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Zhang Qian
State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University, Chongqing, People's Republic of China Chongqing China

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Original research paper
Published: 22 March 2021 in IET Smart Grid
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The disordered charging of electric vehicles (EVs) may result in transmission congestion during the peak load period; therefore, a nodal dynamic charging price (NDCP) strategy is proposed to schedule the charging activity of regional electric vehicle agents (REVAs) to manage congestion. REVA is responsible for meeting the charging demand of users at a minimum charging cost, while the grid aims at minimising the congestion management cost and regulates the charging price of different nodes. The Stackelberg game theory is used to model the negotiation process of charging price, and the congestion contribution index is proposed to determine the congestion cost that the user side should be allocated. The proposed approach is implemented on a modified IEEE 30 bus system, and the results show that the congestion caused by EVs is reduced effectively, while the charging cost and congestion cost are also optimised.

ACS Style

Qian Zhang; Tao Sun; Zhuwei Ding; Chunyan Li. Nodal dynamic charging price formulation for electric vehicle through the Stackelberg game considering grid congestion. IET Smart Grid 2021, 1 .

AMA Style

Qian Zhang, Tao Sun, Zhuwei Ding, Chunyan Li. Nodal dynamic charging price formulation for electric vehicle through the Stackelberg game considering grid congestion. IET Smart Grid. 2021; ():1.

Chicago/Turabian Style

Qian Zhang; Tao Sun; Zhuwei Ding; Chunyan Li. 2021. "Nodal dynamic charging price formulation for electric vehicle through the Stackelberg game considering grid congestion." IET Smart Grid , no. : 1.

Research article
Published: 10 February 2021 in IET Renewable Power Generation
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In engineering practice, the Voronoi diagram is often used to plan electric vehicle (EV) fast-charging stations but it does not consider the spatio-temporal distribution of EV trip. Research on the spatio-temporal distribution of EVs mainly uses the shortest path method to reduce the amount of iterative calculations. The influence of the external environment and traffic congestion on the travel of EVs has not been considered, which results in the inaccuracy of the EV charging demand. To solve the above problems, this study proposes a method for predicting the spatio-temporal distribution of EVs based on quasi-dynamic traffic flow. This method takes into account the external environment and the impact of traffic congestions on EV trips and balances the problem between simulation accuracy and calculation efficiency. Based on this, the particle swarm optimisation algorithm is used to optimise the travel cost of the EVs and the cost of the construction and operation of the charging infrastructure. An optimal siting and sizing model for the fast-charging station based on quasi-dynamic traffic flow is established. Simulation results verify the effectiveness of the model.

ACS Style

Zhang Qian; Zhu Yi; Wang Zhong; Hu Yue; Su Yaojia. Siting and sizing of electric vehicle fast-charging station based on quasi-dynamic traffic flow. IET Renewable Power Generation 2021, 14, 4204 -4214.

AMA Style

Zhang Qian, Zhu Yi, Wang Zhong, Hu Yue, Su Yaojia. Siting and sizing of electric vehicle fast-charging station based on quasi-dynamic traffic flow. IET Renewable Power Generation. 2021; 14 (19):4204-4214.

Chicago/Turabian Style

Zhang Qian; Zhu Yi; Wang Zhong; Hu Yue; Su Yaojia. 2021. "Siting and sizing of electric vehicle fast-charging station based on quasi-dynamic traffic flow." IET Renewable Power Generation 14, no. 19: 4204-4214.

Journal article
Published: 07 May 2020 in Applied Sciences
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In order to solve the problem that the static peak-valley price for electric vehicles cannot truly reflect the relationship between electricity supply and demand, as well as the fact that the low utilization rate of renewable energy in the micro-grid, a dynamic time-of-use pricing strategy for electric vehicle charging considering user satisfaction degree is proposed, to achieve the goal of friendly charging for the micro-grid. Firstly, this paper researches the travel patterns of electric vehicles to establish the grid connection scenes and predict the controllable capacity of electric vehicles. Secondly, the charging preferences of different types of users are studied, and a comprehensive satisfaction degree model is set up to obtain different users’ charging strategies. Furthermore, the paper raises a pricing strategy on account of the dispatching requirements of the micro-grid, and realizes the effective dispatch of electric vehicle charging load based on price signals. Finally, we gain the dynamic time-of-use charging price, using the strategy proposed above, and the economic benefits brought to the micro-grid and electric vehicle users are analyzed, which validates the rationality and effectiveness of the pricing strategy.

