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Cooperative Recharge Method of Connected Electric Vehicles in Smart Grid
Abstract
Knowledge sharing between Electric Vehicles and Smart Grid is a source for improved load management and control. Recharge stations using event-driven communication share the information about recharge processes going to occur, while optimization agent(s) might prepare the optimal energy use policies. In this paper, authors showcase one approach for better integrating electric vehicles into smart grid. A photovoltaic power production is considered, in order to take into account the variability of power availability. The use of event-driven approach highlights faster reaction to the changes occurring inside the electric grid.
... Simonov et al. [22] Information sharing for EVs and smart grid Using event-driven approach integrates EVs into smart grid Gonzalez et al. [23] Using EV intelligent charging (EVIC) device to achieve intelligent charging ...
The private motor vehicles are significantly important means of transportation in modern lifestyle, however, these also contribute to a large proportion of the total air pollution and primary energy consumption. In order to develop green transportation system, it becomes imperative to use integrated technologies to achieve reduced emissions and utilize renewable energy. Electric vehicles (EVs) have been considered as one of these technologies to transform the traditional vehicle mix. However, the uptake of EV has been debated on factors like cost, performance (autonomous mileage), charging point infrastructure construction, energy saving, policy and end users’ adaptation. Present study investigates the technology feasibility (which usually refer to EVs’ cost, EV charging, supplier’s customer services quality, EV travel performance) and users’ adaptation of EV in Beijing, which is a key driver for the EV uptake into the Beijing transportation system. The relevant data have been collected and analyzed in the form of questionnaire survey around all of these factors. While considering the user perception and satisfaction, safety of charging and energy bills have also been investigated. According to the data analysis, it has been found the policy of ‘No traffic restrictions for EVs’ (the traffic restrictions means for certain date, from Monday to Friday the motor vehicles with the last register number of 1 and 6, 2 and 7, 3 and 8, 4 and 9, 5 and 0, are restricted to travel, respectively), the availability of the charging infrastructure and technical support are the most significant factors affecting the users’ opinions on using EVs.
... The claim is that through the collaborative approach, the need for a central energy management unit is eliminated, making the system more robust against single node failures. Similarly, [68] explores information sharing among charging stations to reach optimal charging schedule, smoothing peak demands. The work of Monteiro et al. [69,70], also explores the opportunity of building collective awareness through information sharing, and the possibility of bidirectional power flows to smooth peak demands and compensate for intermittency of renewable energy production. ...
Smart grids are the result of a dynamic co-evolution process that leverages the integration of new technological advances in the energy systems and information and communication technologies. This process is accompanied by changes in business models, organizational structures, roles, and operating practices. In this context, collaboration among multiple entities becomes a crucial element, justifying the term Collaborative Smart Grid. The purpose of this article is to systematically review recent literature with a view to identifying trends, opportunities, and challenges regarding the application of models, approaches, and tools from collaborative networks to the energy domain.
The rising awareness of the limitation of resources and the environmental impact of CO2 emissions in combination with the technological progress in the last decades brought electric vehicles back on the market. Contrary to the still mainstreaming passenger cars with internal combustion engine, electric cars do not necessarily depend on a CO2-emmitting process as power source if they are equipped with a charging interface for the internal battery. The fact that an infrastructure for electricity already exists is beneficial in terms of power availability and the possibility to use renewable energy sources for charging. But the challenging aspect is that the capacity of the electrical grid was originally neither designed for the additional load by electric vehicles nor for the simultaneously ongoing energy transition from conventional to renewable energy generation. However, the fact that an average vehicle is most of the time parking and potentially connected to the grid makes an electric vehicle flexible regarding the charging time schedule. This flexibility can be utilized in a smart grid context. Researchers are involved with this topic for several years now and many findings have already been published. The objective of this paper is to give an overview over already investigated problems and developed solutions. Therefore a general map of aspects which outline the problem of grid integration and the utilization of the electric vehicles flexibility is presented. The report on the review of papers shows which topics have been investigated and what methods have been used.
An energy distribution network is a critical infrastructure that any compromise has an enormous impact on daily lives and the economy. The objective of this work is a computerized tool for distributed monitoring, dynamic re-configuration and control of DC distribution topology. This paper describes the double bar bus DC system exploiting the event-driven, service-oriented architecture, and real-time metering with nonuniform time sampling as an example of neighborhood optimization. We build a system with the capability to assess the resilience of and to rebuild better resilient grid partitions at run-time. The result is an intelligent system distributing loads between two buses dynamically in a way to keep self-sustainable and/or non-interruptible portion running at one bus by moving few other loads to the second bus. In standalone modality, the tool assesses the survivability of microgrid with high penetration of renewable energy. Running in cooperation with grid management tools, the same software can reconfigure optimally the local topology at run-time.
