grid impact analysis of a residential microgrid under various ev penetration … · 2014-09-17 ·...

Center for Embedded Computer Systems University of California, Irvine ____________________________________________________

Grid Impact Analysis of a Residential Microgrid under Various

EV Penetration Rates in GridLAB-D

Fereidoun Ahourai, Mohammad Abdullah Al Faruque

Center for Embedded Computer Systems

University of California, Irvine

Irvine, CA 92697-2620, USA

{fahourai, mohammad.alfaruque} @uci.edu

CECS Technical Report 13-08

July 30, 2013

II

Grid Impact Analysis of a Residential Microgrid under Various

EV Penetration Rates in GridLAB-D

Fereidoun Ahourai, Mohammad Abdullah Al Faruque

{fahourai, mohammad.alfaruque}@uci.edu

This work is part of the project “Smart Grid Capable Electric Vehicle Supply

Equipment (EVSE) for Residential Applications”, which was conducted for the

Department of Energy Office of Electricity Delivery and Energy Reliability

under Contract DE-OE0000587. The content of the report does not necessarily

reflect the position or policy of the Government; no official endorsement should

be inferred.

III

Contents

1 Introduction 1

2 Residential Microgrid Modeling 3

2.1 Model Structure ............................................................................................................................. 3 2.2 Configuration of the Microgrid Model ........................................................................................... 3 2.2.1 Configuration of the Triplex Line Object ....................................................................................... 3 2.2.2 Configuration of the Transformer Object ...................................................................................... 2 2.2.3 Configuration of the Triplex Meter Object .................................................................................... 2 2.2.4 Configuration of the Residential House Object ............................................................................. 2 2.2.5 Configuration of the EV Object ...................................................................................................... 4 2.2.6 Configuration of the EVSE Object .................................................................................................. 4 2.2.7 Configuration of the Occupant Object .......................................................................................... 5 2.2.8 Configuration of the Dishwasher Object ....................................................................................... 5 2.2.9 Configuration of the Lights Object ................................................................................................ 6 2.2.10 Configuration of the Water Heater Object .................................................................................... 7 2.2.11 Configuration of the Plug Load Object .......................................................................................... 7 2.2.12 Configuration of the Refrigerator Object ...................................................................................... 8 2.2.13 Configuration of the Clothes Washer Object ................................................................................ 9 2.2.14 Configuration of the Dryer Object ................................................................................................. 9 2.2.15 Configuration of the Range Object .............................................................................................. 10

3 Model Simulation and Result Validation 11

3.1 EV Model Validation .................................................................................................................... 11 3.2 HVAC Model Validation ............................................................................................................... 12 3.3 Clothes Washer Model Validation ............................................................................................... 14 3.4 Dryer Model Validation ............................................................................................................... 15 3.5 Dishwasher Model Validation ...................................................................................................... 16 3.6 Oven Model Validation ................................................................................................................ 17 3.7 Refrigerator Model Validation ..................................................................................................... 18 3.8 Lighting Model Validation ........................................................................................................... 19 3.9 Miscellaneous Model Validation ................................................................................................. 20 3.10 Water Heater Model Validation .................................................................................................. 21 3.11 House Profile ................................................................................................................................ 22 3.12 EV Penetration Result .................................................................................................................. 25

4 References 28

IV

List of Figures

Figure ‎1-1: Conceptual Residential-Level Microgrid Architecture (a cyber-physical energy system application) 1