ACS Style

Qian Zhang; Yue Hu; Weiyu Tan; Chunyan Li; Zhuwei Ding. Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree. Applied Sciences 2020, 10, 3247 .

AMA Style

Qian Zhang, Yue Hu, Weiyu Tan, Chunyan Li, Zhuwei Ding. Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree. Applied Sciences. 2020; 10 (9):3247.

Chicago/Turabian Style

Qian Zhang; Yue Hu; Weiyu Tan; Chunyan Li; Zhuwei Ding. 2020. "Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree." Applied Sciences 10, no. 9: 3247.

Journal article
Published: 18 March 2020 in IEEE Transactions on Industry Applications
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As with rapid development of the electric vehicle (EV) industry, charging infrastructures are built fast. However, the unreasonable deployments with increasing EVs contribute to a long queuing time for charging demand of EVs, especially in the peak hours. How to navigate a specific EV to economically satisfy its charging demand, while relieve the traffic burden, is an urgent problem. To address that, a price incentive based charging navigation strategy for electric vehicles is proposed. Unlike previous charging navigation studies that mainly focus on the EVs-transportation-power systems modeling, it considers the spatial-temporal influence of EVs' charging decision, especially the simultaneous charging requests. Specifically, the charging navigation framework with the collaborative working mode of EV- charging station-information exchange center (IEC)-intelligent transportation system (ITS) is established first. Following this, spatio-temporal distribution of the charging demand is obtained through the origin-destination (OD) analysis. After this, an event-driven dynamic queue model is constructed. It contributes to the modeling of the charging strategy, together with the proposed reservation opportunity cost mechanism. Finally, the simulation results indicate that the presented charging navigation strategy cannot only reduce the EV's charging cost but also improve the utilization rate of charging facilities, which verify its effectiveness.

ACS Style

Xuecheng Li; Yue Xiang; Lin Lyu; Chenlin Ji; Qian Zhang; Fei Teng; Youbo Liu. Price Incentive-Based Charging Navigation Strategy for Electric Vehicles. IEEE Transactions on Industry Applications 2020, 56, 5762 -5774.

AMA Style

Xuecheng Li, Yue Xiang, Lin Lyu, Chenlin Ji, Qian Zhang, Fei Teng, Youbo Liu. Price Incentive-Based Charging Navigation Strategy for Electric Vehicles. IEEE Transactions on Industry Applications. 2020; 56 (5):5762-5774.

Chicago/Turabian Style

Xuecheng Li; Yue Xiang; Lin Lyu; Chenlin Ji; Qian Zhang; Fei Teng; Youbo Liu. 2020. "Price Incentive-Based Charging Navigation Strategy for Electric Vehicles." IEEE Transactions on Industry Applications 56, no. 5: 5762-5774.

Journal article
Published: 19 February 2020 in IEEE Transactions on Industry Applications
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With the penetration rate of distributed generator and distributed energy storage growing, the frequency stability of micro-grid is severely affected. In this paper, a self-adaptive secondary frequency regulation (FR) strategy based on virtual synchronous generator (VSG) for a micro-grid containing wind turbine, photovoltaic array, and electric vehicle cluster is proposed. First, modified VSG control is applied to renewable energy units and electric vehicle cluster, where non-error frequency regulation is realized by introducing proportional-integral control into power-frequency controller of VSGs. Secondly, the FR reserve capacity of wind turbine, photovoltaic array, and electric vehicle are predicted in real time through their respective characteristics. Then, a self-adaptive adjustment strategy for secondary FR parameters of VSGs is designed to control the FR power of VSGs according to real-time predicted FR capacity. Finally, the coordinated FR strategy of VSGs and conventional unit is proposed considering the situation of insufficient FR capacity of VSGs. Through the analysis of simulation results, the reasonability and validity of proposed self-adaptive FR strategy for multiple VSGs have been verified.

ACS Style

Qian Zhang; Yan Li; Zhuwei Ding; Wenrui Xie; Chunyan Li. Self-Adaptive Secondary Frequency Regulation Strategy of Micro-Grid With Multiple Virtual Synchronous Generators. IEEE Transactions on Industry Applications 2020, 56, 6007 -6018.

AMA Style

Qian Zhang, Yan Li, Zhuwei Ding, Wenrui Xie, Chunyan Li. Self-Adaptive Secondary Frequency Regulation Strategy of Micro-Grid With Multiple Virtual Synchronous Generators. IEEE Transactions on Industry Applications. 2020; 56 (5):6007-6018.

