grid impact analysis of a residential microgrid under various ev penetration … · 2014-09-17 ·...
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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
Figure 3-31: Transformer-Level Power Output for 10% EV Penetration 26
Figure 3-32: Transformer-Level Power Output for 20% EV Penetration 26
V
Figure 3-33: Transformer-Level Power Output for 30% EV Penetration 27
Figure 3-34: Transformer-Level Power Output for 50% EV Penetration 27
List of Tables
Table 2-1: Node 632 1
Table 2-2: Node 684 1
Table 2-3: Node 645 1
Table 2-4: Node 633 1
Table 2-5: Node 692 1
Table 2-6: Node 671 2
Table 2-7: Node 675 2
Table 2-8: Node 680 2
Table 2-9: Node 646 3
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
Phase Transformer Rate [kVA]
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
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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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8
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
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 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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Summer
10
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
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 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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11
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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we
r [k
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EV Charging Profile
Type 1
Type 2
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.
0
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we
r [k
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.5
1
1.5
2
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3.5
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
13
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.
0
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we
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Compare Load Profile
OpenEI
Summer Type2
70
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Tem
pe
ratu
re [
F]
Time
Compare Load Profile
indoor_temperature
outdoor_temperature
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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we
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.005
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0.02
0.025
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1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.005
0.01
0.015
0.02
0.025
0.03
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
15
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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we
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.005
0.01
0.015
0.02
0.025
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1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.005
0.01
0.015
0.02
0.025
0.03
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
16
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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we
r [k
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.005
0.01
0.015
0.02
0.025
0.03
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.005
0.01
0.015
0.02
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
17
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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we
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.01
0.02
0.03
0.04
0.05
0.06
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
18
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
0.06
0.065
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we
r [k
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
19
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
0
0.2
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0.8
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:00
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Po
we
r [k
W]
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Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.2
0.4
0.6
0.8
1
1.2
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
20
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
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
11
:00
2:0
03
:00
4:0
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:00
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07
:00
8:0
09
:00
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:00
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:00
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:00
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:00
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:00
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:00
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:00
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:00
20
:00
21
:00
22
:00
23
:00
24
:00
Po
we
r [k
W]
Time
Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
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
0
0.2
0.4
0.6
0.8
1
1.21
:00
2:0
03
:00
4:0
05
:00
6:0
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:00
8:0
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:00
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:00
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:00
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:00
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:00
20
:00
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:00
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:00
23
:00
24
:00
Po
we
r [k
W]
Time
Average Load Profile
Winter Type1
Winter Type2
Summer Type1
Summer Type2
0
0.2
0.4
0.6
0.8
1
1.2
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for January
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 3 5 7 9 11 13 15 17 19 21 23
Po
wer
[kW
]
Daily Hours
Load shapes for August
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
0.5
1
1.5
2
2.5
3
3.5
4
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
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01
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01
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01
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01
9:0
02
0:0
02
1:0
02
2:0
02
3:0
02
4:0
0
Po
we
r [k
W]
Time
Average Power for Summer Refrigerator
Waterheater
Miscellaneous
Oven
Light
Dishwasher
Dryer
Clotheswasher
HVAC
0
1
2
3
4
5
1:0
02
:00
3:0
04
:00
5:0
06
:00
7:0
08
:00
9:0
01
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01
1:0
01
2:0
01
3:0
01
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01
5:0
01
6:0
01
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01
8:0
01
9:0
02
0:0
02
1:0
02
2:0
02
3:0
02
4:0
0
Po
we
r [k
W]
Time
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
2
2.5
3
3.5
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
Po
we
r [k
W]
Time
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
2
2.5
3
3.5
4
1:0
02
:00
3:0
04
:00
5:0
06
:00
7:0
08
:00
9:0
01
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01
1:0
01
2:0
01
3:0
01
4:0
01
5:0
01
6:0
01
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01
8:0
01
9:0
02
0:0
02
1:0
02
2:0
02
3:0
02
4:0
0
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
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 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
Figure 3-31: Transformer-Level Power Output for 10% EV Penetration
Figure 3-32: Transformer-Level Power Output for 20% EV Penetration
27
Figure 3-33: Transformer-Level Power Output for 30% EV Penetration
Figure 3-34: Transformer-Level Power Output for 50% EV Penetration
28
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