Figure ‎1-2: Cyber-Physical Energy System 1

Figure ‎2-1: IEEE 13 Node Test Feeder [9] 3

Figure ‎2-2: Structure of Residential Microgrid Model using GridLAB-D 3

Figure ‎2-3: Departure and Arrival Time Profile for EV 4

Figure ‎2-4: Occupant Schedule 5

Figure ‎2-5: Dishwasher Schedule 6

Figure ‎2-6: Light Schedule 7

Figure ‎2-7: Water Heater Schedule 7

Figure ‎2-8: Plug Load Schedule 8

Figure ‎2-9: Refrigerator Schedule 8

Figure ‎2-10: Clothes Washer Schedule 9

Figure ‎2-11: Dryer Schedule 10

Figure ‎2-12: Range Schedule 10

Figure ‎3-1: Daily EV Chaging Profile 11

Figure ‎3-2: Average HVAC Simulation Result 12

Figure ‎3-3: HVAC Load Profile from OpenEI 12

Figure ‎3-4: Simulation Result and OpenEI for HVAC 13

Figure ‎3-5: Indoor and Outdoor Temperature 13

Figure ‎3-6: Average Clothes Washer Simulation Result 14

Figure ‎3-7: Clothes Washer Load Profile from Reload 14

Figure ‎3-8: Average Dryer Simulation Result 15

Figure ‎3-9: Dryer Load Profile from Reload 15

Figure ‎3-10: Average Dishwasher Simulation Result 16

Figure ‎3-11: Dishwasher Load Profile from Reload 16

Figure ‎3-12: Average Oven Simulation Result 17

Figure ‎3-13: Oven Load Profile from Reload 17

Figure ‎3-14: Average Refrigerator Simulation Result 18

Figure ‎3-15: Refregirator Load Profile from Reload 18

Figure ‎3-16: Average Lighting Simulation Result 19

Figure ‎3-17: Lighting Load Profile from OpenEI 19

Figure ‎3-18: Average Miscellaneous Simulation Result 20

Figure ‎3-19: Miscellaneous Load Profile from OpenEI 20

Figure ‎3-20: Average Water Heater Simulation Result 21

Figure ‎3-21: Water Heater Load Profile from OpenEI 21

Figure ‎3-22: Type 1 Average Power Consumption in a Typical Summer Day 22

Figure ‎3-23: Type 2 Average Power Consumption in a Typical Summer Day 22

Figure ‎3-24: OpenEI Average Power Consumption in a Typical Summer Day 23

Figure ‎3-25: Average Power Consumption in a Typical Summer Day, [13] 23

Figure ‎3-26: Type 1 Average Power Consumption in a Typical Winter Day 23

Figure ‎3-27: Type 2 Average Power Consumption in a Typical Winter Day 24

Figure ‎3-28: OpenEI Average Power Consumption in a Typical Winter Day 24

Figure ‎3-29: Average Power Consumption in a Typical Winter Day [13] 24

Figure ‎3-30: Transformer-Level Power Output for 0% EV Penetration 25



V



List of Tables

Table ‎2-1: Node 632 1









Table ‎2-10: Node 652 3

Table ‎2-11: Node 611 3

Table ‎2-12: House Model and Appliance Specifications 3

Table ‎2-13: EV Model Specification 4

1

1 Introduction Microgrid is a localized and semiautonomous group of electrical energy resources (storage and generators such

as photovoltaics, wind turbines, fuel cells, and microturbines) and load (consumers) that connects to traditional

power grid (macrogrid). In physical and economic condition, it can disconnect from power grid, and operate

autonomously (island power) [1, 24]. Distributed generators and batteries make the microgrid more secure and

reliable during the catastrophes such as earthquake that might cause a lengthy power outage in electrical power grid

[2]. On other hand, microgrid should be robust in controlling supply, demand, voltage, and frequency [3].

Figure 1-1: Conceptual Residential-Level Microgrid Architecture (a cyber-physical energy system application)

Microgrid can be viewed as a cyber-physical energy system (CPES) that is composed of communication,

computation, control, and physical part [5, 24]. In the context of this work, we considered a microgrid that is built in

a residential area and we call it residential microgrid. Houses, buildings, appliances, transformers, power lines, and

Distributed Energy Resources (DER) are the physical part of CPES. Controllers, embedded systems, and networks

are the cyber part of this cyber-physical system.

Physical Processes

Computation

Communication

ControlNLCCHEM

PLC, ZigBee

WiFi, Ethernet

(Embedded Systems)

EVSE, AMI

DER, Power line, Transformer,

House, appliance

Figure 1-2: Cyber-Physical Energy System

Gridlab-D is an open-source simulation tool developed by Pacific Northwest National Laboratory (PNNL) with

the funding of Department of Energy Office of Electricity Delivery and Energy Reliability (DOE/OE) for power

grid designers to simulate and analyze the distribution microgrid [6][7]. Gridlab-D is currently one of the most

powerful modeling and simulation tools for simulating discrete event-based power systems. It employs an agent-

based simulation approach to model and simulate distribution power flow, distributed energy resources, energy

Grid interface

Controller

Storage controller

Local Embedded

Controllers

An example of Residential

Microgrid ( a CPS

application)

Physical network : bi-directional

electricity flow

Batt

ery

(lo

cal

sto

rage for

the

mic

ro g

rid)

Community-level

Electric vehicle

charging station

Communication network : bi-

directional communication among all

the entities

Houses are

prosumers due to

rooftop solar panels

Micro-level wind

turbine installed in

a community levelMicro-Wind turbine

controller

Wall mounted residential

Electric vehicle charging

station

Houses are

prosumers due to

rooftop solar panelsLocal Embedded

ControllersCommunity-level

solar panels

Solar

controller

Other

homes

within this

residential

microgrid

2

market, and the residential loads at the granularity of end-use appliances [7]. It also provides a variety of modules

with some controlling methods to help users to simulate smart grid, and microgrid.

In the scope of this work, we have modeled the residential microgrid to simulate and explore various dynamic

properties of the distribution power grid virtually and analyze them without the need for physical prototypes. To

model this residential microgrid, we have used the state-of-the-art distribution grid modeling tool GridLAB-D. Our

contributions within the scope of this technical report are as follows:

1) We have modeled a residential microgrid using the state-of-the-art distribution grid power system

modeling tool GridLAB-D. The microgrid is developed based-on the IEEE Distribution Test Feeder

[8]. In our model, we have used IEEE 13 Node Test Feeder [9].

2) We have implemented the EV and EVSE load within the GridLAB-D for our microgrid model.

3) We have validated the fidelity of our model including individual loads compared to the state-of-the-art

results.

4) We have demonstrated the impact of EV charging under various penetration rates for our developed

model.

The rest of the report is organized as follows. In Section 2 we present an instantiation of the GridLAB-D objects

for our developed microgrid model. Section 3 detailed the simulation results.

