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Flexible Energy Management Protocol for Cooperative EV-to-EV Charging
... Также разрабатывается концепция взаимодействия между автомобилями -V2V (Vehicle-to-Vehicle) [6], автомобилем и устройством -V2D (Vehicle-to-Device), автомобилем и инфраструктурой, например, светофорами и дорожными знаками -V2I (Vehicleto-Infrastructure), автомобилем и пешеходом -V2P (vehicle-to-Pedestrian) с помощью выявления частотных диапазонов смартфонов. Эти системы связи предназначены для обмена информации и дорожном движении и обеспечения безопасности движения. ...
Growth of technology leads to an increase in the number of electric vehicles (EV) on the roads in the world. The technologies of rapid battery charging and wireless charging on the parking and on the move are developing for electric vehicles-EV (electric cars, electric buses, mobile robots). At the same time, electric transport can potentially be not only a consumer of energy, but also an important link in the process of energy transfer. In this article, the place of electric vehicles in the new concept of the Internet of Energy is considered, as well as the problems that must be solved for its optimal use.
... Также разрабатывается концепция взаимодействия между автомобилями -V2V (Vehicle-to-Vehicle) [6], автомобилем и устройством -V2D (Vehicle-to-Device), автомобилем и инфраструктурой, например, светофорами и дорожными знаками -V2I (Vehicleto-Infrastructure), автомобилем и пешеходом -V2P (vehicle-to-Pedestrian) с помощью выявления частотных диапазонов смартфонов. Эти системы связи предназначены для обмена информации и дорожном движении и обеспечения безопасности движения. ...
Growth of technology leads to an increase in the number of electric vehicles (EV) on the roads in the world. The technologies of rapid battery charging and wireless charging on the parking and on the move are developing for electric vehicles-EV (electric cars, electric buses, mobile robots). At the same time, electric transport can potentially be not only a consumer of energy, but also an important link in the process of energy transfer. In this article, the place of electric vehicles in the new concept of the Internet of Energy is considered, as well as the problems that must be solved for its optimal use.
... In [37] Zhang et al. provided a developed V2V (Vehicle to Vehicle) charging concept, termed as cooperative V2V charging, which enables active cooperation through charging and discharging operations between EVs. In [38] Zhang et al. investigated the flexible power transfer among EVs from a cooperation perspective in an energy Internet based EV system. A flexible energy management protocol was further proposed to help the EVs achieve more flexible and smarter charging/discharging behaviors. ...
With the increasing number of electric vehicles, temporary charging demands grow rapidly. Unlike charging at home or workplace, temporary charging requires less waiting time. In this paper, a mobile edge computing (MEC) enabled Charging and Discharging Networking System Algorithm (CDNSA) is proposed to minimize the waiting time for electric vehicles (EVs) in charging stations (CSs). Software defined network (SDN) paradigm is adopted to enhance the data transmission efficiency for MEC servers. In CDNSA, the optimization problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP). A heuristic algorithm is proposed to solve the optimal CS selection variables for EVCs (Evs that needs to be Charged) and EVDs (EVs that can be Discharged), and then a remaining problem Non-Linear Programming (NLP) is obtained. By verifying the convexity of each continuous variable, the NLP is solved by adopting Block Coordinate Descent (BCD) method. In simulation, the optimality of CDNSA is verified by comparing with exhaustive algorithm in terms of minimizing maximal waiting time (MMWT) of charging stations. We also compare CDNSA with other benchmarks to illustrate its advantage.
Fossil‐fuelled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using massive unexploited capacity of the battery storage in too many EVs makes these challenges to opportunities. This unused capacity can be employed for the grid ancillary services and trading peer‐to‐peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EVs bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid based on blockchain mechanism. Considering the preferences of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimisation problem aiming at optimal management of EV battery state of charge and energy placement of several services considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real‐world data collected from Tehran city are used as input data of simulation. Numerical results demonstrate the efficacy of the presented model. Simulation results show that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy‐planning model such that it could balance the charging cost. Moreover, this advanced user‐based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid.
