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Particle Swarm Optimization. Encyclopedia of Machine Learning. About the Journal. Join as Reviewer. Editorial Board.

Threshold-Based Random Charging Scheme for Decentralized PEV Charging Operation in a Smart Grid

Online First. Current Issue. Advanced Search. Article Submission. User Username. Remember me. Article Tools Print this article. Indexing metadata. How to cite item. Finding References. Based on the positive outcome of the tests an MSc thesis was published looking into the technical and economic feasibility assessment of a hydrogen and FCEV based Car Park as Power Plant offering frequency reserves. Willett Kempton, Dr. Suresh Advani, and Dr. Kempton being the lead on the project.

Kempton has published a number of articles on the technology and the concept, many of which can be found on the V2G project page. The Company has formed a number of industry partnerships and implemented V2G pilot projects on five continents worldwide. In addition to the technical research, the team has worked with Dr.

Meryl Gardner, a Marketing professor in the Alfred Lerner College of Business and Economic at the University of Delaware, to develop marketing strategies for both consumer and corporate fleet adoption. Its models are used to investigate the challenges and opportunities of V2G services, such as modulation of charging time and charging rate for peak demand response and utility frequency regulation. V2G-Sim has also been used to research the potential of plug-in electric vehicles for renewable energy integration.

Preliminary findings using V2G-Sim have shown controlled V2G service can provide peak-shaving and valley-filling services to balance daily electric load and mitigate the duck curve. On the contrary, uncontrolled vehicle charging was shown to exacerbate the duck curve. The study also found that even at 20 percent fade in capacity, EV batteries still met the needs of 85 percent of drivers.

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In another research initiative at Lawrence Berkeley Lab using V2G-Sim, V2G services were shown to have minor battery degradation impacts on electric vehicles as compared to cycling losses and calendar aging. In May , Nissan and Enel power company announced a collaborative V2G trial project in the United Kingdom, the first of its kind in the country. The project claims electric vehicle owners will be able to sell stored energy back to the grid at a profit.

Willett Kempton has been conducting on-going research. Dr Kotub Uddin analysed lithium ion batteries from commercially available EVs over a two year period.

He created a model of battery degradation and discovered that some patterns of vehicle-to-grid storage were able to significantly increase the longevity of the vehicle's battery over conventional charging strategies, while permitting them to be driven in normal ways.

There is some skepticism among experts about the feasibility of V2G and several studies have questioned the concept's economic rationale.

When these less obvious costs are included, the study finds that V2G represents an economically inefficient solution. The more a battery is used the sooner it needs replacing. Cycling loss is due to usage and depends on both the maximum state of charge and the depth of discharge.

He also prefers recycling over re-use for grid once batteries have reached the end of their useful car life. Another common criticism is related to the overall efficiency of the process. Charging a battery system and returning that energy from the battery to the grid, which includes "inverting" the DC power back to AC inevitably incurs some losses. This needs to be factored against potential cost savings, along with increased emissions if the original source of power is fossil based.

Additionally, in order for V2G to work, it must be on a large scale basis. Power companies must be willing to adopt the technology in order to allow vehicles to give power back to the power grid. There are several electric vehicles that have been modified or are design to be compatible with V2G. From Wikipedia, the free encyclopedia. This section needs to be updated. Please update this article to reflect recent events or newly available information.

The role of Electric Vehicles in a Smart Grid

September This section reads like a press release or a news article or is largely based on routine coverage or sensationalism. Please expand this article with properly sourced content to meet Wikipedia's quality standards , event notability guideline , or encyclopedic content policy. This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. September Learn how and when to remove this template message. Energy portal Renewable energy portal.

Charge control Charging station Distributed generation Electranet Electric vehicle battery Electricity meter Energy demand management Feed-in tariff Grid energy storage Grid-tied electrical system Load profile Load balancing electrical power Operating reserve Peaking power plant Power outage RechargeIT Smart meter Solid-state battery.

Bibliographic Information

Dictionary of Energy. Amsterdam: Elsevier. Archived from the original on Retrieved Applied Energy. International Journal of Automotive Technology. February Energy Policy. Impacts of electric vehicle loads on power distribution systems. Green Wombat. Nevertheless, the penetration of PEV charging systems in the conventional power distribution system may negatively affect the power distribution network in terms of voltage drops and power losses [ 2 , 3 , 4 ].

