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PolyU Technology & ConsultancyCompany Limited 理 大 科 技 及 顧 問 有 限 公 司
The Hong Kong Polytechnic University
Subject: Coordinated Charging of Electric Vehicles in Charging Station
Collaborator: Mr. Michael K.L. NGTel: 3757 6019Email: [email protected]
Project Investigator: Prof. Eric ChengDepartment of Electrical EngineeringThe Hong Kong Polytechnic UniversityHung HomHong KongTel: +852-9669 1628Email: [email protected]
1. Introduction
More and more electric vehicles are used in recent years. The operation of power grid are
faced with new challenges brought by the large-scale charging load of EVs. The massive
charging load of EVs are connected to power grid at the same time, which causes the
voltage instability of power grid, or other issues. The problem is caused by disorderly
charging of EVs. Therefore, a smart charging system should be developed to avoid
disorderly charging and realize coordinated charging of EVs.
2. The proposed works
It is proposed to develop a smart EV charging system with dynamic load and time
management to realize coordinated charging of electric vehicles in charging stations.
Therefore, a strategy and method of coordinated charging will be developed based on the
available capacity of power grid and users’ charging needs. The project works will be
developed in the following:
1) Develop an optimal model of coordinated charging of EVs.
2) Model dynamic load of EVs based on predicting charging needs of EVs
3) Develop operation strategy of coordinated charging in smart charging station
4) Develop simulation program to verify the optimal model and algorithm
5) Develop a prototype of a smart EV charging
6) Build a test platform for debugging test
7) Field test and refinement
3. The scope of service
The proposed works are described in the following:
3.1 Develop an optimal model of coordinated charging of EVs.
1) Propose an objective function
The massive penetration of EVs into the power grid can cause the voltage instability of
power gird. Uncontrolled EV charging will lead to potential cost at both generation and
transmission sides. Development of model of coordinated charging is aimed to reduce
charging energy at the high load of power grid and increase charging energy at the low load
of power grid, which stabilize power grid voltage. Therefore, to charging EVs at the valley
of power grid as much as possible, minimizing the sum of squares of load curve of this
charging station is chosen as the objective function.
(1)
(2)
Where F is the sum of squares of load curve;
T is the optimization period;
PL+EV(t) is the sum of residential load and EV charging load;
PEV_i(t) is the charging load of the i EV;
Pavg is the average of total load.
2) Define constraints of objective function
The objective function is subject to constraints to ensure the power within limits and user’s
needs. All assumed power cannot exceed total power available in the charging station. And
charging arrange should meet the needs of users that include expected SOC of battery after
charging and expected departure time. After defining constraints, the parameters of
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constraints shall be determined from power grid of charging station and needs of users of
EV.
3.2 Model dynamic load of EVs based on predicting charging needs of EVs
The pattern of habit of users driving will affect the operation of strategy of coordinated
charging. The charging load may be dynamic. The controller of coordinated charging has
the function that can predict charging needs of potential EV users. The prediction can be
conducted based on the history data of users charging. For a certain scale of charging
station, a normal distribution can be used as a reference to model the load of EVs. The
randomness of pattern of users driving shall be considered, therefore, the model or
parameters of load of EVs will be changed with the change of pattern of users driving.
3.3 Develop operation strategy of coordinated charging in smart charging station
The aim of developing operation strategy is to flatten the total grid load over time. The
operation strategy collects the information of needs of EV users and information of power
grid and calculate charging power allocation that is charging time allocated to EV users. In
order to meet the needs of EV users and the limits of power grid, the valley of power grid
shall be flattened as much as possible. The operation strategy of coordinated charging as
shown in Fig.1.
Both particle swarm optimization and genetic algorithm will be developed to conduct the
optimization operation based on the model of coordinated charging. The optimization
algorithm is aimed to minimize the value of objective function by iterative calculation.
Optimization algorithm can calculate the optimal charging time to users based on the load
capacity of power grid and users’ needs.
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Fig.1 Strategy of coordinated charging in smart charging station
3.4 Develop simulation program to verify the optimal model and algorithm
A simulation program will be developed to verify the optimal model and algorithm. The
simulation will be conducted under tow strategies that are charging without control
algorithm and coordinated charging proposed algorithm. The simulation results will give
out two the load curves of power grid for comparison. Based on the simulation results, the
optimal model and algorithm will be improved for optimizing load of power grid.
An example of residential load is shown in Fig.2. Load under disorderly charge is plotted
with blue curve as shown in Fig.3. And load under ordinated charging is potted with green
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curve as shown in Fig.4. The valley of load curve of power grid is flattened, which is the
object of this project.
Fig.2 Residential load
Fig.3 Load under disorderly charging
Fig.4 Load under coordinated charging
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3.5 Develop a prototype of a smart EV charging
Based on the algorithm of coordinated charging and design of smart charger, a prototype of
a smart EV charging will be developed. The block diagram of the smart EV charging is
shown in Fig.5. Controller of coordinated charging communicates with power distribution
centre to obtain the curve of power load and power grid capacity. And it also
communicates with charger to obtain the needs of EV users and arrival time of EV. Based
on these information, controller of coordinated charging calculate the optimal charging
time. It allocates the charging time of EVs by controlling on/off of power switch.
Based on the simulation results, the control strategy of coordinated charging is
programmed in the controller. The function description of coordinated charging system is
shown in Fig.6.
Fig.5 Block diagram of coordinated charging system
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Fig.6 Function description of coordinated charging system
3.6 Build a test platform for debugging test
A test platform for debugging test is built to simulate the operating condition of EV
charging station. The test platform supplies the operating condition in which the prototype
of EV smart charging is tested for debugging.
3.7 Test on site and refinement
The coordinated charging system will be tested in the laboratory and then to be tested on
site. It is expected that EMSD will provide a charger station for us to test.
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