Frankfurt (Germany), 6-9 June 2011 W.Du – the Netherlands – Session 5 – 1225 MODELLING...

Frankfurt (Germany), 6-9 June 2011

W.Du – the Netherlands – Session 5 – 1225

MODELLING ELECTRIC VEHICLES AT RESIDENTIAL LOW VOLTAGE GRID BY MONTE CARLO SIMULATION

W.Du

TU DelftThe Netherlands



Problem, Objective and Methodology Base Model (By Monte-Carlo Simulation) An Example of Simulated Low Voltage Grid EV Appliance and EV Scenarios DNOs’ Pricing Strategies Model Results, Validation and Conclusion


Structure



Problem and Objective

Problem: Uncertain EVs’ impacts on low voltage electricity grid

Objectives: Supporting DNOs in grid asset capacity planning Finding out more accurately probabilities of possible

overloads caused by EVs Analyzing DNOs’ pricing strategy in influencing EV

charging behaviors



Method

Monte Carlo Simulation(Base Household behaviors)

DNOs’ Pricing Planning strategies

EV Scenarios

· charging patterns· penetration degrees

Capacity



Base Model (Monte-Carlo Simulation)

Model represents regular household behaviors of using electrical appliances

Stochastically generate load profiles more realistic then using aggreated and

deterministic simulatainty factor especially true for low voltage grid

Aiming at finding more accurate the impacts of having EVs at households



Base Model (Monte-Carlo Simulation)-Cont.

Household Types (HTs) Eight HTs by size, age and working status Percentages for HTs are based on Dutch statistic data

Electrical appliances Commonly used 25 types in Dutch households are stored Information including highest power, duration of use time Penetration degrees based on available statistics, market data

and related researches

Usage of appliances Three types distinguished in relation to use time: constant use;

instant use and semi-constant use Probability distributions are pre-defined in power usage,

frequency to be used and duration time of each use.



An Example of Simulated Residential Grid



EV Appliances

Treated as household appliances Type: instant use

Only full battery-engined EV is considered Attributes:

Power usage:in triangular distribution (unit :watt) Duration of charging:in triangular distribution (time

unit : minute) penetration degree: in percentage frequency of being charged in different charging

periods:in triangular distribution (unit : integer)



EVs Scenarios Starting Time:

Start charging immediately after arriving at home Charging after 11:00pm

Charging Rate: low rate of 3kW and high rate of 10kW.

Charging Time EV efficiency of 5km/kWh is assumed EVs are assumed to be charged at households until full Distance driven pre-defined based on Dutch statistics

Penetration Degrees 0.1 to 1 in step of 0.1



DNOs’ Pricing Strategy

Shifting Charging Time Shifted to after 11:00pm from 10% of households onward up to 100% in

step of 10% Reducing Charging Time

Reduced in 10% to 50% of previous settings



Simulation Results

Highlighted EVs’ charging load in a single household load profile



Simulation Results – Cont.

Aggregation of transformer loads



Simulation Results – Cont.

Aggregated peak load and probabilities of overloads



Validation

Two Steps: Validation of the base model Validation of EVs’ charging profiles

Data Comparition Aggregations of household loads will be

compared with empirical transformer loads provided by Enexis B.V., NL

The EVs’ charging results will be validated with real sampled data at individual households



Conclusion

Modelling EVs as household appliances by a Monte-Carlo simulation

Aiming at analysing EVs’ charging impacts on residential low voltage grid capacity

Charging scenarios are generated stochastically with different penetration degrees and charging patterns

DNOs’ pricing strategies also are estimated in their influences on EVs’ charging patterns



Thank you for your attentions!

Frankfurt (Germany), 6-9 June 2011 W.Du – the Netherlands – Session 5 – 1225 MODELLING...

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