Moreover, as the charging of the PEVs is uncoordinated in the existing power distribution network, the penetration of the PEVs will eventually become a burden for the conventional network [ 5 , 6 ]. To optimize the PEV charging system with coordination from a variety of perspectives, several studies have been performed to analyze the possible impacts on the power systems by the PEV charging loads [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].

The main objectives of those studies are the mitigation of the negative impacts by minimizing the increment of peak loads [ 7 , 8 , 9 ], reducing the power losses [ 10 , 11 ], and minimizing the charging costs [ 12 , 13 ] or maximizing the discharging profits, i. Therefore, most of the research done so far mainly focuses on the centralized optimization of the PEV charging system by stabilizing the power distribution system or by minimizing the charging costs. Recently, decentralized charging schemes have been considered to alleviate the computational complexity of a central controller [ 17 , 18 , 19 ].

To decide the participation of PEV charging, these previous decentralized charging schemes also require that each customer points of charge CPOC should know real time information of all other CPOCs and the central controller, such as a battery state of charging BSOC , voltage drops and power network constraints. However, communication networks to exchange the real time information among the CPOCs are hard to implement, because the real time information of all the CPOCs and the complexity of the central controller exponentially increase according to the increment of the PEVs in the PEV charging system.

As the real time information increases, the cost of data transmissions increases and a complex communication infrastructure is required. For the practical implementation of the decentralized EV charging system, the decentralized charging system should exploit limited information, such as the information of the CPOC itself and statistical information of the power network constraints.

In wireless communication, a selective multiuser diversity SMUD scheme is proposed for attaining a reduction in the feedback overhead as well as for a multi-user diversity gain [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. In the SMUD scheme, users with a channel condition higher than a predetermined threshold, transmit their channel information to a base station, while the others do not transmit. The predetermined threshold is obtained by the average signal-to-noise ratio SNR , which is related to the distance between the transmitter and receiver.

The base station, then, prioritizes the requesters to allocate limited resources for data transmission. Thus, as a portion of users can participate in the requests for the data transmission to the base station, it is possible to reduce the participating users to require the data transmission compared to the conventional multiple user diversity schemes.

Therefore, the SMUD can obtain a multi-user diversity and mitigate computational complexity of the central controller. For this reason, the SMUD is widely known as a distributed scheduling algorithm. According to the number of antennas, different types of fading channels and further feedback overhead mitigation, there have been several different types of SMUD. As the base station and mobile users had a single antenna, the threshold was determined by the normalized average SNR [ 20 ].

In [ 21 , 22 ], the base station and the mobile users had multiple antennas. In addition, capacity analyses of systems with multiuser diversity for Rayleigh and Nakagami fading channels were researched [ 23 , 24 ]. To further mitigate the feedback overhead, the imperfect average SNR feedback schemes using one bit of information were addressed [ 25 , 26 ].

In this paper, a threshold-based random charging scheme TBRC is proposed to reduce the number of PEVs participating in the charging requests to minimize the computation complexity of the central controller. As the number of charging requests by the PEVs increase, the handling of the computation complexity in the central controller deteriorates.

Therefore, the proposed threshold-based charging method can reduce the number of the charging required PEVs in the PEV charging network. Since the threshold is determined by quantizing the BSOC levels, the number of PEVs below the threshold increases cumulatively with the charging time, which causes a significant increase of control signaling overhead.

To further mitigate the charging requests, a randomized charging scheme is also considered, which PEVs below the charging threshold can transmit their charging requests at a predetermined access rate. Thus, the calculation of the thresholds and access rates do not need real time information exchange and updates among the CPOCs and between the target CPOC and the central controller. Therefore, the PEV can decide to participate in the charging request based on the statistical information of the PEV charging system. Further, the central controller finally determines the set of charging PEVs under the real time power network constraints for guaranteeing the system stability.

It is assumed that all the PEVs are charged at off-peak times of the day, from 10 p. In this paper, the charging rate is constant at 4 kW Mode 1. As Mode 1 is the slowest charging mode, it accounts for the worst case of a PEV charging scenario and clearly demonstrates the negative effect of PEV charging system.

To simplify the problem, there will be no interruptions during a charging process i. These assumptions allow the study to focus on the objective of determining the effect of the reduction in the number of the PEV charging request by comparing the proposed scheme with the SCSC, with all other conditions equal. In this paper, two different types of loads are considered; one is residential loads that cannot be delayed, such as household heating, lighting, cooking and entertainment, and the other is PEV charging loads that can be elastically delayed.

