distribution system analysis for smart grid
TRANSCRIPT
Distribution System Analysis for Smart Grid
Roger C. DuganSr. Technical Executive, EPRI
Webcast
Feb 8, 2011
2© 2011 Electric Power Research Institute, Inc. All rights reserved.
OPENSG-SIMSWG Feb 2011
EPRI Power Systems Modeling/Analysis Group
• Resource group -- systems modeling, simulation, analysis
• Consulting services from generation to end-use
• Resource support for R&D collaborative efforts
– Transmission planning
– Operations
– Distribution planning and operations
– Substation design
– Power quality
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The Smart Grid
• SG is different things to different people
– Communications and control
• Typically not represented in DSA (at present)
– Distributed Resources
• Generation, Storage, Demand Response
– Test Feeders WG has done large induction machines
– Monitoring
– Protection
– Energy Efficiency
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Smart Grid Features
• Distributed Resources
– Generation
– Renewable Generation
• Variable sources
– Energy Storage
– Demand Response
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Smart Grid Features, cont’d
• Communications and Control
– AMI deployed throughout the system
– High-speed communications to Metering and Controls
– State Estimation
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Impact of SG on Distribution System Analysis?
• What DSA framework is needed to support all features of the SG?
• Will there be a need for DSA if everything is monitored thoroughly?
• What could we do if we know more about the system?
• How will merging of planning, monitoring and DSE change DSA tools?
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Role of Distribution System Analysis
• Distribution state estimation
• Emergency reconfiguration
– Account for missing data, failed comm
• EPRI vision
– Planning and DMS will converge into one set of tools (Real time and planning will merge)
• Continued need for DSA tools
– Different form and more capabilities
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Advanced Simulation Platform -- OpenDSS
• Open source of EPRI’s Distribution System Simulator (DSS)
– developed in 1997
– open sourced in 2008 to collaborate with other research projects
• OpenDSS designed to capture
– Time-specific benefits and
– Location-specific benefits
• Differentiating features
– full multiphase model
– numerous solution modes
– “dynamic” power flow
– system controls
– flexible load models
• Needed for analysis of
– DG/renewables
– energy efficiency
– PHEV/EV
– non-typical loadshapes
Download for free from
http://sourceforge.net/projects/electricdss
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Computing Annual Losses
Peak load losses are not necessarily indicative of annual losses
0
10
20
30
40
50
60
70
Lo
ad
, M
W
1
5
9
13
17
21
Jan Ap
r Jul
Oct
0
5000
10000
15000
20000
25000
kWh
Hour
Month
Year 5 Losses: total 2413 MWh
20000-25000
15000-20000
10000-15000
5000-10000
0-5000
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Overall Model Concept
Control
Center
Control
Power Conversion
Element
("Black Box")
Inf. Bus
(Voltage, Angle)
Comm
Msg Queue 1
Comm
Msg Queue 2
Power Delivery
System
Supporting Renewables
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Solar PV Simulation 1-hr Intervals
-1
0
1
2
3
4
5
2 Weeks
MW
-1
0
1
2
3
4
5
Dif
fere
nce,
MW
Without PV With PV
Difference
Peak is not reduced
What is the Capacity Gain?
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Can DMS Enable Increased Capacity?
-1
0
1
2
3
4
5
2 Weeks
MW
-1
0
1
2
3
4
5
Dif
fere
nce,
MW
Without PV With PV
Difference
Shorter Duration
Of Peak
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Cloud Transients: 1-sec Interval
1-Sec Solar PV Output Shape with Cloud Transients
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000
Time,s
Pe
r U
nit
of
Ma
xim
um
Impact on Feeder Voltage
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Solar Ramping
Basic Solar Ramp Function
Regulators compensate for drop
Voltage Pushed over limit on recovery
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Regulator Response for Series of Cloud Transients
Regulator Operations
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“Headroom” for PV
Voltage Profile for 100% Load
Voltage Profile for 40% Load
Use DMS to Regulate Lower to
Allow More “Headroom” for DG
More efficient, too??
