Abstract:A new concept of schedulable ability(SA)is proposed to represent the ability of dynamic and two-way energy balance for electric vehicles(EVs)in a certain distribution network, with a priority-based intraday scheduling optimization model for EVs built. Firstly, the SA model is formulated to analyze SA of EVs quantitatively, which takes battery loss, customer's credit and discharging capability of EVs into account. Secondly, based on an analytic model of SA and two-stage supply-demand coordinated optimization method for EVs, the optimal threshold value of SA for EVs is obtained to minimize the peak-valley difference of distribution system load considering customer's driving habit. Finally, the scheduling priority of EVs is determined by numerical comparison between its SA assessment result and the optimal threshold value of SA, thus creating the priority-based intraday scheduling strategy for EVs on the event-driven basis. A certain residential district is taken as an example to show the rationality and effectiveness of the proposed optimization model. This work is supported by National Natural Science Foundation of China(No. 51507153)and Zhejiang Provincial Natural Science Foundation of China(No. LY16E070005).

Abstract:A route selection and charging navigation optimization model for electric vehicles(EVs)is presented for reducing traveling costs of EV users and improving the security of the distribution system concerned. With the aid of crowd sensing, a traffic information matrix acquisition method based on the matrix factorization technique is first addressed. The formulated objective of the presented model is to minimize the sum of driving and waiting costs, fast and regular EV charging costs based on the time of use(TOU)price mechanism, subject to a variety of technical constraints such as path selections, time, battery capacities, and charging or discharging state mutual exclusion constraints. A sample system is built by connecting four EV charging stations to the IEEE 33-bus distribution system, and the four EV charging stations are within a city center within a 25 km×25 km zone, and served for demonstrating the essential feature of the presented method. With respect to the situations with and without real-time traffic information, the impacts of the EV quantities participating in crowd sensing on the travel route and charging/discharging of EVs, as well as on the distribution system are also analyzed.

Abstract:This paper presents a real-time optimized dispatching model for large-scale cluster electric vehicles(EVs)to achieve the charging demand and safe operation of distribution network. For each new optimization scenario, the accessed EVs can cluster according to their desired completion time. The entire dispatching process for charging/discharging strategy can be divided into two steps, i. e. , the upper dispatching based on grey wolf optimization(GWO)algorithm for each cluster, and the bottom layer based on energy buffer consensus for each EV in the corresponding cluster. Simulation results demonstrate that the proposed model can significantly facilitate the real-time large-scale optimal dispatching of EVs, while GWO algorithm and energy buffer consensus are suitable to solve large-scale optimal dispatching with superior performance on availability and convergence rate. This work is supported by National Basic Research Program of China(973 Program)(No. 2013CB228205)and National Natural Science Foundation of China(No. 51477055).

Abstract:Large scale charging load of electric vehicles will increase the degree of the deviation between node voltage and nominal voltage in a local distribution network, and the deviation which caused by the charging load is random on time and place, traditional reactive compensation method can solve the above problem, but the economy is bad. So a voltage control strategy based on reactive power compensation by electric vehicles is proposed, which aims at implementing reactive power compensation for distribution network and changing the charging active power at the same time through regulating the operating power factor of the chargers effectively. The strategy can ensure the security and stability of the node voltage and the efficiency of distribution network will not decrease. A strategy is proposed to calculate the controllable scope of operating power factors of chargers after analyzing the travel behavior characteristic in a working area of first-tier/second-tier city in China and the operating characteristic of charger. Then, the node voltage regulation model is used which considers all operating power factors of chargers as variables and considers the following conditions as the optimization objective: the deviation between node voltage and nominal voltage is minimum, the variance of the node voltage from current time to all time before is minimum, the state of charge charged in at unit duration is maximum. Finally, immune algorithm is used to get the values of variables. The rationality of the proposed node voltage regulation strategy can be validated through the simulation. This work is supported by National Natural Science Foundation of China(No. 51677004)and Fundamental Research Funds for the Central Universities(No. 2016JBM061).

Abstract:This paper researches the optimal electric vehicle charging and navigation strategy from the following four aspects: electric vehicle user, electric power company, charging station operators and traffic management department. It puts forward “vehicle-grid-road-station” multi-objective optimization and orderly charging navigation system. Based on the system, this paper establishes integrated optimal model by fuzzy decision method, which achieves the highest user's income, the lowest voltage deviation and network loss, the highest load balancing index, the highest profit for charging station operators, and the lowest traffic congestion. With nine charging stations that evenly distributed in the traffic network and IEEE 14-bus system as the test cases, optimization results indicate that the smart charging service strategy is reasonable and effective.

Abstract:Because of the good storage characteristics and controllability of battery, in the premise of meeting the changing needs, the operators of battery swapping station for electric vehicles can respond to the incentive measures from the power dispatching department, and meet the system operating demands. Firstly, according to the state parameters of the battery packs in a battery swapping station, the state matrix of battery packs is established. Secondly, the battery packs clustered in the light of status, and the battery packs, which are in controllable state, are controlled. Finally, recursive thought is used to study controllable load margin boundaries of backup batteries in the battery swapping station, and then the battery swapping station operators are guided to draw up corresponding charging plans based on the operation target of power dispatching department, reaching the purpose of balancing load and improving area load characteristics. This work is supported by National Natural Science Foundation of China(No. 51477116).

