Development of a multi criteria model for assisting EV user charging decisions

Electric Vehicles offer one of the most efficient solutions towards the direction of providing sustainable transportation systems. However, a broader market uptake of Electric Vehicle--based mobility is still missing. The lack of sufficient infrastructure (Electric Vehicle charging stations) in combination with the lack of information about their availability appears as a major limitation, leading to low user acceptance. Additional, technology based, assistance services provided to Electric Vehicle users is a key solution to unlock the full potential of their utilization. This paper presents a multi-factor dynamic optimization model using multi-criteria analysis to select the best alternatives for Electric Vehicle charging within a smart grid with the goal of supporting a larger uptake of Electric Vehicle -based mobility. The application provides assistance to the Electric Vehicle drivers through functionalities of energy price, cost and travel time of the electric vehicle to the charging station, the specifications of vehicles and stations, the status of the charging stations as well as the user\u27s preferences. The proposed model is developed by incorporating PROMETHEE II and Analytic Hierarchy Process methodologies to provide the best charging solutions after considering all possible options for each Electric Vehicle user. The multi-criteria analysis algorithm is not only limited to comparing alternative charging options at a specific time but also looks at several starting times of charging. A simulated case study is implemented to examine the functionality of the proposed model. From the results, it is evident that by applying the findings of this work entrepreneurial community and industry can develop new services that will improve user satisfaction, electromobility, urban mobility, and sustainability of cities. At the same time, academia, leveraging the methodology and factors that influence the choice of charging station, can conduct further research on digital innovations that will contribute to the consolidation of e-mobility ensuring the sustainability of cities, while accelerating digital transformation in the transport sector

A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks

With the increasing number of electric vehicles (EVs), the EV fast charging load will significantly affect the voltage quality of electricity distribution networks. On the other hand, EVs have potentials to change the choices of charging locations due to the incentives from the variations of charging prices, which can be considered as a flexible response resource for electricity distribution networks. In this paper, a charging pricing strategy of EV fast charging stations (FCSs) was developed to determine the pricing scheme for the voltage control of electricity distribution networks, which consisted of a simulation model of EV mobility and a double-layer optimization model. Considering the travel characteristics of users, the simulation model of EV mobility was developed to accurately determine the fast charging demand. Taking the total income of FCSs and the users’ response to the pricing scheme into account, the double-layer optimization model was developed to optimize the charging pricing scheme and minimize the total voltage magnitude deviation of distribution networks. A test case was used to verify the proposed strategy. The results show that the spatial distribution of EV fast charging loads was reallocated by the proposed charging pricing scheme. It can also be seen that the proposed strategy can make full use of the response capacity from EVs to improve the voltage profiles without decreasing the income of the FCSs

Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates

This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.</p

Smart navigation system for electric vehicles charging

In the present time, there is still a lack of popularity in the use of electric vehicles, because of the actual disadvantages that they have. For this work presents the process of research and development of a web based application with the main purpose of helping Electric Vehicle owners decide the Charging Station that, by selecting it to go and charge their vehicles, represents the lowest cost in time or money (depending on their priorities) when they need to go to charge their electric vehicles and to give them less time or energy consuming route to follow in order to arrive to the charging station selected. This, to reduce the concern of the users about if the can or not arrive to a charging station. To do this, the application has been developed with several features to help the users. First, the application has the feature of being accessed from multiple type of devices. Second, the application has the feature of detecting the users locations using Global Positioning System. Third, the application has the ability to find the charging stations and their coordinates that are near to the users. Fourth, the application has the ability to formulate the route with the lowest time or energy cost between the users locations and the charging stations. Fifth, after creating all the routes, the application shows the users the parameters of every route and charging station. Sixth, the application has the ability to let the users decide the priority to select the charging station. Seventh, the application let the users decide the battery percentage that they want their vehicles to have after charging them. This application was created using mostly Javascript language, Expressjs as the framework and for the user interface jQuery. Moreover, MongoDB and PosgreSQL were used as databases. Furthermore, some web services like Amazon Web Services were used for server hosting, OpenStreetMap for obtaining GeoSpatial data, Open Charging Map for obtaining charging stations coordinates and data and Fuel Economy for obtaining vehicles data were used to complement the application. For the route formulation, Dijkstra's algorithm and pgRouting was used. Results indicated that the application can successfully recommend routes and charging stations to the users with a reduction of 90\% of time needed against the less time consuming cheapest option when time is the priority and a reduction of 27 times the money needed for the fastest option when price is the priority. Meaning that the navigation system can successfully reduce the time or costs to adjust to the users necessities

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

Research on economic planning and operation of electric vehicle charging stations