Nowadays, there is a conflict between the rapidly increasing demand for electricity and the requirement for reducing dependence on fossil fuel to decrease the greenhouse gas emissions. Proper utilization of renewable energy such as wind energy is proposed as an efficient solution to address this problem. However, due to the high inter-temporal variation and limited predictability, it is difficult to make full use of renewable energy as supplement to the conventional thermal power plants in smart grid. In this paper, we provide an energy efficient solution to solve this problem: the integration of Pug-In-Hybrid Electric Vehicle (PHEV). Through proper charging and discharging processes, the PHEV fleet can act as energy storage when there is excess renewable energy, while in case of energy shortage, the energy can be properly returned to the grid. Optimization models based on stochastic programming are developed for both the cases without PHEV fleet and with PHEV fleet, while various uncertainties such as power price, renewable power and user demand are taken into account. By solving the two stochastic programming problems, the optimal power management solutions are obtained, and the numerical results show that the integration of PHEV can effectively reduce the energy to be generated by conventional thermal power plants, and as a result, the overall energy generation cost can be significantly reduced.
Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. We present an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. We use machine learning techniques to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. We tested our system in one home for several weeks and in five homes for one week each to evaluate the system performance over time and in different types of houses. Results indicate that we can learn and classify various electrical events with accuracies ranging from 85-90%.
As plug-in electric vehicles become more widespread, their charg-ing needs to be coordinated, in order to ensure that capacity constraints are not exceeded. This is becoming particularly critical as new fast-charging technolo-gies are being developed that place additional burden on local transformers. To address this problem, we propose a novel online mechanism in which agents representing vehicle owners are incentivised to be truthful not only about their marginal valuations for electricity units, but also about their arrival, departure and maximum charging speeds. The work extends the state of the art in several ways. We develop an online, model-free mechanism that handles multi-unit de-mand per period, thus accommodating vehicles with heterogeneous and flexible charging speeds; we provide competitive worst-case bounds for our mechanism; finally, we simulate the proposed online mechanism using data from a real-world trial of electric vehicles in the UK, showing that using fast charging leads to sig-nificant cost savings.
This paper presents a novel method of planning the charging of electric-drive vehicles that takes electricity grid constraints into account. The method computes an individual charging plan for each vehicle while minimizing the cost of electricity, avoiding distribution grid congestion, and satisfying the individual vehicle owner's requirements. The underlying algorithm is explained through a simple example and tested on a simulated electricity grid. The method is shown to significantly reduce the overloading in the electricity grid compared to charging schemes that do not consider grid constraints.
Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. We present an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. We use machine learning techniques to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. We tested our system in one home for several weeks and in five homes for one week each to evaluate the system performance over time and in different types of houses. Results indicate that we can learn and classify various electrical events with accuracies ranging from 85-90%.
This paper describes an approach to optimize elec-tric vehicle battery charging behavior with the goal of minimizing charging costs, achieving satisfactory state-of-energy levels, and optimal power balancing. Two methods for charging schedule optimization are compared. The first formulation uses a linear approximation of the battery behavior, whereas the second uses a quadratic approximation. A non-linear and state-dependent battery model is used to evaluate the solutions of the two methods. Our results indicate that the linear approximation is sufficient when considering the electric vehicle charging plan optimization.
Battery-electric vehicles and grid-connected hybrid vehicles rely on the power grid for energy --they have to plug in to charge their batteries. With power alerts and blackouts a recent reality in California, it is easy to conclude that the energy requirements of grid-connected electric vehicles will make the energy crisis worse. Actually, quite the opposite may be true. With a bi-directional grid power interface, virtually any vehicle that can plug into the grid can potentially provide beneficial support to the grid. Battery electric vehicles can support the grid exceptionally well by providing any of a number of functions known collectively as ancillary services. These services are vital to the smooth and efficient operation of the power grid. A hybrid vehicle can provide ancillary services, and can also generate power. Fuel cells are already being commercialized for small stationary power sources, so a vehicle-mounted fuel cell could also serve as a vehicle-to-grid power source. Sharing power assets between transportation and power generation functions can create a compelling new economics for electrically-propelled vehicles.
This paper discusses studies funded by the National Renewable Energy Laboratory (NREL) via SunPower, Inc. and the California Energy Commission (CEC), performed by New Power Technologies and Optimal Technologies that showed that high-penetrations of distribution-connected storage devices or plug-in vehicles can have adverse grid impacts due to their charging loads. Randomly-located or unmanaged additions, such as plug-in vehicles, can also have greater impacts at lower penetrations when compared to managed additions such as utility-sponsored storage. The studies also found that potential adverse impacts from such charging loads are highly localized, and once identified are readily managed. The studies also show the use of a high-definition Energynetreg power system simulation and AEMPFASTtrade power system optimization software for identifying and managing the potential impacts of distribution-connected storage.
Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular. The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park. These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations. Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay. This uncoordinated power consumption on a local scale can lead to grid problems. Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor. The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses. As the exact forecasting of household loads is not possible, stochastic programming is introduced. Two main techniques are analyzed: quadratic and dynamic programming.
Improving Grid Sustainability by Intelligent EV Recharge Process
- M Simonov
- A Attanasio
M. Simonov, A. Attanasio, Improving Grid Sustainability by Intelligent EV Recharge Process, in L. Frommberger et al. (eds.) Proc. of 3rd Workshop on Artificial Intelligence and Logistics (AILog-2012), pp.19-24, Montpellier, France, 2012.