Abstract:With the increasing penetration of electric vehicles(EVs), the disordered charge and discharge behaviors bring negative impact on normal operation of power grid. Vehicle-to-grid(V2G)is an excellent solution for this problem. V2G mode based on microgrid is used for energy exchange between EVs and power grid, and multi-objective optimization model, in which minimum grid load fluctuation, maximum renewable energy utilization and maximum owners gain are taken as the optimization objectives, is established. In order to solve the optimization model, variable threshold optimization algorithm is proposed firstly, and an improved algorithm named variable charge and discharge rate optimization algorithm is proposed. The optimization results show that the two proposed algorithms could increase utilization of renewable energy, improve the imbalance between power supply and demand in the microgrid and increase benefits of EV owners through reasonable scheduling.

Abstract:The security and economic operation of the power system is impacted or even challenged by the disordered charging of large-scale electric vehicles, especially for the distribution system. Aiming at the defects of centralized optimization and control method and the fixed pricing strategy, a decentralized optimization strategy of ordered charge scheduling is proposed based on the Lagrange relaxation method. The traditional centralized charging optimization problem of electric vehicle charging station is decomposed into N sub problems(N is the number of electric vehicles demanding charging). The maximum profits of the charging station are used as the objective function in the optimization model. The constraints of power demand of users, charging time, capacity of transformer and time-of-use(TOU)price of charging station are taken into consideration. In order to verify the effectiveness of the proposed method, Monte Carlo method is adopted to simulate the charging demand of electric vehicles. The profits of charging station, the effect of clipping load peak and the calculation efficiency under the fixed price and TOU price strategy of the charging station in situations of ordered charging and disordered charging when adopting centralized and decentralized optimization respectively are simulated and analyzed. The simulation results show that the proposed method can significantly increase the profits compared to use the disordered charging and fixed price strategy. By contrast with the centralized optimization, the computational efficiency of the proposed decentralized optimization is much higher. Though the charging station using TOU price has the effect of filling valley, the effect is not too ideal in stabilizing the load fluctuation.

Abstract:The intermittent and randomness of distributed generations and charging power of electric vehicles bring new challenges to the distribution network reconfiguration. In this context, an interval number method based dynamic reconfiguration strategy considering electric vehicles and distributed generation is proposed. This paper introduces the uncertainty of the distribution power supply and the charging load of electric vehicles. The objective function to minimize network loss by interval number is proposed. Meanwhile, the dynamic time division of dynamic loss parameters is come up with and a mathematical model of dynamic reconfiguration of distribution network is established. The flow equation is solved by using Krawczyk-Moore interval iteration method as well as an affine multiplication and division operation is introduced to replace the interval multiplication, which help to solve the problem of interval arithmetic. Finally, the optimization method for the model above is proposed by combining neighborhood search and clonal selection algorithm, in order to realize dynamic reconfiguration of active distribution network with multiple uncertainties. The analysis of the example shows that the proposed method is superior to the traditional artificial intelligence algorithm.

Abstract:Considering the stochastic charging behaviors of electric vehicles(EVs), a software simulation is conducted to simulate the driving patterns of users. Based on the virtual battery aggregation model, a novel double-layer charging control strategy for EV is developed. By synthesizing the strength of the two mainstream control strategies, i. e. centralized control strategy and decentralized control strategy, the charging control is divided into two levels with different optimal objectives. For the upper control layer, the global interests are taken into consideration, and peak load, node voltage and overall electricity cost are adopted as the global optimal objectives. For the bottom layer, in order to consider the high penetrations of EVs, the scalable and flexible alternating direction method of multipliers(ADMM)algorithm is deployed, which blends the benefits of dual decomposition and augmented Lagrangian methods to solve the optimal problem. To verify the effectiveness of the proposed method, four scenarios are simulated and their effects on node voltage, line load, and daily load curve are compared and analyzed. The results indicate that under the proposed charging control strategy, higher node voltage, lower line load and smoother load curve can be achieved.

Abstract:For the two levels of two-way charging machine, a kind of charge/discharge control strategy is proposed based on the virtual synchronous machine(VSM)in microgrid. First of all, the VSM is applied to control the first-level AC/DC converter, while introducing the virtual inertia, damping and excitation to make the charging machine possess the same operation characteristics as the synchronous machine. Secondly, an auxiliary frequency modulation algorithm is designed by using the active power-frequency operation curve to calculate the “correction of the active power reference” and change the power reference, which makes electrical vehicles participate in the primary and secondary frequency regulation of microgrid. Based on the MATLAB/Simulink, the microgrid model with electrical vehicles is developed. The results show that electric vehicles controlled by the proposed strategy can transmit electricity to the power system, which supports the inertia, damping, voltage and frequency, realizes the non-differential regulation of frequency in microgrid at independent mode, and improves the dynamic stability of microgrid.

