When charging and discharging priority use the

When the electric vehicles participate in regionaldistribution network for peak power regulation, it must bearranged in corresponding region scheduling scheme. TheV2G forecasting is necessary, which include prediction of thenumber of vehicle participated in V2G service, the totalavailable capacity, the available power in each time. Accurateforecast the parameters of V2G is the basic elements of realizethe functions of V2G. It is because of V2G power withtwo-way interaction and the user interaction, so the predictionof V2G available capacity is more complex than the traditionalload forecasting. From technology to analyze, V2G storagecapacity can be acquired through the following two ways.egin{enumerate} item { extit{ EVs user settings}}:  It means that the vehicle owners set next week program of participate V2G vehicle in the weekend, then the V2G station control system can count the V2G available capacity in next week. In the actual implementation of charging and discharging priority use the plan capacity. The model is similar to the contract market and real-time market in power market and has good promotion prospects. item { extit{ V2G capacity}}: V2G capacity is closely related with the amount of participated vehicle, user’s hobbit, weather, energy price, season and the economic development. In the initial of V2G service, relatively few vehicles involved, so can adopt the method that discharge all the vehicle at the peak power time and charge in low power time. As technology and markets mature, the number of vehicle participate the V2G will increase, and the historical data will gradually accumulate. Then these predict methods can be applied in V2G capacity forecast, such as the time series method, exponential smoothing method, gray model method, Kalman filtering method, and some artificial intelligence methods such as expert systems, artificial neural networks, fuzzy prediction method etc. end{enumerate}subsection {Smart Charging Coordination}The interaction between EV and charging station is well-defined in international electro technical commission IEC 61851.  The IEC 61851 standard will facilitate the communication from the charging station to the EV.  Due to the incomplete the EV communication protocols standard, it is hardly applied in the current available vehicles and charging stations. Currently, the smart charge stations that are working with AC, can provide very inadequate communication. For instance, increased the number of electric vehicles and charging stations are anticipated to be increase continually. Therefore, V2G features such as data scheduling will become very important to achieve stable power grid. The communication standard implemented by the utility, normally is power line communication (PLC) that will facilitate a possible technical solution for vehicle-to-grid communication.The control signal can facilitate a two-way communication and is produced by the EV supply equipment (EVSE). The available current of the charging station will be based on the average voltage can be measured by getting the variable duty cycle and high voltage. Pulse width modulation (PWM) signals are used for a wide variety of control signal with a frequency of 1 kHz. The smart vehicle charger should not exceed the maximum of charge current. The EV has the ability to change the voltage level of the controller to signalize the vehicles charging statues. Apart from above-mentioned EV charging planning phase, the charging station is also responsible for checking the status of smart grid frequently. It has to make a proper decision when the power grid condition is not stable. For example, if the EV charging station started to schedule the charge for EV, during the charging process if the grid operation system condition changed and not stable. Hence, the EV charger station will take into their account the status of the power system and start to decreases the rat of charging gradually till the power grid status back to normal. If the power grid condition is continuous in dangers the charging station will stop charging completely.subsection {V2G of Peak Power Shifting} Distributed energy storage entities of V2G can facilitate as a supplement of the smart grid to release the pressure from the power resource and increase the system reliability. Practically, the vehicles owners are on average parked and EV stay idle for almost 3 to 5 hours a day in the parking lots. Throughout this time, the battery in of EVs can performance as distributed energy storage unit of power system. Thus, the battery storage can fed the power back to the main grid when the energy power supply is inadequate. V2G can deliver an actual revenue for the load regulation in the distribution system because the V2G charging stations access the power grid in the distribution network side. Implementing EVs as an energy storage component to shift the regional load is resulting in speed the regulation, high efficiency, and economic benefits. The response time of the EV charge/discharge procedure can be completed in milliseconds, as