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© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 1
OpenDSS: Industry Applications
IEEE PES Rio de Janeiro Chapter
SBSE 2018
Prof Luis(Nando) Ochoa
IEEE PES Distinguished Lecturer
Professor of Smart Grids and Power Systems
13th May 2018
SBSE 2018, Niteroi - RJ, Brazil
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 2
Prof Luis(Nando) Ochoa
Research Areas
HV/LV Network Integration of Distributed Energy Resources
Advanced Operation and Planning of Distribution Networks
Future Distribution System Operators
RTDS Hardware-in-the-Loop Studies
Professor of Smart Grids and Power Systems
The University of Melbourne, Australia
Professor of Smart Grids (Part Time)
The University of Manchester, UK
https://sites.google.com/view/luisfochoa
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 3
Outline
Impacts of Low Carbon Technologies on LV Networks
– Low Voltage Network Solutions Project
Management of Electric Vehicles
– My Electric Avenue Project
Management of PV-Rich Networks (Smart Inverters)
– Active Management of LV Networks Project
Optimal Voltage Control in MV-LV Networks
– Smart Street Project
Conclusions
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 4
Impacts of Low Carbon Technologies on LV Networks
Dr Alejandro Navarro (Past PhD Student)
LV Network Solutions Projectwww.enwl.co.uk/lvns ; Research Gate
A. Navarro, L.F. Ochoa, Probabilistic impact assessment of low carbon technologies in LV distribution systems, IEEE Trans. on Power Systems, May 2016
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 5
Impacts of LCTs on LV Networks
LV Network Solutions (LVNS) Project
Impact Assessment Methodology
Creation of Low Carbon Technology (LCT) profiles
Stochastic Analysis Using OpenDSS
– Impact Assessment Application
– Single feeder, metrics and multi-feeder
Key Remarks
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 6
LV Network Solutions (LVNS) Project
To understand the behaviour and needs of future LV networks with high penetrations of low carbon technologies (LCTs)
Residential Loads
3.863 3.864 3.865 3.866 3.867 3.868 3.869
x 105
3.9785
3.979
3.9795
3.98
3.9805
3.981
3.9815
3.982
3.9825
3.983
3.9835
x 105
[m]
[m]
Substation 16
Electric Vehicles (EV) Photovoltaic Panels (PV)
Electric Heat Pumps (EHP)
Micro combine heat & power (uCHP)
Different behaviour and sizes of loads and LCT along the day
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 7
How to achieve this objective?
Considerations
– Monte Carlo analysis to cope with the uncertainty (LCT sizeand location, sun profile, heat requirements, EV utilization,load profile, etc.)
– Time-Series Analysis (5-min synthetic data)
– Three-phase unbalanced power flow (OpenDSS)
Input data
– Load and LCT profiles
– Real UK networks (topology and characteristics)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 8
Impact Assessment Methodology
Impacts metrics:
– Customers with voltage problems: definedaccording to the Standard BS EN 50160.
– Utilization level of the head of the feeder: hourlymaximum current divided by the ampacity.
• Random allocation for each customer node.
Loads
• Random allocation of sites and sizes.
LCT• Time Series
Simulation.
• 3 Phase four wire power flow
Power Flow
Impact Assessment
Results Storage
0
20
40
60
80 050
100150
200250
300
228
230
232
234
236
238
24 hours - 5 minutes Resolution
Voltage Profile in each load
Loads
V
This process is repeated 100 times for each feeder and penetration level
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 9
Creation of Profiles
Pools of thousands of different individual residential profiles with a granularity of 5 minutes are created for:
– Loads
– Photovoltaic Panels
– Electric Vehicles
– Micro CHP Units
– Electric Heat Pumps0
200
400
600
800
1000
1200
1 13 25 37 49 61 73 85 97 109
121
133
145
157
169
181
193
205
217
229
241
253
265
277
W/m2
24 Hours - 5 minutes resolution
0 50 100 150 200 250 3000
1
2
3
4
5
6
7
8
9
Ele
ctr
icity [
kW
]
0 50 100 150 200 250 3000
0.2
0.4
0.6
0.8
1
1.2
1.4
Ele
ctr
icity [
kW
]
0 50 100 150 200 250 300-5
0
5
10
ºC
24 Hours - 5 min resolution
0 50 100 150 200 250 3000
5
10
15
kW
Temperature
Auxiliary Heater
EHP Consumption
EHP Production
Loads uCHP EHP
Sun Profiles
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 10
Stochastic Analysis Using OpenDSS
4-Result Visualization
3-Power flow Simulation
2-Profiles Random Allocation
1-Input Data acquisition
OpenDSS driverRandom variables creatorResults Analyser
Time-Series, three-phase power flow solver
Simple
Can also be done using VBA,
Python, etc.
DSS
COM
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 11
Impact Assessment Application
The voltage and thermal metrics are presented for the real feeder shown in the figure.
The PV, EV, EHP and uCHPare implemented and studied.