Electric vehicles (EVs) as the frequency regulation (FR) resources with fast response speed provide a promising solution to improve FR performance. However, the exorbitant battery degradation cost (BDC) restricts EVs providing FR service. To achieve the FR performance with cost‐efficiency, a hierarchical dispatching method based on the Hungarian algorithm is proposed for reducing the BDC of EVs in this study. To ensure system security, the required FR power is dispatched to FR units and EV aggregators according to the optimization coordinating strategy for reducing frequency fluctuation. Then, EV aggregators further dispatch the power to each EV for reducing the total BDC of EVs. In the dispatching process, various EVs provide FR service in the light of their actual condition such as BDC characteristics and state‐of‐charges (SOCs). To calculate the BDC of EVs accurately, the corresponding cost formulas that consider various EVs are fitting for guiding power dispatching. According to the sensitivity analysis, the FR regularity of EVs about FR performance is drawn for promoting the quantity of EVs participating in FR and further improving FR performance. Extensive case studies are presented to validate the effectiveness of the proposed method, where a large number of EVs participate in FR with cost‐efficiency.
This paper presents the design and implementation of a single-phase multifunctional electric-vehicle (EV) on-board charger with an advanced vehicle-to-vehicle (V2V) functionality for emergency roadside charging assistance situations. Using this function, an EV is able to charge from another EV in case of an emergency when the battery is flat and there is no access to a charging station. The designed EV charger can support the proposed V2V function with rated power and without the need for an additional portable charger. It can also provide conventional functions of vehicle-to-grid (V2G), grid-to-vehicle (G2V), the static synchronous compensators (STATCOM) and active power filter (APF) (i.e. reactive power support, and harmonics reduction). All the functions are addressed in the control part through the sharing of existing converters in an all-in-one system. The proposed EV charger is designed and simulated in MATLAB/Simulink, and a laboratory prototype is also implemented to validate its key functions.
The vehicle transport system is rapidly increasing towards a sustainable electric vehicle population. In this chapter, cloud-based monitoring and management of the smart charger station for electric vehicles (EVs) for the security-driven IoT enabled direct current (DC) fast chargers is discussed. For plug-in electric vehicles and hybrid vehicles (xEVs), energy storage devices are charged through the power grid. The proposed methodology discussed a dedicated interface to predict and estimate the battery energy consumed at different locations and the bill payment process. The chapter discusses the information about the charging time for the energy storage devices of the electric vehicles. Also, technological consideration of conventional alternating current (AC) chargers and DC fast chargers are studied. The retrieved information is shared via the internet at each charging station with the original equipment manufacturers to analyse the vehicle’s charging cost through integration of IoT operated devices. Also, the EV users are provided with an optimal energy trading solution for all units integrated and associated parameters are facilitated at the DC fast charging stations. At each charging location, the EV metering architecture acquires real-time data to provide current information about the functions and behaviours at the energy distribution network. Thus, the platform provides an optimal way for the system to provide an execution framework for the EVs users to provide energy demand solutions to all entities operated at a smart chargers infrastructure facility charging station.
In this article, a new optimization framework is introduced to facilitate peer-to-peer (P2P) electric vehicles (EVs) charging in different power distribution networks (PDNs) of the city. A concept of multi-area PDN is projected to optimize the EV charging while improving the performance of multiple PDNs simultaneously. Objectives of the model are to minimize system power loss and node voltage deviation by exploiting charging points, in multiple distribution systems, of different areas simultaneously. For real-life scenario, the proposed approach is implemented on eight PDNs, modeled based on available geographic information. To solve this optimization problem, an improved genetic algorithm is adopted. The effectiveness of proposed model is demonstrated by comparing the performance of these PDNs in different EV charging strategies.