it does not include any mechanical equipment’s. section {Energy Flow Optimization}Smart grid operator systems adopting flexible EVs charging scenarios to tackle the potential pressure due to additional load demand of EVs connected with the power grid. Motivated by the challenge, this article presented new operational algorithm that enable the EVs battery charge and discharge for connected grid support of active power, as shown in Fig.3. Uncertainties associated with EVs’ SOC status and departure time are taken into consideration through proposing aggregator controller to manage the random energy available in the grid. However, the technique proposed here is based on allocating each EV a charging priority, called scored-priority (SCR), the calculation of which includes reflexion of the power grid, vehicle, battery, aggregator controller, and charger. In order to allow public parking lots to get benefit from smart charging for end-user DR, a framework for energy flow is developed in which the aggregator controller can manage the decision-making using real-time interactions with EV owners. This method is based on a calculation of the techniques in which EV charging can help real-time proficient energy delivery and phase-unbalance mitigation in a three phase LV system.The optimal battery capacity of the grid connected system can be designed under two cases. The battery discharging process under each scenarios is considered. The electricity imports from the grid is measured as grid more than zero and the electricity export to the grid is considered as grid less than zero. Battery charging process is measured as battery DC more than 0 and battery discharging is measured as battery DC less than 0. The energy dispatch scheduling of the battery storage will due to increase the income by push the stored energy for supplying the loads throughout peak hours of the day when the cost of electricity is high to purchase low electricity priced from the main grid during off peak hours at night to charge the battery for using it during the peak hours of the day.egin{figure*} includegraphicswidth= extwidth{Fig3} caption{The structure of the decision making algorithm.}end{figure*}egin{figure}!htb egin{center} includegraphicsscale=0.42{Fig4} caption{Single line diagram of modified IEEE 13 node with parking lots } %label{fig:Fig1} end{center}end{figure}section {Case Study}A simplified model of typical distribution system with different parking lots has been used. The voltage on the output of the secondary transformer is set at 1.06 p.u. In the network, each load bus is connected with a set of EVs and transformers connect to each phase of each node.  The minimum allowed voltage at each load bus is considered to be 4 kV. Each EV connected with aggregator controller that is capable of receiving and responding to energy load from the power grid. Also, this energy demand is strongly influenced by factors such as weather, thermostat settings, and other objectives behaviour. Each EV in the parking lot can deliver measured data message to the aggregator controller including the initial and the final need of SOC, and the departure required time of each EV.egin{figure*} includegraphicswidth= extwidth{Fig5} caption{Average Transformer-Level Power Output}end{figure*}The structural model of distribution energy grid consists of the distribution feeder, step down transformers, energy storage, and number of houses. Energy storage is located at the end of the feeder in the power grid network. The distribution model of residential has been model with tool GridLAB-D by using modified IEEE 13 node. GridLAB-D is released as an open source  power system simulation tool environment to a limited group of charter developers in December 2007. This release is developed by Pacific Northwest national laboratory (PNNL) with the funding of the department of energy (DOE).Single line diagram of modified IEEE 13 node test feeder %cite{IEEE} is shown in Fig. 4. In this power grid system the primary voltage of the feeder is considered as 33KV and the secondary voltage is 2.4KV or 10 kV distribution feeder.  The power rate for the substation transformer is 5MVA. The step-down transformer connects to one of the IEEE 13 nodes. The primary voltage for this type of transformer is 2.4KV while the secondary voltage is 120V. The 2.5 km feeder to the primary substation supplies LV networks. The transformer power rate is assumed 5KVA for each EV that is connected to a transformer. The parking lots in the distribution grid is considered around 150 EVs randomly distributed among the nodes of the IEEE 13 node feeder in way of each 5 to 9 EV connect to a step-down transformer. Sample of output voltage at Bus-4 and Bus-5  are shown in Fig.5. In this analysis, the operation of each technology combination was simulated in a sequential hourly dispatch model. The technical specifications of the technology constrained the operations of the modelled storage device, accounting for charging and discharging capacity (in kW), energy storage capacity (kW-h), round-trip efficiency, and minimum depth of