3

2 Residential Microgrid Modeling In this section, we describe our model, and the parameters of the objects that we have used in the scope of this

report. A schedule is attached to each object (e.g. appliance of type dryer, occupant, etc.). The schedules assumed to

be randomly distributed with a variance proportional to the mean (by using skew) whenever it is necessary.

2.1 Model Structure

We have modeled the structure of the residential microgrid with our modeling tool GridLAB-D [6] by using

IEEE 13 node [9]. Figure 2-1 shows the single line diagram of the IEEE 13 Test Feeder.

Figure 2-1: IEEE 13 Node Test Feeder [9]

Each transformer connects to one of the IEEE 13 nodes. House connects to transformer through the triplex

meter. Figure 2-2 illustrates the structure of residential microgrid in GridLAB-D. The node stands for one of IEEE

13 node.

Node

Transformer

1

0..*

Triplex Meter

1

1

Triplex line

1

0..*

Triplex Meter

1

1

House

1

1

Water heater

Dis

h w

ash

er

Dryer

Lights

Ran

ge

Ref

rige

rato

r

Clothes washer

Occupantload

Mic

row

ave

Free

zer

Plug load

EV charger

11

11

11

11

11

21

11

1

1

1

1

1

1

1

1

1

1

Figure 2-2: Structure of Residential Microgrid Model using GridLAB-D

In this Model, we have considered 1000 houses. Tables 2-1 to 2-11 show the number of transformers and houses

those are connected to each IEEE 13 node.

Phase A 320 houses

Phase B 305 houses

Phase C 375 houses

646 645 632 633 634

650

692 675611 684

652

671

680

1

Table 2-1: Node 632

IEEE 13 Node

Phase Transformer Rate [kVA]

Type 1

Type 2

632

'A'

20 1 3

35 7 0

15 2 1

20 0 4

30 3 3

25 3 2

35 7 0

20 3 1

'B'

25 3 2

35 2 5

25 2 3

35 4 3

25 0 5

35 2 5

20 3 1

35 4 3

'C'

25 0 5

30 6 0

15 1 2

25 4 1

20 3 1

35 5 2

30 0 6

30 3 3

Table 2-2: Node 684

IEEE 13 Node


Type 1

Type 2

684

'A'

30 4 2

35 6 1

30 0 6

35 6 1

20 2 2

15 0 3

35 7 0

30 3 3

'C'

15 0 3

35 7 0

15 3 0

25 3 2

15 0 3

15 2 1

15 2 1

Table 2-3: Node 645

IEEE 13 Node


Type 1

Type 2

645

'B'

15 2 1

25 3 2

25 2 3

15 2 1

15 1 2

25 3 2

20 3 1

35 6 1

'C'

30 6 0

25 2 3

25 4 1

35 5 2

15 2 1

35 0 7

20 4 0

30 4 2

Table 2-4: Node 633

IEEE 13 Node


Type 1

Type 2

633

'A'

25 0 5

30 4 2

15 0 3

25 4 1

35 0 7

30 0 6

30 6 0

25 1 4

'B'

15 0 3

20 3 1

15 3 0

15 1 2

25 1 4

25 0 5

20 4 0

25 0 5

'C'

20 1 3

20 4 0

15 0 3

15 3 0

25 0 5

25 4 1

25 4 1

15 1 2

Table 2-5: Node 692

2

IEEE 13 Node

Phase Transformer

Rate [kVA] Type

1 Type

2

692

'A'

15 1 2

30 5 1

25 3 2

30 4 2

35 3 4

25 4 1

30 4 2

'B'

25 5 0

15 2 1

30 1 5

30 0 6

15 2 1

30 4 2

30 5 1

'C'

20 0 4

25 2 3

35 2 5

35 0 7

35 7 0

35 5 2

20 1 3

Table 2-6: Node 671

IEEE 13 Node


Type 1

Type 2

671

'A'

20 0 4

35 3 4

15 2 1

25 2 3

35 4 3

20 1 3

20 3 1

'B'

25 4 1

20 0 4

30 3 3

35 6 1

35 7 0

20 3 1

20 3 1

25 4 1

'C'

30 6 0

25 0 5

15 1 2

30 6 0

35 3 4

25 3 2

35 5 2

Table 2-7: Node 675

IEEE 13 Node


Type 1

Type 2

675

'A'

25 4 1

25 5 0

30 0 6

30 3 3

15 3 0

35 4 3

25 1 4

'B'

25 1 4

25 0 5

30 4 2

25 2 3

30 4 2

30 6 0

30 1 5

'C'

35 1 6

15 1 2

20 0 4

30 0 6

20 3 1

15 2 1

30 4 2

Table 2-8: Node 680

IEEE 13 Node


Type 1

Type 2

680

'A'

35 6 1

15 3 0

30 1 5

15 0 3

30 4 2

30 1 5

20 0 4

'B'

30 4 2

15 2 1

30 4 2

30 1 5

20 1 3

20 0 4

30 1 5

30 5 1

'C'

30 2 4

35 6 1

20 3 1

25 3 2

30 6 0

20 4 0

20 2 2

3

Table 2-9: Node 646

IEEE 13 Node


Type 1

Type 2

646

'B'

25 0 5

15 0 3

20 3 1

30 0 6

30 3 3

20 3 1

15 2 1

15 3 0

'C'