Voltage reference at the bus bar (secondary side):
Vsec = 241 Vfn (1.05*Vnom) 2.2 km (including services cables) and 94 loads
3.8395 3.84 3.8405 3.841 3.8415 3.842 3.8425
x 105
3.9315
3.932
3.9325
3.933
3.9335
x 105
[m]
[m]
Substation 18
Example using PV panels
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 12
Stochastic Impact Analysis of LCTs:Input data
3.8395 3.84 3.8405 3.841 3.8415 3.842 3.8425
x 105
3.9315
3.932
3.9325
3.933
3.9335
x 105
[m]
[m]
Substation 18
Typical definitions (preferably before LineCode and Lines):
set datapath=C:\......
new circuit.LV …….
new transformer.LVSS …..
Information sent usingthe command redirect
Don’t forget the monitors
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 13
Stochastic Impact Analysis of LCTs:Input data
Average profile
0 50 100 150 200 250 3000
0.5
1
1.5
2
2.5
3
24 Hours - 5 min resolution
[kW
]
Profile 1
Profile 2
Profile 3
0 50 100 150 200 250 3000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
24 Hours - 5 min resolution[k
W]
Individual profiles
This average is calculated among the 100 profiles provided
Realistic Load Profiles*
* I. Richardson, “Integrated High-resolution Modelling of Domestic Electricity Demand and Low Voltage Electricity Distribution Networks”, PhD Thesis, University of Loughborough, 2011
Information sent usingthe command redirect
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 14
Average profile
0 50 100 150 200 250 3000
0.5
1
1.5
2
2.5
3
3.5
24 Hours - 5 min resolution
[kW
]
Profile 1
Profile 2
Profile 3
0 50 100 150 200 250 3000
0.5
1
1.5
2
2.5
3
3.5
24 Hours - 5 min resolution[k
W]
Individual profiles
This average is calculated among the 100 profiles provided
Stochastic Impact Analysis of LCTs:Input data
* The University of Manchester, “The Whitworth Meteorological Observatory.”[Online]. Available: http://www.cas.manchester.ac.uk/restools/whitworth/.
Realistic Load Profiles*
Information sent usingthe command redirect
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 15
Stochastic Impact Analysis of LCTs:MATLAB
11/04 kV transforme
r
• MATLAB randomly selects a domestic profile for each house and sends it through the COM server to OpenDSS
Repeated for each house
• MATLAB randomly selects a house to allocated the LCT, its size, etc., and sends the corresponding LCT profile through the COM server to OpenDSS
Repeated for each house with LCT
Load+PV
Load
Load+PV
Load
Random assignation of variables
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 16
Stochastic Impact Analysis of LCTs:OpenDSS
11/04 kV transforme
r
Current at the head of the feeder
0 50 100 150 200 250 3000
10
20
30
40
50
60
70
80
24 hours - 5 minutes resolution
Curr
ent
[A]
0 50 100 150 200 250 300234
235
236
237
238
239
240
241
242
24 hours - 5 minutes resolution
Voltage [
V]
Voltage at the last customer
Load+PV
Load
Load+PV
Load
Time-SeriesUnbalanced
Power Flows
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 17
Stochastic Impact Analysis of LCTs:MATLAB
11/04 kV transforme
r
Load+PV
Load
Load+PV
Load
AssessmentVisualization
Current at the head of the feeder
Voltage at the last
customer
0 50 100 150 200 250 3000
50
100
150
200
250
24 hours - 5 minutes resolution
Curr
ent
[A]
Summer Loads
+PVs
0 50 100 150 200 250 300230
235
240
245
250
255
260
24 hours - 5 minutes resolution
Voltage [
V]
Summer Loads
+PVs
Extract results&
Analysis
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 18
Metric 1: Voltage Problems
% of Customers with Voltage Problems –BS EN 50160
0 10 20 30 40 50 60 70 80 90 100 1100
10
20
30
40
50
60
PV Penetration [%]
Cu
sto
me
rs [
%]
0 10 20 30 40 50 60 70 80 90 100 1100
5
10
15
20
25
EHP Penetration [%]
Cu
sto
me
rs [
%]
0 10 20 30 40 50 60 70 80 90 100 1100
0.5
1
1.5
2
2.5
3
EV Penetration [%]
Cu
sto
me
rs [
%]
PV
EV
EHP
uCHP
No voltage problems in this feeder with
uCHP
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 19
Metric 2: Thermal Problems
Utilization Level of the Head of the Feeder
0 10 20 30 40 50 60 70 80 90 100 1100
10
20
30
40
50
60
70
80
PV Penetration [%]
Utiliza
tio
n L
eve
l [%
]
0 10 20 30 40 50 60 70 80 90 100 1100
50
100
150
EHP Penetration [%]
Utiliza
tio
n L
eve
l [%
]
0 10 20 30 40 50 60 70 80 90 100 1100
5
10
15
20
25
30
35
40
45
uCHP Penetration [%]
Utiliza
tio
n L
eve
l [%
]
0 10 20 30 40 50 60 70 80 90 100 1100
10
20
30
40
50
60
70
80
90
100
EV Penetration [%]
Utiliza
tio
n L
eve
l [%
]
PV
EVµCHP
EHP
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 20