discharge, among other factors. Within those constraints, each energy storage device was dispatched based on expected load demand to maximize.subsection{EV Charging Controller Analysis}Battery energy storage operation condition is subject to the frequency responses of power system. The BES data rate output is the amount of the load, which consider the total output of natural gas and of photovoltaic (PV) subtracted. The battery is assumed to be operated under the range of state of charge (SOC) from 30\% to 80\%. The battery stops discharging when SOC is below 30\%. Battery also stops charging when SOC is over 80\%. The maximum state capacity of charge which is the upper limit of the battery are $90\%$ and the minimum state capacity of charge is the lower limit $10\%$ of the battery. However, the storage is turned off when the reaches $SOC_{max}$, $SOC_{min}$ the storage can be discharged only to supply the load and the storage can be charged by an excess production. To protect the electrolytic storage from overcharge and over discharge, two limits are given as $P{bt+}$ is the storage charge power of the battery, and$P{bt-}$ is the storage discharge power of the battery. The maximum allowable charge and discharge current must be less than $10\%$ of the battery Ampere-Hour (AH) capacity. The use of energy storage can improve the grid network, that can led to increase the permissible penetration of PV in LV distribution networks. In addition, considering of a variable load demand into the energy storage will result in very lower increases in the battery state of charge (SOC) than can notes if the battery is charged at a constant rate, as shown in Fig. 6. The resulting of energy storage period through the summer season can be full cycling time during sunny days of summer. However, the predominant  of low charging during the night-time. egin{figure}!htb egin{center} includegraphicsscale=0.53{Fig6} caption{Battery state of charge } %label{fig:Fig1} end{center}end{figure}To demonstrate the effect of coordinated EV charging in the distribution grid, we have developed the fair sharing algorithm to mitigate the peak load of the EV that are connected to the grid. We have simulated our EVs model for different  penetration rates (0\%, 10\%, 20\%, 30\%, 40\%, and 50\%). 150 EVs were randomly distributed in the parking lots for each penetration rate. The analysis results of different EV penetration rate is illustrated in table I. According to the data analysis the  50\% and 60\% penetration rate of transformers are fully loaded by EV charging and the maximum time duration is about 4 hours. The data aggregated time during the fully loaded EV charger is more than 190 hours for 50\% penetration. The long time for fully loaded charger EV charger can help to reduce the transformer life-time. The EV charging effect can be mitigated by coordinating the EV charging. By increasing the penetration rate, the number of overloaded of parking lots transformers increases exponentially during evening, when all the EVs arrive.% Please add the following required packages to your document preamble:% usepackage{graphicx} egin{table*}t centering caption{TRANSFOMER OVERLOAD DATA} label{my-label}
esizebox{ extwidth}{!}{% egin{tabular}{|l|l|l|l|} hline EV Penetration & Overloaded Transformers & Maximum Duration (min) & Aggregated overloading time (min) \ hline 0\%            & 0\%                     & 0                      & 0                                 \ hline 10\%           & 4\%                     & 133                    & 477                               \ hline 20\%           & 11\%                    & 181                    & 1122                              \ hline 30\%           & 25\%                    & 234                    & 4744                              \ hline 40\%           & 50\%                    & 290                    & 9022                             \ hline 50\%           & 60\%                    & 321                    & 12896                             \ hline end{tabular}% }end{table*}section{Conclusions}The growing demand for energy demand in smart grid applications is ambitious by robust industrial based trends. In this article, insight some of energy optimization option through smart electric vehicle technology options and change/discharge pattern that can be utilized by energy industry sectors. As a case study of the presented energy optimization, this article has considered the demand-side management of charging/discharge process in the smart grid applications of the EV charging network. Thus, the present analysis was an interactive approach to realize DR approach by incorporating aggregated EVs into public smart parking lots, whereby an aggregator controller facilitate various opportunities based on the energy load in the power grid. The incorporated solutions and reliability are required to maintain robust smart operation of grid services. These challenges will support new research paradigm and stretch the next generation of grid developments that need to be considered due to the unique belongings of V2G technique.