Chicago/Turabian Style

Qian Zhang; Yan Li; Zhuwei Ding; Wenrui Xie; Chunyan Li. 2020. "Self-Adaptive Secondary Frequency Regulation Strategy of Micro-Grid With Multiple Virtual Synchronous Generators." IEEE Transactions on Industry Applications 56, no. 5: 6007-6018.

Research article
Published: 16 January 2020 in IET Generation, Transmission & Distribution
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The electricity price plays an important role in stimulating electric vehicles (EVs) to participate in the power grid's scheduling. It is necessary to formulate a reasonable discharging price for the power grid and electric vehicles. Here, a negotiation strategy of electric vehicles participating in optimal scheduling under the multi-agent situation which aims to formulate reasonable discharging price is proposed, and then a two-stage negotiation model considering multiple agents is established. First, a charging and discharging optimisation scheduling model considering EV travel characteristics is proposed, based on which the bidding limits of the power grid and EV agents are calculated. Then, the negotiation process is divided into two stages. In the first stage, all negotiators offer tentative bidding; in the second stage, negotiators will adjust their bidding based on learning other negotiators and the discharging price is obtained finally. In numerical cases, the proposed negotiation model is proved to be effective in balancing benefits of power grid and electric vehicles as well as peak load shifting.

ACS Style

Qian Zhang; Zhuwei Ding; Weiyu Tan; Wenrui Xie; Yan Li. Negotiation strategy of discharging price between power grid and electric vehicles considering multi‐agent. IET Generation, Transmission & Distribution 2020, 14, 833 -844.

AMA Style

Qian Zhang, Zhuwei Ding, Weiyu Tan, Wenrui Xie, Yan Li. Negotiation strategy of discharging price between power grid and electric vehicles considering multi‐agent. IET Generation, Transmission & Distribution. 2020; 14 (5):833-844.

Chicago/Turabian Style

Qian Zhang; Zhuwei Ding; Weiyu Tan; Wenrui Xie; Yan Li. 2020. "Negotiation strategy of discharging price between power grid and electric vehicles considering multi‐agent." IET Generation, Transmission & Distribution 14, no. 5: 833-844.

Journal article
Published: 10 September 2019 in IEEE Access
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With the increasing number of electric vehicles and the emergence of vehicle-to-grid technology, electric vehicles have become distributed loads and power sources with random movement characteristics in the distribution network. In order to evaluate the reliability of the distribution network incorporating electric vehicles, firstly, this paper uses the trip chain theory to describe the travel of electric vehicles. Based on the static traffic flow distribution, a high efficiency quasi-dynamic travel simulation method is proposed to consider the influence of traffic congestion on the path selection. Then, the simulation time advancement of the Monte Carlo method is improved, which makes the reliability assessment of the distribution network containing a large amount of electric vehicles’ charging and discharging behaviors realizable. Finally, the practicality of the method is verified by the modified IEEE-RBTS Bus-6 test system. The effects of electric vehicles penetration, discharging threshold, and battery capacity on reliability of both distribution networks and electric vehicles are studied.

ACS Style

Qian Zhang; Yi Zhu; Zhong Wang; Yaojia Su; Chunyan Li. Reliability Assessment of Distribution Network and Electric Vehicle Considering Quasi-Dynamic Traffic Flow and Vehicle-to-Grid. IEEE Access 2019, 7, 131201 -131213.

AMA Style

Qian Zhang, Yi Zhu, Zhong Wang, Yaojia Su, Chunyan Li. Reliability Assessment of Distribution Network and Electric Vehicle Considering Quasi-Dynamic Traffic Flow and Vehicle-to-Grid. IEEE Access. 2019; 7 (99):131201-131213.

Chicago/Turabian Style

Qian Zhang; Yi Zhu; Zhong Wang; Yaojia Su; Chunyan Li. 2019. "Reliability Assessment of Distribution Network and Electric Vehicle Considering Quasi-Dynamic Traffic Flow and Vehicle-to-Grid." IEEE Access 7, no. 99: 131201-131213.