Abstract:A random selection method of starting time for electric vehicle is proposed. The control decision generator obtains electricity price and load information of current control area, divides valley period into several parts, and calculates the load capacity in each period considering the load curve. Charging pile calculates the probability distribution of the start charging moment corresponding to its user with different charging durations, and randomly determines the start charging moment based on generated probability distribution. In order to balance the utilization of valley power resources, the proposed method quantifies the load capacity of the grid as probability distribution during the valley period, and randomly generates the charging task according to the load capacity of each period. The charging task can be independently decided by the charging pile without complicated centralized communication control. The Monte Carlo simulation results show that the proposed strategy can effectively realize peak load shifting and stabilize the load fluctuation, and the system and user costs can also be reduced.

Abstract:As one of potential operating reserve measures with comparative capacity in future power systems, the operation of electric vehicles(EVs)should follow their users' willingness in travel and charge. Therefore, it is only reasonable to analyze the capability of EV as a reserve measure under a suitable market mechanism in which users' willingness is respected. A charging/discharging contract principle is designed to balance the needs between the system operator and the users. An algorithm to calculate the short-term reserve capability of EV, as well as a sorted charging/discharging strategy to respond the variation of electricity price, is proposed. Based on these models or methods, case studies are done aiming to give the primary assessment on capability to supply reserve capacity/energy instantly but with short duration from a typical individual EV or EV clusters under different charging/discharging strategies. Furthermore, parameter influences e. g. the price and design features of a reserve market on the reserve capability of EV are also analyzed.

Abstract:A reasonable layout of charging facilities is crucial to electric vehicles(EVs)due to the endurance mileage deficiency. Travel behaviors of EVs are simulated based on the historical trajectories from the automatic number plate recognition(ANPR)data. Thus, by giving an allocation of charging stations, charging behaviors of EVs are simulated using the typical pattern of charging choices based on their trip characteristics. The charging demand and cost of EVs and the profit of operators can be obtained. Consequently, reasonable locations and capacities of charging stations for an optimized layout are determined by minimizing the sum of the charging-trip ratio(CTR)and the cost-benefit ratio(CBR). Furthermore, they should be restricted by user psychology and the capacity of power grid. A case study(including 151 signal intersections)is given to demonstrate the effectiveness of optimization method. Moreover, the influence of workdays/weekends, the increase of traffic demand and the development of battery on the allocation is analyzed.

Abstract:The comprehensiveness, rapidness and economy of charging service is one of main factors that influences the popularity of electric vehicles, and the construction and management of charging facilities in a residential area play a key role. Because of the limited capacity and other reasons, the development of charging facilities in the residential area falls behind the other forms of charging energy. Based on the practical charging conditions of a typical residential area, the influence of possible technical/market measures on the battery electric vehicle(BEV)charging process is discussed according to the detailed simulation of charging process for BEVs and its effect and value are quantitatively analyzed. On the basis of defining BEV charging contract model, the analysis mentioned above is put forward to propose an optimization method according to the charging strategy based on the optimal value network and the algorithm is verified to be effective by the examples. The simulation results indicate that on the condition of limited capacity, compared to the existing disordered charging mode, the ordered charging/discharging mode under the centralized management can obviously increase the capacity of charging service in the typical residential area by improving the efficiency inside. Moreover, the interactive capacity of emergency backup for the grid by BEV is improved and the revenue channels and comprehensive benefits increase as well.

Abstract:In view of the rapid growth of electric vehicle(EV)ownership in megacities in China and the contradiction between the EV charging demand and the lower distribution network service ability, an orderly EV charging strategy based on technique for order preference by similarity to ideal solution(TOPSIS)is proposed to dispatch the available power energy resources to the utmost extent. By analyzing EV travel laws, a mathematical model is built to simulate the traveling, returning, waiting and charging process of EVs in residential areas. An orderly charging method based on TOPSIS analysis is proposed considering comprehensively the decision indicators such as charging time, charging cost, queuing time, charging completion rate, and the overall load variation rate of residential areas. The simulations further compare the impact of peak-valley price on users' charging behavior under the time-of-use price strategy. Simulation results show that, compared with the grid picking method, the proposed TOPSIS based orderly charging strategy can better consider the continuity, convenience and economy of EV charging, and provide a decision basis for the orderly charging management of EVs parking in residential areas under the premise of the high completion rate of EV charging and the stability of residential load.

Abstract:Contrapose the spatial-temporal transfer random of electric vehicles, temperature and traffic conditions are taken into consideration, and a spatial-temporal distribution prediction method of charging load for urban electric vehicle based on Markov decision process(MDP)random path simulation is proposed. Firstly, the types of electric vehicles are classified according to their spatial transfer and charging characteristics. Secondly, the real-time dynamic random path simulation based on Monte Carlo method and Markov decision theory are used to establish the spatial-temporal transfer model of various electric vehicles. The model of temperature and traffic energy is established according to the measured data of electric vehicles, and the energy consumption is obtained accordingly. Finally, taking a typical urban area as an example, the charging load is calculated under different temperatures and different traffic conditions. The simulation results show that the charging load of the nodes with fast charging in the region is higher, and ambient temperature rise or deterioration of traffic congestion can lead to an increase in charging load.