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: michaelng@emsd.gov.hk

Project Investigator: Prof. Eric ChengDepartment of Electrical EngineeringThe Hong Kong Polytechnic UniversityHung HomHong KongTel: +852-9669 1628Email: eeecheng@polyu.edu.hk

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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