20 3 1

35 1 6

30 3 3

15 3 0

30 5 1

35 6 1

25 0 5

25 5 0

Table 2-10: Node 652

IEEE 13 Node

Phase Transformer

Rate [kVA] Type

1 Type

2

652 'A'

35 1 6

35 3 4

30 3 3

35 0 7

30 5 1

20 3 1

35 2 5

15 0 3

Table 2-11: Node 611

IEEE 13 Node


Type 1

Type 2

611 'C'

25 0 5

15 0 3

20 4 0

30 1 5

35 7 0

25 5 0

20 2 2

25 5 0

2.2 Configuration of the Microgrid Model

As mentioned earlier, for our microgrid model we have used IEEE 13 nodes [9]. Transformers connect to each

phase of each node. Transformer connects a node to a triplex meter. In GridLAB-D, houses can be connected to

power flow through a triplex meter; therefore, each house should have one triplex meter. Triplex line connects

transformer triplex meter to house triplex meter. Below we describe the models of transformer, triplex line, triplex

meter, house, and appliances.

2.2.1 Configuration of the Triplex Line Object

Triplex line configuration defines the configuration of triplex line and is described as follows:

object triplex_line_configuration { name trip_line_config; conductor_1 object triplex_line_conductor { resistance 0.97; geometric_mean_radius 0.01111;}; conductor_2 object triplex_line_conductor { resistance 0.97; geometric_mean_radius 0.01111;}; conductor_N object triplex_line_conductor { resistance 0.97; geometric_mean_radius 0.01111;}; insulation_thickness 0.08; diameter 0.368; } object triplex_line { name TPL_[node number]_T_[transformer number]_[phase]S_[node number]; phases [phase]S;

2

from TP_T_[transformer number]_[phase]S_[node number]; to TM_[node]_T_[transformer number]_[phase]S_[node number]; length 2; configuration trip_line_config; groupid Triplex_Line_[phase]S; }

2.2.2 Configuration of the Transformer Object

The transformer is configured as a single phase center trapped with 2.4kV primary voltage.

object transformer { name T_[transformer number]_[phase]S_[node number]; from [node IEEE 13]; to TP_T_[transformer number]_[phase]S_[node number]; phases [phase]S; configuration object transformer_configuration { connect_type SINGLE_PHASE_CENTER_TAPPED; install_type POLETOP; shunt_impedance 10000+10000j; primary_voltage 2401.777000; secondary_voltage 120.000000; powerA_rating [#houses*5] kVA; power_rating [#houses*5] kVA; impedance 0.00033+0.0022j;}; groupid Distribution_Trans_[phase]S; }

2.2.3 Configuration of the Triplex Meter Object object triplex_meter { name TP_T_[transformer number]_[phase]S_[node number]; phases [phase]S; nominal_voltage 120.000000; groupid Trans_Meter_[phase]S; } object triplex_meter { name TM_[phase]_T_[transformer number]_[phase]S_[node number]; phases [phase]S; nominal_voltage 120.000000; groupid House_Meter; }

2.2.4 Configuration of the Residential House Object

We have connected 3 to 7 houses randomly to each transformer. We have characterized two types (Type 1 and

Type 2) of residential single-family houses in our residential microgrid model. In general, Type 1 houses present

houses with lower power consumption, and Type 2 houses stands for bigger houses with higher power consumption.

In [14], the author defined two types of houses, and the National Renewable Energy Laboratory [10] provides load

profile for three different types of houses. We have used the same approach to define our two types of houses. The

houses were randomly selected from Type 1 and Type 2. The table 2-12 shows the physical model and appliance

specifications for each type of house.

3

Table 2-12: House Model and Appliance Specifications

Type 1 Type 2 Validation

Source

Number of stories 1 2 [14]

Floor area 2100 sq. ft 2500 sq. ft [14]

Heating system GAS GAS [14]

Cooling system Electric Electric [14]

Thermal integrity Normal ABOVE_AVERAGE [14]

Motor efficiency AVERAGE AVERAGE [14]

Number of occupants 3 5 [20]

Heating set point 68 F 68 F [14]

Cooling set point 72 F 72 F [18]

Light power 1.2kW 1.5 kW [10]

Dishwasher power 1 kW 1.5 kW [16]

Water tank volume 40 gal 50 gal [14]

Water heater power 3 kW 4 kW [14]

Clothes washer power 0.8 kW 1 kW [14]

Miscellaneous 0.7 kW 0.8kW [10]

Compressor power 0.5kW 0.6kW [17]

Oven 2.4kW 3kW [19]

Oven set point 500 F 500 F [19]

Dryer 2kW 3kW [16]

We have extracted this information from the state-of-the-art statistical information sources: U.S. Department of

Energy [16], OkSolar [17], and Optimal energy management in community micro-grids [14].