As the distribution network needs to firstly satisfy the expectations of the residential loads while charging the PEVs, it is necessary to consider the effect of the residential loads on the supply power to the PEVs. In other words, the supply power for charging the PEVs is the subtraction between the supply power from a feeder and the residential loads. Furthermore, due to the time-varying property of the supply power from the feeder and the residential loads, we consider that the supply power for charging the PEVs is modeled with a sinusoidal waveform to simplify the problems.

In the previous studies [ 11 , 18 , 27 ], the supply power for charging the PEVs was assumed to be a convex shape with the maximum power in the middle of the night, owing to the decrement of industrial and household loads. In this study, the supply power for charging the PEVs is modeled as a sinusoidal function incorporated with an additional random component, which is in accordance with existing assumed models [ 11 , 18 , 27 ]. This mathematical model may also enable us to easily perform some parametric studies on the supply power for charging the PEVs, by adjusting its amplitude and period or by transforming its coordinate.

If the measured data of the supply power for charging PEVs is obtained, the supply power model could be replaced to a realistic raw-data model for industrial and real applications. In the following section, different ratios of the supply power from the feeder to the residential loads are considered and discussed in more detail. Three simulation cases will be analyzed for both the SCSC and the proposed TBRC based on different possibilities of the residual capacity owing to the correlation between the residential loads and the supply power.

In the first simulation case there is a balanced demand between the residential loads and PEV loads within the network supply. Hence, the first case describes a scenario where the total supply of the power distribution system meets the total demand of the residential loads and the PEV loads. The second possible simulation is conducted when the residential loads increase and the supply power exclusive of the residential loads is less than the PEV load requirements.

This simulation case is not suitable to establish the policy of the power distribution system, but we would like to evaluate the feasibility of PEV charging algorithms as a worst case. Finally, a low residential load scenario is also considered. There is extra supply power delivered to PEVs than required. This section formulates a simulation model with a distribution network and load data broadly based on the previous researches in [ 16 , 27 ]. N c h and T c h are the number of charging time-steps and the charging time of each charging time-step used for the simulation, relatively.

As a total of 32 time-steps for 8 h of the charging period are considered from 10 p. The average total demand energy for the charging of the PEVs [kWh] can be represented by. For simulation purposes, the average power supply for charging the PEVs is calculated by the multiplication of different weight factors and the PEV charging demand for the three scenarios. From Equation 1 , the average supply energy to charge the PEVs kWh which is subtraction between the supply energy from the feeder and the residential load is calculated by. In Section 2. Thus, the supply power to charge the PEVs is represented by.

The trend component of the supply power for charging the PEVs is represented by. Therefore, all these constant values are determined by the statistical values of the supply power for charging the PEVs and are not instantaneously changed. If the statistical characteristics of the PEV charging system may be varied according to seasonal or monthly changes, all constant values of the supply power for charging the PEVs are updated periodically.

The maximum number of PEVs charged per time-step based on the statistical information of the PEV charging network can be represented by. Compared with the performance of the proposed threshold based charging scheme, in this section, the SCSC [ 27 ] based on the assumptions is considered for focusing on the maximization of the PEV charging performance without considering the computational complexity of the central controller. The instantaneous total demand energy for the PEV charging is represented by. The instantaneous demand power for charging the PEVs during the t -th charging time-slot under the network constraints, P D E V t , can be expressed as.

If the demand power for charging the PEVs does not exceed the maximum supply power of the network, all the PEVs that are plugged into the power distribution network will be charged. In the central controller, PEVs that are served during this charging time-slot are determined by an objective function [ 27 ]. The objective function is decided under network constraints to achieve the fairness of PEV charging system, and is represented by.

From 13 , the central controller decides the PEVs that will be served during a charging time-slot to optimize the PEV charging system. With such a process, the network is coordinated to ensure utilization to its fullest extent in terms of the delivered energy. Under these conditions, in the beginning of every charging time-slot, the BSOCs of all the PEVs are updated and provided to the central controller, repeatedly.

In accordance with the condition of the power network such as sudden occurrences of the residual load, the charging PEVs do not fully receive the prearranged charging power in practical. The flow chart of the charging process of the SCSC for each charging time-slot is presented in Figure 1. Operational flow of the smart charging system with cooperation SCSC for each time-step.