* motivation * objective find out proper fairness schemes/metrics to meet the customers’ needs to...

* Smart Grid and Electric Vehicles

Instructor: Nicholas F. Maxemchuk

Members: Yingjie ZhouChen Wang

Xiangying Qian

*Motivation & Objective

*Motivation

*Objective Find out proper fairness schemes/metrics to meet the customers’ needs to a great extent.

Customer : various energy requirement & desired departure time

Available Energy :limited for some time

Fairly distribute energy?

*Model

*Use Switch to control power supply (5 min interval)*Information : Energy requirement, desired departure time, battery level, energy available *Cars come to charge in a queue with Poisson dist..*Each 5 min can only charge limited cars.*Different fairness schemes : Decide who will be charged, update charging queue for every 5 min. *When energy requirement is satisfied, remove the car from the queue.

*Baseline System

*Round Robin

Charged Not ChargedNew Arrivals

Beginning of the queueBeginning of the queue

*Fairness Scheme 1

* Min-Max Energy Requirement (MMER) * Only need the info. of Energy Requirement

8 units 10 units 5 units

Beginning of the queue

*Fairness Scheme 2

*Min-Max Delay Time (MMDT) *New metric:

N (spare time)= (desired departure time-current time)/5

-Units of energy required

*First charge those cars with smallest N

Current timeDesired

departure time

Units of energy required Spare time-N

*Simulation Environment

Parameters Data /Distribution

Arrival of EV’s Poisson Distribution Arrival rate -> real world data

Desired Plug-in time Gaussian distribution [6, 22]: Mean(μ)=14 hrs, StDev(σ)=4 hrs

Desired Departure time Arrival time + Desired Plug-in time

*Simulation Environment

Parameters Data /Distribution

Desired distance Fitting real world data to Exponential dist. [20, 90]

R (Supply and demand ratio)

=

Energy Available Function of energy consumption (real world data) and R

Current battery level (%) Uniform dist. 0%-30% of full battery level

Energy RequirementCalculated from desired distance100 mile = 28 kwh 1 kwh = 6.67 Units of 5 min

Full battery level = 100 miles= 186.7 Units of 5 min

*3 Metrics of evaluation:*Fraction of delayed vehicles:=*Average delay for delayed vehicles: =*Average delay for all vehicles: =

*Simulation Environment

*Measurement

*Run each fairness scheme for successive (n=10) days.

*Take the measurements from the day to ensure stable initializations.

*Take the measurements till day to ensure the departure of all cars by the end of day.

*For comparison, FCFS, FDFS

*Contribution

*MMER fairness scheme does not work well in terms of the metrics we applied because it takes no advantage of the information of departure time. It performs even worse than the baseline Round Robin.*MMDT fairness scheme generally achieves the best performance.*To ensure 95% cars departing without delay, Round Robin: R > 1.8 MMDT: R < 1.1

*Result - 1

*MMER shows the worst performance in comparison to other charging schemes.

*Result - 1

*MMER shows the worst performance in comparison to other charging schemes.

zoom out

*Result - 1

*MMER shows the worst performance in comparison to other charging schemes.

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*Result - 1

*MMER shows the worst performance in comparison to other charging schemes.

*Result - 2

*Compare Round Robin with two schemes which take the information of departure time into account.

*It turns out that using the additional information can improve the performance.

*Result - 2

*Compare Round Robin with two schemes which take the information of departure time into account.

*It turns out that using the additional information can improve the performance.

*Result - 2

*Compare Round Robin with two schemes which take the information of departure time into account.

*It turns out that using the additional information can improve the performance.

*Result - 3

*In comparison to FDFS, MMDT fairness scheme achieves better performance when the available power is a little bit more than the required energy.

*Result - 3

*In comparison to FDFS, MMDT fairness scheme achieves better performance when the available power is a little bit more than the required energy.

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*Result - 3

*In comparison to FDFS, MMDT fairness scheme achieves better performance when the available power is a little bit more than the required energy.

*Example

Car A (Unit of 5 minute) Car B (Unit of 5 minute) Car A Car BRequired Energy Desired Depart Time - Current Time Required Energy Desired Depart Time - Current Time MMDTMetric3.5 3 2 2 -0.5 02.5 2 2 1 -0.5 -12.5 1 1 0 -1.5 -1

Car A (Unit of 5 minute) Car B (Unit of 5 minute)Required Energy Desired Depart Time - Current Time Required Energy Desired Depart Time - Current Time3.5 3 2 23.5 2 1 13.5 1 0 0

FDFS:

*Result - 3

*MMDT fairness scheme achieves the best performance when the available power is a little bit more than the required energy.

*Results from Uniform Distribution

*Discussion of Lies

*1. Lie about Time (2 methods)

*a. punish with extra time b. punished by fineDay Claim Actuall

yPunish Desired departure

time

1 8am 10am (10-8)+1=3 8am

2 7am 10am (10-7)+2=5 10am

3 5am 10am (10-5)+3=8 10am

4 2am 10am (10-2)+4=12

10am

5 ……..

*2. Lie about Distance (2 methods)

*a. punish with extra time (convert distance to time)

1 mile = 10 min

*b. punished by fine

Week Tasks1, 2, (Jan 30 – Feb 10) Background reading and literature

review. √

3,4, (Feb 13 – Feb 24) Simulate a baseline charging system. √

5, 6 (Feb 27 – Mar 9)8, (Mar 19 – Mar 23)

Implement different types of fairness; Each of us is responsible for one specific fairness scheme. √

9,10, 11 (Mar 26 – Apr 13) Compare the results and make some conclusions. Discuss about lies. √

12,13, (Apr 16 – Apr 27) Write a technical paper.

Contract

*Thank you!