Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

© 2016 Elsevier B.V. Participation of plug-in electric vehicles (PEVs) is expected to grow in emerging smart grids. A strategy to overcome potential grid overloading caused by large penetrations of PEVs is to optimize their battery charge-rates to fully explore grid capacity and maximize the customer satisfaction for all PEV owners. This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO). The online algorithm is updated at regular intervals of Δt = 5 min to maximize the customers’ satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOCReq) and willingness to pay the higher charging energy prices. The algorithm also ensures that the distribution transformer is not overloaded while grid losses and node voltage deviations are minimized. Simulation results for uncoordinated PEV charging as well as CAPSO with fixed charge-rate coordination (FCC) and variable charge-rate coordination (VCC) strategies are compared for a 449-node network with different levels of PEV penetrations. The key contributions are optimal VCC of PEVs considering battery modeling, chargers’ efficiencies and customer satisfaction based on requested plug-out times, driving pattern, desired final SOCs and their interest to pay for energy at a higher rate

Application of SVC and single-phase shunt capacitor to improve voltage profiles and reduce losses of unbalanced multiphase smart grid with PEV charging stations

In smart grids, the conventional approach of locating compensation devices based on the forecasted daily load curves is not realistic as the locations, times and durations of some loads such as plug-in electric vehicles (PEVs) and smart appliances are randomly changing during the 24 hour period. This paper proposes a new approach to improve the performance of unbalanced multiphase distribution systems consisting of single-, two- and three-phase networks with PEV charging stations. The approach is designated to perform online VRI ranking, place SVCs and single-phase capacitors at the weakest three-phase and single-phase buses, respectively; and then switch these devices in and out of the service according to the lowest voltage ranking index (VRI) values in order to improve voltage profiles and reduce total system losses. Simulation results are performed and compared for an unbalanced multiphase 13 node test feeder with PEV charging stations using DIgSILENT PowerFactory software

An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system\u27s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques

Standardization of DGA interpretation techniques using fuzzy logic approach

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation. As a result, various DGA interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. DGA interpretation is yet a challenge in the power transformer condition monitoring research area. To alleviate this issue, this paper introduces a fuzzy logic approach to help in standardizing DGA results quantification and classification using various interpretation techniques such as key gas, Rogers ratio, IEC ratio, Doernenburg and Duval triangle methods. In this context, DGA results for 2000 oil samples have been collected from different transformers of different ratings, life span and operating conditions. Traditional DGA interpretation techniques are used to analyze the results which are then compared with the results of the fuzzy logic models. Results show that the fuzzy logic models enhance the consistency among all current interpretation techniques and can eliminate the need for expert personal to interpret DGA results

Robust placement and sizing of charging stations from a novel graph theoretic perspective

This paper proposes analytical approaches to extend the capacity of existing networks of electric vehicles (EVs) by placement of additional charging stations (CSs) as well as determining the sizes of existing and new CSs in order to handle future expansions of EVs. The EV flow at CSs is modeled by a graph where nodes are potential locations for CSs and edges are uncertain parameters representing the variable EV flow at CSs. The required extra CS locations are explored by transforming the CS placement problem into a controllability framework addressed by maximum matching principle (MMP). To find the sizes of each CS, the graph of CS network is partitioned featuring only one CS in each subgraph. The size of CS in each subgraph is then determined by transforming the problem into the problem of robust stability of a system with uncertain parameters where each parameter is associated with an edge of subgraph. The zero exclusion principle is then tested for the related Kharitonov rectangles and polygonal polynomials of closed loop system with selected feedback gain as CS capacity. The proposed analytical approach is tested on the existing Tesla CS Network of Sydney. The locations of extra required CSs as well as the sizes of existing and new CSs are determined to maintain the waiting times at all stations below the threshold level

Charging infrastructure for commercial electric vehicles: Challenges and future works

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed

Natural plant precursor for the facile and eco-friendly synthesis of carbon nanodots with multifunctional aspects

In this research, green, low toxicity and good biocompatibility fluorescent carbon dots (CDs) were prepared under one-step hydrothermal conditions from the essential oil of Thymus vulgaris L. as a carbon source. The morphology and composites of the CDs were characterized using transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). Optical and fluorescence properties of the CDs, such as photostability and the quantum yield as high as 5.2 were measured by UV�Visible and fluorescence spectroscopy. The varying effects of reaction time and temperature were clearly investigated on the yield of products. The fluorescence of CDs could be quenched specifically by Fe 3+ , hence; the prepared CDs were applied to detect Fe 3+ from aqueous solution with a limit of detection of 0.23 mg/L. The effect of cytotoxicity on the cell viability was investigated and low toxicity of these carbon dots was demonstrated. Thus natural-based fluorescent carbon dots as fluorescent biomarkers were applied through in vitro fluorescence imaging without any further purifications. In addition, the antioxidant activity of CDs was carried out with IC 50 value of 23.43 mg/L. © 2019 Elsevier B.V

Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings

Photovoltaic (PV) systems in residential buildings require energy storage to enhance their productivity; however, in present technology, battery storage systems (BSSs) are not the most cost-effective solutions. Comparatively, thermal storage systems (TSSs) can provide opportunities to enhance PV self-consumption while reducing life cycle costs. This paper proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), thermal and electrical energy storage systems. For simultaneous optimal sizing of BSS and TSS, a particle swarm optimization (PSO) algorithm is applied to minimize daily electricity and life cycle costs of the smart building. A model predictive controller is then developed to manage energy flow of storage systems to minimize electricity costs for end-users. The main objective of the controller is to optimally control HP operation and battery charge/discharge actions based on a demand response program. The controller regulates the flow of water in the storage tank to meet designated thermal energy requirements by controlling HP operation. Furthermore, the power flow of battery is controlled to supply all loads during peak-load hours to minimize electricity costs. The results of this paper demonstrate to rooftop PV system owners that investment in combined TSS and BSS can be more profitable as this system can minimize life cycle costs. The proposed methods for optimal sizing and operation of electrical and thermal storage system can reduce the annual electricity cost by more than 80% with over 42% reduction in the life cycle cost. Simulation and experimental results are presented to validate the effectiveness of the proposed framework and controller

Developing a Questionnaire for Iranian Women's Attitude on Medical Ethics in Vaginal Childbirth

Background: Vaginal delivery is one of the challenging issues in medical ethics. It is important to use an appropriate instrument to assess medical ethics attitudes in normal delivery, but the lack of tool for this purpose is clear. Objectives: The aim of this study was to develop and validate a questionnaire for the assessment of women’s attitude on medical ethics application in normal vaginal delivery. Patients and Methods: This methodological study was carried out in Iran in 2013 - 2014. Medical ethics attitude in vaginal delivery questionnaire (MEAVDQ) was developed using the findings of a qualitative data obtained from a grounded theory research conducted on 20 women who had vaginal childbirth, in the first phase. Then, the validation criteria of this tool were tested by content and face validity in the second phase. Exploratory factor analysis was used for construct validity and reliability was also tested by Cronbach’s alpha coefficient in the third phase of this study. SPSS version 13 was used in this study. The sample size for construct validity was 250 females who had normal vaginal childbirth. Results: In the first phase of this study (tool development), by the use of four obtained categories and nine subcategories from grounded theory and literature review, three parts (98-items) of this tool were obtained (A, B and J). Part A explained the first principle of medical ethics, part B pointed to the second and third principles of medical ethics, and part J explained the fourth principle of medical ethics. After evaluating and confirming its face and content validity, 75 items remained in the questionnaire. In construct validity, by the employment of exploratory factor analysis, in parts A, B and J, 3, 7 and 3 factors were formed, respectively; and 62.8%, 64% and 51% of the total variances were explained by the obtained factors in parts A, B and J, respectively. The names of these factors in the three parts were achieved by consideration of the loading factor and medical ethics principles. The subscales of MEAVDQ showed significant reliability. In parts A, B and J, Cronbach’s alpha coefficients were 0.76, 0.72 and 0.68, respectively and for the total questionnaire, it was 0.72. The results of the test–retest were satisfactory for all the items (ICC = 0.60 - 0.95). Conclusions: The present study showed that the 59-item MEAVDQ was a valid and reliable questionnaire for the assessment of women’s attitudes toward medical ethics application in vaginal childbirth. This tool might assist specialists in making a judgment and plan appropriate for women in vaginal delivery management

Multiwall Carbon Nanotubes Modified Carbon Paste Electrode for Determination of Copper(II) by Potentiometric and Impedimetric Methods

Abstract A chemically modified carbon paste electrode with multiwall carbon nanotube (MWCNT) was prepared and used as a sensor for Cu 2+ ion. The unique chemical and physical properties of CNT have paved the way to new and improved sensing devices. A central composite chemometrics design was applied for multivariate optimization of the effects of three significant parameters (Graphite powder (X 1 ), MWCNT (X 2 ) and Ionophre (X 3 )) influencing the response of the electrode. In the optimized conditions, the electrode exhibits a Nernstian slope of 30.1 mV/decade in a linear range between 1.0×10 -6 to1.0×10 -1 M over a wide pH range (2.0-6.5). Importantly, the effect of the MWCNT on the performance of electrode was investigated by impedance technique, that showed the MWCNT helps the transduction of the signal in carbon paste electrode and the charged transfer resistance (R ct ) was reduced. The impedimetric results indicated that the linear concentrations range was 1.0×10 −7 to 1.0×10 −1 M and in comparison with potentiometry, the pH range increased to 2.0−7.5. JNS All rights reserve