Type 1 house is modeled as follows:

object house { parent ….; name H_[house number]_G_[type]_T_[transformer number]_[phase]S_[node number]; schedule_skew [random number]; floor_area 2100; number_of_stories 1; heating_system_type GAS; cooling_system_type ELECTRIC; thermal_integrity_level NORMAL; motor_efficiency AVERAGE; cooling_setpoint 72; heating_setpoint 68; groupid House_Type_1; }

Type 2 house is modeled as follows:

object house { parent ….; name H_[house number]_G_[type]_T_[transformer number]_[phase]S_[node number]; schedule_skew [random number]; floor_area 2500; number_of_stories 2; heating_system_type GAS; cooling_system_type ELECTRIC; thermal_integrity_level ABOVE_NORMAL; motor_efficiency AVERAGE; cooling_setpoint 72;

4

heating_setpoint 68; groupid House_Type_2; }

2.2.5 Configuration of the EV Object

To model the impact of EV penetration in our residential microgrid, we have considered five different

penetration rates of EV in the residential domain (0%, 10%, 20%, 30%, and 50%, respectively). In this model, we

have assumed that all the EVs arrive at their households following the Gaussian probability distribution model

(mean is 5:30 PM and standard deviation is 1 hour). The battery size is 25kWh or 40kWh according to the following

table.

Table 2-13: EV Model Specification

House EV

Battery Size

Miles classification

State of charge

Charging Amp.

Charging Volt.

Type 1 25kWh 75 miles 20% 30A 240V

Type 2 40kWh 140 miles 25% 30A 240V

We have used the same assumption when the EVs leave their households (EV departure follows a Gaussian

probability distribution with mean equals to 7:30 AM). Next figure illustrates the arriving time and departing time of

the EV.

Figure 2-3: Departure and Arrival Time Profile for EV

We have also used Gaussian probability distribution for the distance which the EV drives every day. When the

EV arrives home, the state of the charge (SOC) is modeled using the Gaussian probability distribution theory with

mean value according to the above table. In the next section, we show how to define EV model in GridLAB-D.

2.2.6 Configuration of the EVSE Object

In our model each EV has one EVSE which is installed inside each household. The EVSE starts to charge the EV

as soon as the EV arrives home, and stop charging when the battery is full or the EV leaves house. In this model, the

ESVE provides a constant rate of charge at 30A to charge the battery, so the battery gets charge at 7.2 kW rate. Time

of charging depends on the size of the battery and the initial state of the charge. The EVSE gets some information

from the EV model such as SOC, time of the next trip, distance of the next trip, the mileage classification, and the

battery size. The developers can use this information to control the charging rate of the EV through EVSE (EVSE

can be built smart and communicable to control the supplied AMPS).

To define the EVSE and the EV in GridLAB-D, we use the EVSE class which we have developed in the scope

of this work. The following code segment shows how we define EVSE and EV in our model:

object evse {

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

1:0

02

:00

3:0

04

:00

5:0

06

:00

7:0

08

:00

9:0

01

0:0

01

1:0

01

2:0

01

3:0

01

4:0

01

5:0

01

6:0

01

7:0

01

8:0

01

9:0

02

0:0

02

1:0

02

2:0

02

3:0

02

4:0

0

Pro

bab

ility

Time

EV Time Profile

Arriving Time

Departing Time

5

name evse_H_[house_number]; //EV model definition

battery_size [25 or 40]kW; mileage_classification [75 or 140];

variation_mean 0; // Gaussian distribution mean time for leaving and arriving time variation_std_dev 3600; //standard deviation of leaving and arriving time variation_trip_mean [mileage_classification*0.75]; // mean driving distance variation_trip_std_dev [mileage_classification*0.25/3]; // std. dev. of driving distance variation_SOC_mean [5 or 10]; // mean SOC variation_SOC_std_dev [(5 or 10)/3]; // std. dev. of SOC data_file "data_sample_NHTS.csv"; // input file of leaving and arriving time

vehicle_index 1; // index of input file //EVSE model definition

charge_current 30; is_240 1; groupid House_Type_[ 1 or 2]; }

2.2.7 Configuration of the Occupant Object object occupantload { name occupant_H_[house number]; schedule_skew [random number]; number_of_occupants [2 or 4]; occupancy_fraction OCCUPANT; heatgain_per_person 435; configuration IS220; groupid House_Type_[1 or 2]; };

Figure 2-4 shows the schedule for occupant as a fraction of number of occupants. Occupant impacts the

temperature of air inside the house and thereby the overall power consumption of a particular house.

Figure 2-4: Occupant Schedule

2.2.8 Configuration of the Dishwasher Object

The dishwasher simulation is based on an hourly demand profile attached to the object. The queue is used to

determine the probability of a load being run during that hour. Demand is added to the queue until the queue

becomes large enough to trigger an event.

object dishwasher {

0

0.2

0.4

0.6

0.8

1

1.2

1:0

02

:00

3:0

04

:00

5:0

06

:00

7:0

08

:00

9:0

01

0:0

01

1:0

01

2:0

01

3:0

01

4:0

01

5:0

01

6:0

01

7:0

01

8:0

01

9:0

02

0:0

02

1:0

02

2:0

02

3:0

02

4:0

0

Time

Schedule Winter

Summer

6

name dishwasher_H_[house number]; schedule_skew [random number]; energy_baseline [1kWh or 1.5kWh]; Heateddry_option_check true; control_power 10W; motor_power 250W; dishwasher_coil_power_1 580W; dishwasher_coil_power_2 695W; dishwasher_coil_power_3 950W; queue [random number]; queue_min 0;

queue_max 2; daily_dishwasher_demand [DISHWASHER schedule]*20; groupid House_Type_[1 or 2]; };

Figure 2-5 shows the schedule of a dishwasher as a daily demand.