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Steady-State Voltage
Primary Bus Voltages - Base Case
0.95
0.975
1
1.025
1.05
1.075
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
distance from substation (mi)
per-
un
it v
olt
ag
e
V(1)
V(2)
V(3)
• Maximum change in voltage
• PV at increased penetration until limit exceeded
• Use of volt/var control accommodates added PV before violations occur
10% PV
Voltage Change
0
0.005
0.01
0.015
0.02
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
distance from substation (mi)
per-
un
it v
olt
ag
e
deltaV(1)
deltaV(2)
deltaV(3)
15% PV
Voltage Change
0
0.005
0.01
0.015
0.02
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
distance from substation (mi)
per-
un
it v
olt
ag
e
deltaV(1)
deltaV(2)
deltaV(3)
20% PV
Voltage Change
0
0.005
0.01
0.015
0.02
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
distance from substation (mi)
per-
un
it v
olt
ag
e
deltaV(1)
deltaV(2)
deltaV(3)
20% PV with VVC
Voltage Change
0
0.005
0.01
0.015
0.02
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
distance from substation (mi)
per-
un
it v
olt
ag
e
deltaV(1)
deltaV(2)
deltaV(3)
Device Locations / Voltage
638000 640000 642000
X
185000.00
186000.00
187000.00
188000.00
189000.00
Y
Baseline – No PV
10% PV
15% PV
20% PV
20% PV and VVC
PV
VAnalysis results from other
feeders indicate 25%-100%
more PV can be
accommodated using VVC
Substation
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Primary Voltage Response with Volt/Var Control
12 kV Voltage
0.9
0.925
0.95
0.975
1
1.025
1.05
0 4 8 12 16 20
Hour
V (
pu
)
Baseline – No PV
20% PV
20% PV w/ volt-var control
Storage
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Generic Storage Element Model(OpenDSS Model)
% Eff. Charge/DischargeIdle | Discharge | Charge
Idling Losses
kW, kvar
kWh
STORED
Other Key
Properties
% Reserve
kWhRated
kWhStored
%Stored
kWRated
etc.
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Controlling Storage from DMS
Discharge Mode
Charge Mode
kW Target
Discharge Time
Total Fleet kW Capacity
Total Fleet kWh
et. al.
Storage “Fleet”Substation
V, IComm Link
Time + Discharge rate
Peak Shaving
Load Following
Loadshape
Substation controller/DMS
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Load Shapes With and Without Storage
Mode=Peak Shave, Target=8000 kW, Storage=75 kWh
Charge=2:00 @ 30%
0
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6000
7000
8000
9000
10000
0 50 100 150 200 250 300
Hours
kW
0
10
20
30
40
50
60
70
80
Base kW
Net kW
kWh Stored
Simple Substation Peak Shaving
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Load Shapes With and Without Storage
Mode=Load Follow, Time=14:00, Storage=25 kWh
Charge=2:00 @ 30%
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 50 100 150 200 250 300
Hours
kW
0
5
10
15
20
25
30
Base kW
Net kW
kWh Stored
Attempting Peak Shave Every Day
Too Early
About Right
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OPENSG-SIMSWG Feb 2011
Load Shapes With and Without Storage
Mode=Time + fixed rate, Time=14:00 @ 25% Storage=25 kWh
Charge=2:00 @ 30%
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
200 210 220 230 240 250
Hours
kW
0
5
10
15
20
25
30
Base kW
Net kW
kWh Stored
Accounting for Storage Losses
ChargingDIscharging
Charging energy > Discharging energy (compare areas)
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Key Distribution Modeling Capabilities for Smart Grid
• Distributed generation modeling
• Time series simulations
• Efficiency studies
• Meshed networks
• Large systems
• Parallel computing
• Distribution state estimation
• Protective relay simulation
• AMI Load data
• Modeling controllers
• Modeling comm
• Work flow integration
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Key Challenges
• Merging Planning and Real-Time Analysis
• Very Large System Models
• Systems Communications Simulations
• Large Volume of AMI Data
• AMI-based Decision Making
• Time Series Simulations
• Distribution State Estimation
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Key Challenges, Cont’d
• Detailed LV Modeling
• Including multiple feeders, transmission
• DG Integration and Protection
• Generator and Inverter Models
• Meshed (Looped) Network Systems
• Regulatory Time Pressures
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Reference
• R.C. Dugan, R. F. Arritt, T. E. McDermott, S. M. Brahma, K. P. Schneider, “Distribution System Analysis To Support the Smart Grid”, presented at 2010 IEEE PES General Meeting, Minneapolis, MN
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Together…Shaping the Future of Electricity