Journal article
Published: 14 July 2019 in Applied Sciences
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Wind turbines can participate in frequency regulation by controlling active power output, but the indeterminacy and volatility of wind power result in low reliability of frequency support. Therefore, as a kind of energy storage system, an electric vehicle is adopted to coordinate with wind turbines to regulate system frequency considering its large-scale development. First, based on the reasonable division of wind speed regions and operation point selection of pitch angle, the de-loading strategy of doubly-fed induction generator for reserve capacity under continuously varying wind speed is proposed. Then, through the combination of rotor speed and pitch angle control, frequency regulation model of a doubly-fed induction generator in whole wind speed range is established. Finally, taking into account the driving demand of electric vehicle owners, through the real-time allocation of system frequency regulation task based on frequency regulation capacity, the coordinated control strategy of doubly-fed induction generator and electric vehicle cluster for secondary frequency regulation is put forward. The simulation results show that the coordinated frequency regulation strategy based on real-time allocation can suppress frequency deviation effectively, and the regulation effect is better than the situations of wind turbine coordinating with the conventional unit or coordinating with electric vehicle cluster based on fixed allocation ratio.

ACS Style

Qian Zhang; Yan Li; Chen Li; Zhuwei Ding; Wenrui Xie. Coordinated Secondary Frequency Regulation Strategy of Doubly-Fed Induction Generator and Electric Vehicle. Applied Sciences 2019, 9, 2815 .

AMA Style

Qian Zhang, Yan Li, Chen Li, Zhuwei Ding, Wenrui Xie. Coordinated Secondary Frequency Regulation Strategy of Doubly-Fed Induction Generator and Electric Vehicle. Applied Sciences. 2019; 9 (14):2815.

Chicago/Turabian Style

Qian Zhang; Yan Li; Chen Li; Zhuwei Ding; Wenrui Xie. 2019. "Coordinated Secondary Frequency Regulation Strategy of Doubly-Fed Induction Generator and Electric Vehicle." Applied Sciences 9, no. 14: 2815.

Research article
Published: 23 May 2019 in Transactions of the Institute of Measurement and Control
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The time-varying characteristics of electric vehicle (EV) controllable energy and the rationality of frequency regulation (FR) demand power allocation have significant influences on participating in system FR. Combined with the state transition characteristics of EVs, the calculation models of real-time controllable quantities and real-time controllable energy of EVs are established. Then, considering the dynamic changes of EVs’ controllable energy, the system FR strategy with real-time adjusting scheme of FR coefficients is put forward. Finally, based on the unit participation time contribution, the selecting strategy for individual EVs to participate in FR is proposed. The simulation results show that based on the calculation of EVs’ real-time controllable energy, the proposed load frequency control model with real-time allocation of FR demand power suppresses the frequency deviation effectively, and the private electric car is found to have the most potential for the FR system.

ACS Style

Qian Zhang; Yan Li; Chen Li; Chun-Yan Li. Real-time adjustment of load frequency control based on controllable energy of electric vehicles. Transactions of the Institute of Measurement and Control 2019, 42, 42 -54.

AMA Style

Qian Zhang, Yan Li, Chen Li, Chun-Yan Li. Real-time adjustment of load frequency control based on controllable energy of electric vehicles. Transactions of the Institute of Measurement and Control. 2019; 42 (1):42-54.

Chicago/Turabian Style

Qian Zhang; Yan Li; Chen Li; Chun-Yan Li. 2019. "Real-time adjustment of load frequency control based on controllable energy of electric vehicles." Transactions of the Institute of Measurement and Control 42, no. 1: 42-54.

Journal article
Published: 01 October 2018 in Electric Power Systems Research
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The precondition for electric vehicles (EVs) to be involved in the system frequency regulation (FR) is that the owner’s driving demand can be satisfied, which is the key factor in promoting vehicle owners to participate in system FR service. At present, many studies have proposed FR control strategy, but most of them are confined to the fleet level of EVs and ignore the individual level. And the individual demand of EV in FR process includes both satisfying the energy demand and reducing the harm to storage battery caused by the conversion between charge and discharge state. Accordingly, this paper firstly expounds the demand declaration strategy of EVs. Based on the declared information, the grouping strategy of EVs connected to the grid is proposed. Then the FR control strategy at the level of individual EV is elaborated. At last, the load frequency control (LFC) model considering the driving demand of EV is established. The simulation results show that the proposed control strategy can effectively satisfy the energy demand of individual EVs participating in FR, control the number of conversions between charging and discharging state, and achieve the aim of stabilizing system frequency fluctuation.

ACS Style

Qian Zhang; Yan Li; Chen Li; Chunyan Li. Grid frequency regulation strategy considering individual driving demand of electric vehicle. Electric Power Systems Research 2018, 163, 38 -48.