Direct Vehicle-to-vehicle (V2V) charge sharing system has the potential to provide more flexibility to electric vehicle (EV) charging without depending on the charging station infrastructure or building designated parking lots. It can also provide an opportunity to shift peak time utility load to off-peak times. However, the assignment between the EVs that demand energy and the EVs with surplus energy or existing charging stations is a challenging problem as it has to be performed in real-time considering their spatio-temporal distribution, availability and grid load. In this paper, we study this assignment problem specifically in a supplier non-intrusive scenario (without changing their mobility) and aim to understand the potential benefits of a direct V2V charge sharing system. To this end, we present two new algorithms to match the demander EVs to suppliers for charging. In the first one, the maximum system benefit is targeted under multiple system objectives with different priorities and considering the grid load. In the second one, individual EV priorities are taken into account and a stable matching process depending on predefined user preferences is provided. We conduct extensive simulations using real user commuting patterns and existing charging station locations in three different cities together with a newly developed probabilistic EV charging behavior model and EV mobility. Simulation results show that direct V2V charge sharing can reduce the energy consumption of EVs by 20-35% by providing a closer charging facility compared to charging station only case, offload grid charging power in peak times by 35-55%, and help sustain up to 2.2x EV charging requests without building new charging infrastructure, while causing a marginal increase (i.e., 3-9%) in the energy cycling of supplier EVs. The results also show that the proposed matching algorithms offer 80-90% more energy consumption reduction compared to the intrusive V2V charging at designated parking lots.
Electric Vehicles (EVs) can be considered as a flexible source of energy which can receive some benefit in terms of incentives for selling their energy. For efficient and economic trading amongst the EV owners, various researchers have proposed a variety of preference based matching algorithms like College Admission Framework (CAF), Max-weight, Merge and Split, Gale-Shapley Algorithm and Brute-Force Algorithm etc, where buyer and seller EVs can exchange energy and receive better payoffs. Unlike the above mentioned algorithms, in this paper the participating entities do not submit the preferences menu (which contains preferred choices of sellers (of a buyer) and of buyers (for a seller)) to a central authority.) However, this paper proposes an assignment energy trading game where no central authority is needed, the matching algorithm is hosted on cloud which matches charging and discharging of EVs based on their aspiration level and bids. The contribution of the work is to deduce the bids and aspiration level of charging and discharging EVs which is not considered in any of the existing work. Another contribution of the work is the behavioral assignment game that eludes the need of integer linear programming problem and deduces the convergence of game by adjustments of aspiration levels. Futhermore, the entire algorithm is cloud hosted with no middleman hence trading EVs identities are concealed from each other making the system unbiased. The proposed game helps both the buyer and the seller side of EVs to achieve their best bids as well as by reducing grid dependency it boosts the profit margin of the charging stations (CS).
The global warming problem together with the environmental issues has already pushed the governments to replace the conventional fossil-fuel vehicles with Electric Vehicles (EVs) having less emission. This replacement has led to adding a huge number of EVs with the capability of connecting to the grid. It is noted that the presence of such vehicles may introduce several challenges to the electrical grid due to their Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) capabilities. In between, the Power Quality (PQ) issues would be the main items in electrical grids highly impacted by such vehicles. Thus, this paper is devoted to investigating and reviewing the challenges brought to the electrical networks by EVs. In this regard, the current and future conditions of EVs along with the recent research works made into the issue of EVs have been discussed in this paper. Accordingly, the problems due to the connection of EVs to the electrical grid have been discussed, and some solutions have been proposed to deal with these challenges
The emerging Smart Grid (SG) paradigm promises to address decreasing grid stability from thinning safe operating margins, meet continually rising demand from pervasive high capacity devices such as electric vehicles (EVs), and fully embrace the shift towards green energy solutions. At the SG edge, widespread decentralisation of heterogeneous devices coupled with fluctuating energy availability and need as well as a greatly increased fluidity between their roles as energy producers, consumers, and stores raises significant challenges to ensuring robustness and security of both information and energy exchange. Detecting and mitigating both malicious and non-malicious threats in these environments is essential to the realisation of the full potential of the SG. To address this need for robust, localised, real-time security at the grid edge we propose CONCEDE, a collaborative cross-layer ego-network integrity awareness and attack impact reduction extension to our previous work on delay-tolerant cognitive adaptive energy exchange. We detail a substantial, targeted, energy disruption attack perpetrated by colluding mobile energy prosumers. Our CONCEDE proposal is then evaluated in multiple, diverse smart micro-grid (SMG) scenarios using hybrid traces of EVs and infrastructure from Europe, North America, and South America in the presence of a coordinated attack from malicious distributors seeking to disrupt energy supply to a target community. We show that CONCEDE successfully detects and identifies the nodes exhibiting malicious, dishonest behaviour and that CONCEDE also reduces the impact of a coordinated energy disruption attack on innocent parties in all explored scenarios across multiple criteria.
