposed model, a comparison against three traditional models is performed. It is demonstrated that the proposed model yields the same ranking order as the alternative approaches. Sensitivity analysis validates the best and worst scheduling alternatives