AMA Style

Qian Zhang, Yan Li, Chen Li, Chunyan Li. Grid frequency regulation strategy considering individual driving demand of electric vehicle. Electric Power Systems Research. 2018; 163 ():38-48.

Chicago/Turabian Style

Qian Zhang; Yan Li; Chen Li; Chunyan Li. 2018. "Grid frequency regulation strategy considering individual driving demand of electric vehicle." Electric Power Systems Research 163, no. : 38-48.

Conference paper
Published: 05 September 2018 in Communications in Computer and Information Science
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The time-varying characteristics of electric vehicle (EV) controllable energy and the rationality of frequency regulation (FR) task allocation have significant influences on participating in system FR. Considering the various types of EV, the controllable quantities and the real-time controllable energy of EVs are simulated and calculated. On the basis of the real-time changes of EV’s controllable energy, the real-time allocating scheme of system FR coefficient is put forward. The simulation results show that the real-time adjustment of load frequency control (LFC) model based on dynamic controllable energy of EV can effectively suppress the system frequency deviation; under the same total battery energy, the electric private car participates in the system FR for the longest time, the real-time controllable energy of which is the largest; the real-time allocating ratio of system FR has a wide range of fluctuation, and the FR effect under real-time allocation mode is better than that under the fixed allocation mode.

ACS Style

Yan Li; Qian Zhang; Chen Li; Chunyan Li. Real-Time Adjustment of Load Frequency Control Based on Controllable Energy of Electric Vehicles. Communications in Computer and Information Science 2018, 105 -115.

AMA Style

Yan Li, Qian Zhang, Chen Li, Chunyan Li. Real-Time Adjustment of Load Frequency Control Based on Controllable Energy of Electric Vehicles. Communications in Computer and Information Science. 2018; ():105-115.

Chicago/Turabian Style

Yan Li; Qian Zhang; Chen Li; Chunyan Li. 2018. "Real-Time Adjustment of Load Frequency Control Based on Controllable Energy of Electric Vehicles." Communications in Computer and Information Science , no. : 105-115.

Conference paper
Published: 05 September 2018 in Programmieren für Ingenieure und Naturwissenschaftler
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In the research of Vehicle-to-grid (V2G), the large-scale EVs needs to be aggregated to participate in the charging/discharging strategy. Aiming at the problem of electric vehicles (EVs) in aggregator participating in dispatching plan, this paper proposed an orderly scheduling strategy of the EV in the aggregator (Vehicle-to-Aggregator, V2A). Under the condition of real-time price, an index evaluation system of the EVs has been established, which consider the randomness and credit of EV users. This paper first analyzed the impact of the declaration information on the schedule plan. With the consideration of declared scheduling capacity, EV user’s credit, battery loss and the degree of participation as the evaluation index, the evaluation index system of the EV aggregator is proposed. Then, the weight of each index is determined, which uses the method of combination weighting based on the Accelerated Genetic Algorithm. Then the scheduling priority of EVs in the aggregator can be obtained. Finally, combining with the dispatching plan of power grid, the actual scheduling capacity of aggregators at different nodes in each period is determined. The simulation results show that the strategy proposed in this paper can consider the influence of various indexes of EVs on schedule, and effectively realize the dispatching plan for aggregators.

ACS Style

Wenrui Xie; Qian Zhang; Huazhen Liu; Yi Zhu. An Orderly Charging and Discharging Scheduling Strategy of Electric Vehicles Considering Demand Responsiveness. Programmieren für Ingenieure und Naturwissenschaftler 2018, 116 -126.

AMA Style

Wenrui Xie, Qian Zhang, Huazhen Liu, Yi Zhu. An Orderly Charging and Discharging Scheduling Strategy of Electric Vehicles Considering Demand Responsiveness. Programmieren für Ingenieure und Naturwissenschaftler. 2018; ():116-126.

Chicago/Turabian Style

Wenrui Xie; Qian Zhang; Huazhen Liu; Yi Zhu. 2018. "An Orderly Charging and Discharging Scheduling Strategy of Electric Vehicles Considering Demand Responsiveness." Programmieren für Ingenieure und Naturwissenschaftler , no. : 116-126.