Figure 2-5: Dishwasher Schedule

2.2.9 Configuration of the Lights Object object lights { name light_H_[house number]; installed_power [1.2kW or 1.5kW]; type INCANDESCENT; placement INDOOR; demand [LIGHTS schedule]; schedule_skew [random number]; groupid House_Type_[1 or 2]; };

Indoor lighting has impact on the internal temperature of a house, and the overall power consumption varies

based on lighting schedule.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1:0

02

:00

3:0

04

:00

5:0

06

:00

7:0

08

:00

9:0

01

0:0

01

1:0

01

2:0

01

3:0

01

4:0

01

5:0

01

6:0

01

7:0

01

8:0

01

9:0

02

0:0

02

1:0

02

2:0

02

3:0

02

4:0

0

De

man

d

Time

Schedule

Winter

Summer

7

Figure 2-6: Light Schedule

2.2.10 Configuration of the Water Heater Object object waterheater { name waterheater_H_[house number]; schedule_skew [random number]; tank_volume [40 or 50]; heating_element_capacity [3kW or 4kW]; demand [water sechduler]; groupid House_Type_[1 or 2]; };

Figure 2-7: Water Heater Schedule

2.2.11 Configuration of the Plug Load Object object plugload { name plugload_H_[house number]; schedule_skew [random number]; installed_power [0.7kW or 0.8kW]; demand [PLUGS schedule]; groupid House_Type_[1 or 2]; };

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Figure 2-8: Plug Load Schedule

2.2.12 Configuration of the Refrigerator Object object refrigerator { name refrigerator_H_[house number]; schedule_skew [random number]; compressor_on_normal_power [0.5kW or 0.6kW]; door_opening_criterion [REFRIGERATOR schedule]; daily_door_opening 20; size [20 or 25]; defrost_criterion DOOR_OPENINGS; delay_sefrost_time 600s; energy_used 13.5kWh; state COMPRESSSOR_OFF_NORMAL; groupid House_Type_[1 or 2]; };

Figure 2-9: Refrigerator Schedule

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2.2.13 Configuration of the Clothes Washer Object

The clothes washer simulation is based on an hourly demand profile attached to the object. The queue is used to



object clotheswasher { name clotheswasher_H_[house number]; schedule_skew [random number]; motor_power [0.8 or 1]kW; demand [CLOTHESWASHER schedule]; queue [random number] queue_min 0;

queue_max 2; state STOPPED; groupid House_Type_[1 or 2]; };

Figure 2-10: Clothes Washer Schedule

2.2.14 Configuration of the Dryer Object

The dryer simulation is based on an hourly demand profile attached to the object. The queue is used to determine

the probability of a load being run during that hour. Demand is added to the queue until the queue becomes large

enough to trigger an event.

object dryer{ name dryer_H_[house number]; schedule_skew [random number]; energy_baseline [2kWh or 3kW]; state STOPPED; daily_dryer_demand [DRYER schedule]; control_power 10W; motor_power 200W; dryer_coil_power 5800W; queue [random number]; queue_min 0; queue_max 2; groupid House_Type_[1 or 2]; };

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Figure 2-11: Dryer Schedule

2.2.15 Configuration of the Range Object

The range (oven) simulation is based on an hourly demand profile attached to the object. The queue is used to



object range{ name range_H_[house number]; schedule_skew [random number]; heating_element_capacity [2.4kW or 3kW]; oven_volume [5 or 8]; oven_setpoint 500; demand_oven [RANGE schedule]; queue [random] queue_min 0; queue_max 2; groupid House_Type_[1 or 2]; };

Figure 2-12: Range Schedule

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3 Model Simulation and Result Validation We have considered the existence of our residential microgrid at Newark, New Jersey location and simulated the

model for both Summer and Winter seasons. For Summer day simulation, we have simulated from August 2nd

to 3rd

2012. We have simulated the same power system model form January 3rd

to 4th

2012 for Winter season. Due to

avoid the setup overhead of our model which are GridLAB-D specific, we have simulated our model for two days to

stabilize the result, and used the second day simulation result for our experiments. We have compared the simulation

results of our model with different state-of-the-art sources. We use different sources to validate our simulation

results for the developed residential microgrid model. Residential load profile for all locations in the United States is

available in [10] developed by the National Renewable Energy Laboratory. We have validated simulation results of

various modeled end-use loads such as HVAC, lights, water heater with this source. The power data in [10] is

presented in three categories: Low, Base, and High. We have used the high category to validate our model for Type

2 houses. For other appliances, we have used [11] and [12] together. In [11], the annual energy consumption of the

residential appliance is presented according to the size of a building (the number of bedrooms). We have assumed

that the typical number of bedrooms within a single family residential house is 3, and extracted annual energy

consumption of each appliance. In [12], the hourly power load of each appliance presented as percentage of the

annual load. For our purpose, we have used load shapes profiles for January and August for Winter and Summer,

respectively. Following subsections show the simulation results of the appliances and compare the results with other

sources. In the 5.12 subsection, we have validated the overall model and show that the total average power

consumption of the house in our developed model is as close as the real data presented at [10], [13], [14]. We use

moving average to show our result whenever it is necessary. Finally, the last subsection shows the result for

different EV penetration rates.