The current age is witnessing speedy revolution of vehicles from the hundred-year old moving metal box on four wheels into a new species with dazzling intelligence. To enable such intelligence, the nervous system heavily hinges upon the connectivity among vehicles as well as between vehicles and the transportation infrastructure. With such intelligence, humans would be relieved from the driving duties and naturally convert the vehicle into moving offices or entertainment rooms, thus imposing unprecedented burden to the connectivity to the world beyond the vehicle. Due to the mobile nature of vehicles, wireless naturally becomes the rescue. However, though wireless has been, to some extent, deployed on vehicles for more than half a century, the current wireless-vehicle interactions are, to the best, a mere combination, in which the wireless systems are designed accounting for the mobile environment, but do not have much to do with the vehicle core functions. In this paper, we will discuss the challenges, progresses and perspectives of the present-to-the-near-future vehicular wireless channels, wireless-vehicle combination, as well as the more demanding wireless-vehicle integration.
While the wireless-vehicle combination intends to increase the efficiency of link-level and network-level data transmissions to fulfill the communication requirements in vehicular applications, the wireless-vehicle integration focuses on exploring the core functions of vehicles that are evolving more and more towards being highly intelligent and electrified. Surrounding the core vehicle functions, in this chapter, the requirements on the supporting wireless infrastructure and how to achieve these requirements will be discussed from the wireless-vehicle integration perspective. Specifically, we will focus on some interesting VCN-based vehicular applications including electric vehicles, distributed data storage, and physical layer security. As for the next leap, VCN-based autonomous driving is also discussed.
To enable electric vehicles (EVs) to access to the internet of intelligent vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent need. The resonant beam charging (RBC) technology can provide high-power and long-range wireless energy for EVs. However, the RBC system is unefficient. To improve the RBC power transmission efficiency, the adaptive resonant beam charging (ARBC) technology was introduced. In this paper, after analyzing the modular model of the ARBC system, we obtain the closed-form formula of the end-to-end power transmission efficiency. Then, we prove that the optimal power transmission efficiency uniquely exists. Moreover, we analyze the relationships among the optimal power transmission efficiency, the source power, the output power, and the beam transmission efficiency, which provide the guidelines for the optimal ARBC system design and implementation. Hence, perpetual energy can be supplied to EVs in IoIV virtually.
With the rapid development of EVs and their penetration in our modern society, the energy management issues in the smart grid integrated with EVs are attracting more and more research interest. Most of the existing literature only involves two-sided interactions between the EVs and the power grid or the smart home/buildings in the DSM investigation. In fact, however, similar to EVs, when equipped with distributed renewable energy sources and electricity storage facilities, SCs can also be regarded as either energy consumers or energy providers with their individual rationality. This can lead to increased?exibility of the energy management and power trading in the power system involving both EVs and SCs. In this article, we propose a three-party architecture for the smart grid integrated with EVs, involving complex and?exible interactions among the power grid, EVs, and SCs. Based on the proposed three-party architecture, we introduce two interesting systems, SC-centered and EV-centered systems, and further propose a schedule-upon-request energy management framework to achieve effective and intelligent energy management in the power system involving both EVs and SCs. Feasible optimization methods for energy management problems in this framework are also summarized.
Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.
Electric vehicles (EVs) are regarded as one of the most effective tools to reduce the oil demands and gas emissions. And they are welcome in the near future for general road transportation. When EVs are connected to the power grid for charging and/or discharging, they become gridable EVs (GEVs). These GEVs will bring a great impact to our society and thus human life. This paper investigates and discusses the opportunities and challenges of GEVs connecting with the grid, namely, the vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G) technologies. The key is to provide the methodologies, approaches, and foresights for the emerging technologies of V2H, V2V, and V2G.
Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or ‘users’). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
This paper presents an architecture for a future electric power distribution system that is suitable for plug-and-play of distributed renewable energy and distributed energy storage devices. Motivated by the success of the (information) Internet, the architecture described in this paper was proposed by the NSF FREEDM Systems Center, Raleigh, NC, as a roadmap for a future automated and flexible electric power distribution system. In the envisioned “Energy Internet,” a system that enables flexible energy sharing is proposed for consumers in a residential distribution system. The key technologies required to achieve such a vision are presented in this paper as a result of the research partnership of the FREEDM Systems Center.
With the rapid development of EVs and their penetration in our modern society, the energy management issues in the smart grid integrated with EVs are attracting more and more research interest. Most of the existing literature only involves two-sided interactions between the EVs and the power grid or the smart home/buildings in the DSM investigation. In fact, however, similar to EVs, when equipped with distributed renewable energy sources and electricity storage facilities, SCs can also be regarded as either energy consumers or energy providers with their individual rationality. This can lead to increased?exibility of the energy management and power trading in the power system involving both EVs and SCs. In this article, we propose a three-party architecture for the smart grid integrated with EVs, involving complex and?exible interactions among the power grid, EVs, and SCs. Based on the proposed three-party architecture, we introduce two interesting systems, SC-centered and EV-centered systems, and further propose a schedule-upon-request energy management framework to achieve effective and intelligent energy management in the power system involving both EVs and SCs. Feasible optimization methods for energy management problems in this framework are also summarized.
This article proposes the consumer-centered energy system (CCES), a cyber-physical-social system that integrates the power grid (physical), communications and computing (cyber), and consumer interactions (social). The authors introduce some basic features of the CCES, provide a CCES architecture for electric vehicles and smart grid, and propose a request-and-schedule energy management protocol.
Recently, plug-in hybrid electric vehicles (PHEVs) have attracted considerable attention as a sustainable transport system and also an essential component of the smart grid. With the rapid growth of PHEVs penetration, the charging and discharging of PHEVs will pose a significant impact on the residential electricity distribution network. For this reason, the management of PHEV charging and discharging has become one of the key issues in the research of PHEVs. In most existing work, PHEVs are supposed to operate individually for charging and discharging in the grid. However, we argue that, by leveraging the cooperation among PHEVs, the grid will efficiently stimulate PHEV users to charge in load valley and discharge in load peak. As a consequence, the electricity load is well balanced. Meanwhile, the PHEV users also achieve higher profit. The PHEV charging and discharging cooperation is a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of coalition game. The simulation results indicate that the peak-valley difference in electricity load of the grid is significantly reduced. Besides, the PHEV users have better satisfaction in the vehicle battery status and the economic profit.
In the foreseeable future, power grids will be managed largely with demand-side management (DSM) programs. With the growing population of electric vehicles (EVs) and the emergence of aggregators, DSM will surely introduce more intense competition to the markets. Since EV charging produces a large amount of time-flexible load in power systems, competition of its management could become a major game. This paper first formulates the game of EV charging management to describe this major form of the future DSM competition and then studies three challenges inherent in it: 1) inefficiency of Nash equilibria; 2) the game of chicken; and 3) cheating on private information. It is found that a central regulator is required to prevent these drawbacks. Solutions are proposed and a central governing procedure is also presented. The notion of the game of EV charging management is compatible with DSM programs that are able to schedule load flexibly over multiple time periods.
In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or the energy stored in their plug-in electric vehicles. A distributed optimization algorithm based on the alternating direction method of multipliers is developed to solve the optimization problem, in which consumers need to report their aggregated loads only to the utility company, thus ensuring their privacy. Consumers can update their loads scheduling simultaneously and locally to speed up the optimization computing. Using numerical examples, we show that the demand curve is flattened after the optimization, even though there are uncertainties in the model, thus reducing the cost paid by the utility company. The distributed algorithms are also shown to reduce the users' daily bills.
With the more stringent regulations on emissions and fuel economy, global warming, and constraints on energy resources, the electric, hybrid, and fuel cell vehicles have attracted more and more attention by automakers, governments, and customers. Research and development efforts have been focused on developing novel concepts, low-cost systems, and reliable hybrid electric powertrain. This paper reviews the state of the art of electric, hybrid, and fuel cell vehicles. The topologies for each category and the enabling technologies are discussed
The Hungarian Algorithm for Weighted Bipartite Graphs
- A Grinman