Article
Published: 21 May 2018 in IET Generation, Transmission & Distribution
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To stimulate the participation of electric vehicles (EVs) in vehicle-to-grid (V2G) activities, some economic incentives should be offered to the EV owners and the discharging price is negotiated by EV aggregator and electricity grid. Here, this study proposes a negotiation strategy between EV aggregator and electricity grid which focuses on how to develop a reasonable mechanism for discharging price, and then the bilateral negotiation function models of discharging price based on fuzzy Bayesian learning are established. In the models, the certain parameters are calculated according to the profits and cost of the EV aggregator and electricity grid; and the fuzzy probability calculation method is formulated to estimate and calculate the uncertain parameters of the functions of both sides, respectively. Additionally, the negotiation function models based on fuzzy Bayesian learning is utilised for updating and correcting the deviation of estimates and the discharging price is finally found out by the parameters above. Through numerical cases, the negotiation strategy proposed in this study is verified to be effective in the early promotion of V2G.

ACS Style

Qian Zhang; Weiyu Tan; Jiajia Cai; Zhong Wang; Chunyan Li. Negotiation strategy for discharging price of EVs based on fuzzy Bayesian learning. IET Generation, Transmission & Distribution 2018, 12, 4396 -4406.

AMA Style

Qian Zhang, Weiyu Tan, Jiajia Cai, Zhong Wang, Chunyan Li. Negotiation strategy for discharging price of EVs based on fuzzy Bayesian learning. IET Generation, Transmission & Distribution. 2018; 12 (20):4396-4406.

Chicago/Turabian Style

Qian Zhang; Weiyu Tan; Jiajia Cai; Zhong Wang; Chunyan Li. 2018. "Negotiation strategy for discharging price of EVs based on fuzzy Bayesian learning." IET Generation, Transmission & Distribution 12, no. 20: 4396-4406.

Article
Published: 30 January 2018 in IEEJ Transactions on Electrical and Electronic Engineering
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Aiming at the problems related to the dispatch, decomposition, and coordination, temporal and spatial distribution, and the randomness of electric vehicles (EVs), this paper establishes a hierarchical dispatch model between EVs and the power grid to achieve energy conservation and economy optimization. The model is composed of two layers. In the upper layer, considering spatial and temporal distribution characteristics, a bi-level economic dispatch model based on traditional unit commitment for EV aggregators is established. Then, on the account of the priority considering the convenience of EV users, the lower layer proposes the charging and discharging scheduling model for aggregators. In this layer, an evaluation index system is established by analyzing the impact of EVs' declared information, which includes the declared capacity and charging duration of EVs, the sincerity degree, and the battery loss, to determine the priority of scheduling for each EV based on the entropy method. Finally, the simulation is carried out on a four-bus micro-grid system. It shows that the hierarchical dispatch model could not only obtain the dispatch plan for aggregators but also divide the strategy results of regional power grid to each EV, to determine the optimal scheduling plan for charging and discharging of the EV aggregator in each period.

ACS Style

Qian Zhang; Huazhen Liu; Chen Li. A hierarchical dispatch model for optimizing real-time charging and discharging strategy of electric vehicles. IEEJ Transactions on Electrical and Electronic Engineering 2018, 13, 537 -548.

AMA Style

Qian Zhang, Huazhen Liu, Chen Li. A hierarchical dispatch model for optimizing real-time charging and discharging strategy of electric vehicles. IEEJ Transactions on Electrical and Electronic Engineering. 2018; 13 (4):537-548.

Chicago/Turabian Style

Qian Zhang; Huazhen Liu; Chen Li. 2018. "A hierarchical dispatch model for optimizing real-time charging and discharging strategy of electric vehicles." IEEJ Transactions on Electrical and Electronic Engineering 13, no. 4: 537-548.

Journal article
Published: 01 January 2018 in International Journal of Information and Education Technology
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ACS Style

Guangjin Peng; Zhihong Fu; Qian Zhang; Chunyan Li. Design and Practice of EMI Teaching Method in Electric Network Principle Course. International Journal of Information and Education Technology 2018, 8, 223 -226.

AMA Style

Guangjin Peng, Zhihong Fu, Qian Zhang, Chunyan Li. Design and Practice of EMI Teaching Method in Electric Network Principle Course. International Journal of Information and Education Technology. 2018; 8 (3):223-226.

Chicago/Turabian Style

Guangjin Peng; Zhihong Fu; Qian Zhang; Chunyan Li. 2018. "Design and Practice of EMI Teaching Method in Electric Network Principle Course." International Journal of Information and Education Technology 8, no. 3: 223-226.