3.1 EV Model Validation

EV specification is extract from [15] which is supported by the U.S. Department of Energy (DOE). According to

our model, the EV arrival follows a Gaussian probability distribution with the mean of 5:30 PM and 1 hour standard

deviation. This means that EV charging can start from 2:30 PM to 8:30 PM (99.7% of events are within three

standard deviations). The EVSE start to charge EV with 7.2kW when EV arrives home. The charging time depends

on the State-Of-the-Charge (SOC) and the size of the battery. In worst case, it takes 5.5 hours for Type 2 with empty

battery charge. In this model, the late time to arrive home is 8:30 PM, so the charging should start after 3:30 PM and

finish before 2:30 AM. The result of Type 2 shows same charging profile for EV.

Figure 3-1: Daily EV Chaging Profile

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12

3.2 HVAC Model Validation

The HVAC load profile completely depends on the outside temperature (weather input file), and physical model

of the house. The peak load of HVAC occurs when the temperature is at its maximum value (occurs around 3:00 PM

to 4:00 PM in the afternoon).

Figure 3-2: Average HVAC Simulation Result

Next figure shows the power profile of HVAC from OpenEI [10] for a Summer day.

Figure 3-3: HVAC Load Profile from OpenEI

In the following figure, we compare our simulation result for power consumption of HVAC considering a Type

2 house with daily load shape from OpenEI. Both of them are related to a Summer day in August for Newark, NJ.

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Figure 3-4: Simulation Result and OpenEI for HVAC

This figure shows that the peak load time of OpenEI occurs between 4:00 PM to 6:00 PM. However, in the

simulation, the peak load time occurs between 3:00 PM to 5:00 PM. This difference is related to outside temperature

data and the model of house. In our model, the peak of temperature is around 4:00 PM which requires maximum

activity of cooling system. Moreover, in our model the HVAC works all the time even though there is no occupant

in the house. Next figure, illustrates that outside temperature peak is around 4:00 PM. We extracted this data from

[15] (TMY2 data weather which we have used in our simulation).

Figure 3-5: Indoor and Outdoor Temperature

The cooling set point for HVAC is 72° F, and the thermostat dead band is 2° F, so the indoor temperature can

fluctuate between 71° F to 73° F. The previous figure shows the same result for the indoor temperature.

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14

3.3 Clothes Washer Model Validation

Our simulation results for a clothes washer show that Winter and Summer data are very similar, and Type 2

house has higher power consumption compared to Type 1 house. The following figure illustrates the average clothes

washer power consumption for two types of houses during both Winter and Summer seasons.

Figure 3-6: Average Clothes Washer Simulation Result

According to Building America House Simulation Protocols [11], the annual energy for clothes washer in a

house is 77kWh. We have used this data to extract the daily load shapes for clothes washer from Reload Data [12].

The next figure shows that our result for clothes washer is very similar to the simulation result from Reload Data.

Figure 3-7: Clothes Washer Load Profile from Reload

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3.4 Dryer Model Validation

Our simulation results for a dryer show that the dryer consumes more power in a typical Winter day than a

Summer day, and Type 2 data has higher power consumption than Type 1. The following figure illustrates the

average dryer power consumption for two types of house in both Winter and Summer.

Figure 3-8: Average Dryer Simulation Result

According to Building America House Simulation Protocols, the annual energy for dryer in a house is

1076.4kWh. We have used this data to extract the daily load shapes for dryer from Reload Data. The next figure

shows that our result for dryer is close to the simulation result from Reload Data.

Figure 3-9: Dryer Load Profile from Reload

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3.5 Dishwasher Model Validation

The simulation results for a dishwasher show that a typical Winter day power consumption is higher than a

Summer day and Type 2 consumes more power than Type 1. The following figure shows the average power

consumption for a dishwasher.

Figure 3-10: Average Dishwasher Simulation Result

We compare the result with the data from Building America House Simulation Protocols and Reload Data in the

next figure.

Figure 3-11: Dishwasher Load Profile from Reload

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3.6 Oven Model Validation

The simulation results for an oven show that a typical Winter day power consumption is higher than a Summer

day and Type 2 consumes more power than Type 1. The following figure shows the average power consumption for

an oven.

Figure 3-12: Average Oven Simulation Result

We compare the result with the data from Building America House Simulation Protocols and Reload Data in the

next figure.

Figure 3-13: Oven Load Profile from Reload

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3.7 Refrigerator Model Validation

The simulation results for a refrigerator show that a typical Summer day power consumption is higher than a

Winter day and Type 2 consumes more power than Type 1. The following figure shows the average power

consumption for a refrigerator.

Figure 3-14: Average Refrigerator Simulation Result

We compare the result with the simulation data from Building America House Simulation Protocols and Reload

Data in the next figure.

Figure 3-15: Refregirator Load Profile from Reload

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3.8 Lighting Model Validation

Our simulation results for the lighting show that a typical Winter day consumes more power than a Summer day,

and Type 2 data has higher power consumption than Type 1. Moreover, the peak load time for Winter occurs earlier

than Summer, and the peak time duration in Winter lasts much longer. The following figure illustrates the average

lighting consumption for two types of houses in both Winter and Summer.

