Ordered Electric Vehicles Charging Scheduling Algorithm Based on Bidding in Residential Area

Round 1

Reviewer 1 Report

The problem described in the manuscript is interesting, however the methods of priority based charging and time slot allocation have been investigated in many papers. The manuscript should make a thorough literature review and describe clearly what are the novelty and contributions of the manuscript.

Author Response

Dear Referee,

Thank you very much for your positive works. We are also highly grateful for the feedback, which helped us to improve the quality of the paper. Accordingly, we have modified the paper by taking into consideration all the changes requested by you. We take it as a positive gesture that you have submitted positive remarks about the paper.

Thank you very much, indeed!

The revised paper has been re-written by taking into account all these recommendations. The changes are catalogued below. We also highlight the recommended changes suggested by you in the manuscript.

We have, thus, replied to yours comments one-by-one, and indicated clearly what has changed.

 

The problem described in the manuscript is interesting, however the methods of priority based charging and time slot allocation have been investigated in many papers. The manuscript should make a thorough literature review and describe clearly what are the novelty and contributions of the manuscript.

 

Response: We thank you for the valuable suggestion.

In the revised version of the manuscript, we have added the review of some new literatures about EV charging methods and the novelty and contributions of the manuscript in the introduction section as you recommended.

 

Reference [8] puts forward a kind of charging of battery life strategy, and realize the rapid and flexible charging the battery and combines with the battery charged state (state of charge, SOC) according to the temperature characteristics of the lithium iron phosphate battery, voltage characteristics. Reference [9] presents a new technique for smart charging of EVs, which uses a fuzzy logic to control and manage the EV charging process to maximize electric utility and EV owner benefits. Reference [10] presents a multi-objective optimization algorithm based coordinated EVs charging strategy, which considers both user level and system level benefits simultaneously. Reference [11] presents various strategies for coordinating the charging/discharging of PHEVs in electric parking with V2G capability and the aim of increasing parking profits. Reference [12] presents a method which electric vehicles charge and discharge orderly containing unit running cost and effectiveness of power system operation by using the multi-objective optimization genetic algorithm. Reference [13] proposes a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging.

 

Based on the above analysis, we mainly study the problem that how to adopt the user bidding in the residential area to distinguish the users’ charging requirements and allocate the corresponding charging time slot. Meanwhile, the electric vehicle alternating strategy to enhance the charging flexibility and the utilization of time slot is needed when users leave halfway through the charging time slot or new charging requirements with higher bidding appear. Therefore, our contribution is to present a scheduling algorithm of ordered charging for residential EVs based on users bidding. User charging priority is developed according to the user bidding, and the corresponding charging time slots are allocated for users based on the idea of game competition. Meanwhile, the alternating strategy is designed with the consideration of the halfway leaving of users and new charging requirement with higher bidding during the charging time slot.

 

 

With gratitude,

Corresponding author

Reviewer 2 Report

This paper is good approche for optimization the charging of electrical vehicule. The algorithme are simple quite fast for real time implementation. My question is about the performance of this algorithme in comparaison with the simple algorithme like

Charging car base on the arrival time (first in first out)  Charging the most priority car first

The number of car in the example are some how too small for real application, another numerial study case shoud be considered.

The novelty of this approach could be the bidding system with two priorities groups. It is good contribution for scientific community. It could help to make new design of energy management system for EV parking. But for scientific and English quality, I think there are a the improvement to do: Line 71 electricity per hour => do author mean energy ? Line 73 The percentage of residual electric power => state of charge of EV ? Section 3: problem model => problem modelling or problem formulation Line 98 : k is not defined Sometime k is reference as user (line 106) and sometime as period (line 129). That is confusing. Author should define clearly a nomenclature of index and mathematical definition. A equation 11, the period is again present by t not k Line 217 : Percentage of remaining battery => State of charge of energy storage Line 251: The charging time of EVs in cell is concentrated => I don't understand this phase Line 252: the arrival and departure of users are very random => are random (very random doesn't make sense) Line 256: We use the game idea => we use the serious game concept

 

 

 

 

Author Response

Dear Referee,

Thank you very much for your positive works. We are also highly grateful for the feedback, which helped us to improve the quality of the paper. Accordingly, we have modified the paper by taking into consideration all the changes requested by you. We take it as a positive gesture that you have submitted positive remarks about the paper.

Thank you very much, indeed!

The revised paper has been re-written by taking into account all these recommendations. The changes are catalogued below. We also highlight the recommended changes suggested by you in the manuscript.

We have, thus, replied to yours comments one-by-one, and indicated clearly what has changed.

 

 

This paper is good approche for optimization the charging of electrical vehicle. The algorithme are simple quite fast for real time implementation. My question is about the performance of this algorithme in comparaison with the simple algorithme like Charging car base on the arrival time (first in first out)  Charging the most priority car first. The number of car in the example are some how too small for real application, another numerial study case shoud be considered.

 

Response: Thanks for pointing this out.

We highly agree with your consideration of the number of cars in the example. Indeed, it is some how too small for real application. However, in our research and example, we found that the performance of different charging scheduling algorithms is relatively obvious difference when the number of cars in the charging system is relatively small. Once the phenomenon of charging queuing appears frequently which means the number of cars in the charging system is very large (approaching even exceeding the capacity of charging system), the performance of different charging scheduling algorithms is close to each other. Therefore, we choose a relatively small number of cars in the example with the consideration of actual capacity of charging system in the residential area to precisely evaluate the performance of our algorithm. Of course, in our future work, we will continue to research and verify the performance of our algorithm under the condition that the phenomenon of charging queuing appears frequently in the charging system in the residential area.

 

The novelty of this approach could be the bidding system with two priorities groups. It is good contribution for scientific community. It could help to make new design of energy management system for EV parking.

 

Response: We thank you for the affirmation.

 

But for scientific and English quality, I think there are a the improvement to do: Line 71 electricity per hour => do author mean energy ? Line 73 The percentage of residual electric power => state of charge of EV ? Section 3: problem model => problem modelling or problem formulation Line 98 : k is not defined Sometime k is reference as user (line 106) and sometime as period (line 129). That is confusing. Author should define clearly a nomenclature of index and mathematical definition. A equation 11, the period is again present by t not k Line 217 : Percentage of remaining battery => State of charge of energy storage Line 251: The charging time of EVs in cell is concentrated => I don't understand this phase Line 252: the arrival and departure of users are very random => are random (very random doesn't make sense) Line 256: We use the game idea => we use the serious game concept

 

Response: Thanks for pointing this out.

As you suggested, we have modified the paper in the revised version of the manuscript. We have carefully checked the presentation of the paper and modified the grammar errors and the confusing sentences.

“electricity per hour” in Line 71 has been modified as “the real-time price in each hour”.

“The percentage of residual electric power” in Line 73 has been modified as “the SOC”.

“problem model” in Section 3 has been modified as “Problem modelling”.

k in Line 98 is defined as the index of time slot. We modified the corresponding index in Line 106, Line 129. We also modified the equation 3 and 11.

“Percentage of remaining battery” in Line 217 has been modified as “SOC”.

“The charging time of EVs in cell is concentrated” in Line 251 has been modified as “The statistic show that the charging behaviors usually happen in a relatively concentrated time”.

“the arrival and departure of users are very random” in Line 252 has been modified as “the arrival and departure of users are random”.

“We use the game idea” in Line 256 has been modified as “We use the serious game concept”.

 

 

With gratitude,

Corresponding author