Appropriately planning and scheduling strategies can improve the enthusiasm of Electric vehicles (EVs), reduce charging losses, and support the power grid system. Thus, this dissertation studies the planning and operating of the EV charging station. First, an EV charging station planning strategy considering the overall social cost is proposed. Then, to reduce the charging cost and guarantee the charging demand, an optimal charging scheduling method is proposed. Additionally, by considering the uncertainty of charging demand, a data-driven intelligent EV charging scheduling algorithm is proposed. Finally, a collaborative optimal routing and scheduling method is proposed

Design and implementation of hybrid series compensators for smart grid applications

The vision of future modern grids goes through the increase of renewable énergies penetration while providing an efficient, reliable and sustainable power supply to consumers. According to the recent report on climate challenging the way electrical energy is produced and because of the rapid emerging of power electronics based equipment; some serious actions should be engaged. In order to achieve such promoting visions, all power grids are required to become smarter especially at the distribution level. Increasing the application of renewable energy sources and distributed generations assist these vision in the development of a modern power grid where modern equipment are becoming highly sensitive to the supplied voltage quality. Moreover, in this paradigm of design, the traditional power systems based on large concentrated power plants should be able to deal with these unpredictable sources of energy at distribution level. Under these circumstances, considerable activities were carried out aiming to render the grid more flexible and intelligent while taking the power efficiency and its environmental impacts into account. In this way, the power quality issues should be considered for the development of new type of smart grids which are more efficient and sustainable with regards to environmental constraints. Available active and passive compensators are widely involved to improve major power quality issues. Recent trends towards realization of multitasking devices which can solve several power quality issues simultaneously, propose Hybrid active filters or Unified power quality conditioners. These versatile devices should threaten both voltage and current related issues in one place for compensation. They can significantly improve power quality issues, such as voltage distortions, voltage sags, voltage swells, voltage unbalances, and ensure a constant and reliable voltage supply to the load. On the other hand, they compensate for current problems of linear and non-linear loads, such as current harmonics, unbalances, neutral current, and load reactive power. The Hybrid series active filter (HSeAF) is among the most versatile and efficient power electronics based active power compensators. Without the shunt passive filter, the active part could operate solely to rectify for voltage problems and is commonly known as Dynamic voltage restorer. A conventional HSeAF, targeting three-phase system, consists of a three separate series isolation transformer connected to a three-phase converter sharing a common DC link bus. The device is controlled as a variable voltage source in similar but duality manner as of Shunt active power filter. A shunt passive filter tuned for harmonic frequencies is installed to produce an alternative path for load current harmonics and reducing voltage distortions at the load terminals. The existing literature suggests utilizing the hybrid active power filters to compensate for load current related issues only, while due to the complexity and implementation outlays of such devices, it shows a significant drawback of under usage of series compensation to address such power quality problems. The present doctoral research is based on the philosophy of optimal utilization of the available resources in the most efficient way to enhance the product efficiency and to reduce the overall cost. This work proposes a novel control approach for three-phase system in which both the grid’s voltage and load current issues are treated in a co-ordination between the series active and the shunt passive filters without affecting the basic voltage or current compensation capabilities. This eventually results in a better utilization of the series active filter, reduction of the shunt passive filter rating to some extent, and ultimately in the reduction of the overall cost for a unified compensator. Moreover, this thesis also introduces a novel transformerless topology in which the threephase configuration is split into separate devices. It is then possible to extent the Series active power compensation based for three-phase systems with three or four wires to single-phase or bi-phase systems. This newly transformerless hybrid series active filter (THSeAF) is first hosted for single-phase system where appropriate developed controllers ensure adequate operation under low profile power quality systems. The developed single-phase THSeAF concept is successfully validated through digital simulations as well as real-time extensive experimental investigations. The experimental results show that for a given laboratory test conditions with highly polluted nonlinear loads, the active compensator ride of the bulky transformer is capable of compensating load current and correcting the power factor. Moreover, the performance of the THSeAF under polluted grid supply with voltage harmonics, sags, and swells, demonstrates regulated and reduced voltage distortions at the load’s terminals. Following this successful transformerless configuration, and to integrate the series compensation concepts dedicated for power quality improvement of distribution network, the three-phase configuration is anticipated. Three-phase control strategies developed previously for the HSeAF are applied to the proposed topology to make the point of common coupling (PCC) smarter and to decentralize the control of the distribution network. This affordable solution increases the efficiency and sustainability of modern smart power systems and help higher penetration of renewable fluctuating power into the network. The off-line simulations demonstrate that the three-phase THSeAF is capable of healing voltage problems and load current issues simultaneously. The real-time experimental results, carried out on a laboratory prototype, validate successfully the proposed configuration