Figure 3-16: Average Lighting Simulation Result

The next figure shows lighting data from OpenEI, and validates our result for the lighting.

Figure 3-17: Lighting Load Profile from OpenEI

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3.9 Miscellaneous Model Validation

In our model, miscellaneous includes plug loads such as TV and small appliances. The simulation results for the

miscellaneous show that a typical Winter day consumes more power than a Summer day, and Type 2 data has

higher power consumption than Type 1. Moreover, the peak load time for a Winter day occurs earlier than a

Summer day. The following figure illustrates the average miscellaneous consumption for two types of houses during

both Winter and Summer.

Figure 3-18: Average Miscellaneous Simulation Result

The next figure shows miscellaneous data from OpenEI and validates that our result for the miscellaneous is as

close as to the result from the National Renewable Energy Laboratory.

Figure 3-19: Miscellaneous Load Profile from OpenEI

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[kW

]

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21

3.10 Water Heater Model Validation

The simulation results for a water heater show that a Winter day consumes more power than a Summer day, and

Type 2 data has higher power consumption than Type 1. Moreover, the water heater has two peak load time (the

peak in the morning is bigger than peak time in the evening). The following figure illustrates the average water

heater consumption for two types of houses during both Winter and Summer seasons.

Figure 3-20: Average Water Heater Simulation Result

The next figure shows water heater data from OpenEI and validates our result for the water heater.

Figure 3-21: Water Heater Load Profile from OpenEI

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Summer Type2

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22

3.11 House Profile

In this section, we compare the average power consumption of a house with two different sources [10], [13].

Next two figures show our simulation result for a Summer day (average power consumption of Type 1 and Type 2

houses). The peak load occurs around the 6:00 PM.

Figure 3-22: Type 1 Average Power Consumption in a Typical Summer Day

Figure 3-23: Type 2 Average Power Consumption in a Typical Summer Day

The two following figures show the power consumption of a house from [10], [13] sources respectively, and

validate our results. The peak load time in the following figures is between 5:00 PM to 6:00 PM that is same as our

simulation result. Our peak load is about 4.5 kW which is as close as to the data from OpenEI.

0

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Po

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W]

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Average Power for Summer Refrigerator

Waterheater

Miscellaneous

Oven

Light

Dishwasher

Dryer

Clotheswasher

HVAC

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r [k

W]

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Average Power For Summer Refrigerator

Waterheater

Miscellaneous

Oven

Light

Dishwasher

Dryer

Clotheswasher

HVAC

23

Figure 3-24: OpenEI Average Power Consumption in a Typical Summer Day

Figure 3-25: Average Power Consumption in a Typical Summer Day, [13]

The two following figures illustrate our simulation result for a typical Winter day. The figures show two peaks of

power in daily load profile (one in the morning around 7:30 AM, the other one in the afternoon around 7:00 PM).

Figure 3-26: Type 1 Average Power Consumption in a Typical Winter Day

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Po

we

r [k

W]

Time

Open Energy Info for August

Waterheater

Miscellaneous

Appliances

Exterior Lights

Interior Lights

HVAC

0

0.5

1

1.5

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3.5

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Average Power for Winter Refrigerator

Waterheater

Miscellaneous

Oven

Light

Dishwasher

Dryer

Clotheswasher

HVAC

24

Figure 3-27: Type 2 Average Power Consumption in a Typical Winter Day

The next two figures show the power consumption of a house from [10], [13] respectively, and validate our

result. The peak load in the morning occurs between 7:30 AM to 8:00 PM and the peak load in the evening occurs

between 6:00 PM to 8:00 PM which is very close to our simulation results.

Figure 3-28: OpenEI Average Power Consumption in a Typical Winter Day

Figure 3-29: Average Power Consumption in a Typical Winter Day [13]

0

0.5

1

1.5

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Po

we

r [k

W]

Time

Average Power for Winter Refrigerator

Waterheater

Miscellaneous

Oven

Light

Dishwasher

Dryer

Clotheswasher

HVAC

0

0.5

1

1.5

2

2.5

3

3.5

4

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Po

we

r [k

W]

Time

Open Energy Info for Winter

Water heater

Miscellaneous

Appliances

Exterior Lights

Interior Lights

HVAC

25

3.12 EV Penetration Result

In the scope of this report, we have simulated our residential microgrid model according to five different EV

penetration rates (0%, 10%, 20%, 30%, and 50%). For each penetration rate, we have randomly distributed the EVs

among all houses; for example, 100 EVs were distributed among 1000 houses for 10% penetration rate. By

increasing the penetration rate, the residential transformer becomes overloaded during evening when all the EVs

arrive households and the EVSEs start to charge the batteries of the EVs. The following figures show the

transformer level power output during one day for different EV penetration rates. The vertical axe shows the

transformers, and the horizontal axe show time. We normalized the output power of the transformer to the rate of the

transformer. The blue color shows small number while the red color shows the large number which means the

transformer is overloaded heavily. In general, when the color change to yellow (and the red color consequently), it

means that the transformer works with higher load than its nominal rate.

Figure 3-30: Transformer-Level Power Output for 0% EV Penetration

26



27



28

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grid impact analysis of a residential microgrid under various ev penetration … · 2014-09-17 ·...

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