Multi-Feeder Analysis
Network Examples
3.899 3.9 3.901 3.902 3.903 3.904 3.905 3.906
x 105
3.984
3.985
3.986
3.987
3.988
3.989
x 105
[m]
[m]
Substation 2
3.569 3.57 3.571 3.572 3.573 3.574
x 105
4.014
4.0145
4.015
4.0155
4.016
4.0165
4.017
4.0175
4.018
x 105
[m]
[m]
Substation 3
3.6245 3.625 3.6255 3.626 3.6265 3.627 3.6275 3.628 3.6285 3.629
x 105
4.043
4.0435
4.044
4.0445
4.045
4.0455
4.046
4.0465
x 105
[m]
[m]
Substation 4
3.596 3.5965 3.597 3.5975 3.598 3.5985 3.599 3.5995 3.6 3.6005 3.601
x 105
3.9835
3.984
3.9845
3.985
3.9855
3.986
3.9865
3.987
x 105
[m]
[m]
Substation 5
3.8105 3.811 3.8115 3.812 3.8125 3.813
x 105
3.9788
3.979
3.9792
3.9794
3.9796
3.9798
3.98
3.9802
3.9804
3.9806
3.9808
x 105
[m]
[m]
Substation 6
3.7995 3.8 3.8005 3.801 3.8015 3.802 3.8025 3.803 3.8035 3.804
x 105
3.955
3.9555
3.956
3.9565
3.957
3.9575
3.958
3.9585
x 105
[m]
[m]
Substation 7
3.863 3.864 3.865 3.866 3.867 3.868 3.869
x 105
3.9785
3.979
3.9795
3.98
3.9805
3.981
3.9815
3.982
3.9825
3.983
3.9835
x 105
[m]
[m]
Substation 16
3.905 3.9055 3.906 3.9065 3.907 3.9075 3.908 3.9085 3.909 3.9095 3.91
x 105
3.9275
3.928
3.9285
3.929
3.9295
3.93
3.9305
3.931
3.9315
x 105
[m]
[m]
Substation 1
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 21
Multi-Feeder Analysis (128)
Feeders with less than 25 customers (30%) do not present any technical problem for any of the technologies analysed
Below are the results for the feeders with a technical problem at some penetration level:
% of feeders with problems per technology
% of “Bottleneck” cases per technology
PV EHP uCHP EV EV Fast EV Shifted0
10
20
30
40
50
60
70
[%]
% of Feeders with Voltage Problems
% of Feeders with Thermal Problems
PV EHP uCHP EV EV Fast EV Shifted0
10
20
30
40
50
60
70
80
90
100
[%]
Voltage Problems before than Thermal Problems
Thermal Problems before Voltage Problems
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 22
Key Remarks
Uncertainties of LCT Probabilistic impact assessment
– Identifying the likelihood of impacts
True understanding of impacts Realistic models
– Networks, demand, LCTs
Monitoring When, where, what, how often?
– Who keeps an eye on the data (flagging issues)?
Industry needs to adopt this learning
– ENWL is now integrating the findings of LVNS into their rules for monitoring LV networks with LCTs
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 23
Management ofElectric Vehicles
Dr Jairo Quiros (Past Post-Doc)
My Electric Avenue Projectmyelectricavenue.info ; Research Gate
J. Quiros, L.F. Ochoa, S.W. Alnaser, T. Butler, Control of EV charging points for thermal and voltage management of LV networks, IEEE Trans. on Power Systems, Jul 2016
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 24
Management of Electric Vehicles
My Electric Avenue (MEA) Project
– General Idea, Trials and Infrastructure
EV Charging Behaviour Modelling
EV Management Design
EV Management Using OpenDSS
– Control of EV charging points
– Case study
Key Remarks
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 25
Control of EV Charging PointsGeneral Idea
Computer
100%
Challenges:
• Charging behaviour of EVs
• Modelling of customers
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 26
Conceptual Approach
1. Disconnect charging points when problems are detected
• Following a hierarchical (corrective) approach
2. Reconnect charging points when no problems are detected
• Following a hierarchical (preventive) approach
3. Suitable selection of the EVs based on a priority list (times)
Simplest Control
Algorithm
AdvancedControl
Algorithms
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 27
Geographical Extent of the Trial
112 Social Trials
109 Technical Trials
221 in total
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 28
Transformer
11/0.4 kVPLC
Infrastructure Overview
MEA makes the most of available infrastructure
Sensors and actuators at EV charging points
PLC-like device at substations
(control hub)
Power Line Carrier-based
communications
(bi-directional)
Sensors (V, I) head of feeders
Violations in the thermal
limits
Significant voltage drops
State of Charge: Unknown
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 29
Transformer
11/0.4 kVPLC
Substation
Infrastructure Overview
ROLEC* charging point
+EA TechnologyIntelligent Control Box
Real 500 kVA Transformer
* http://www.rolecserv.com/
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 30
Statistical Analysis ofCARWINGS Data
30
Time of day
EV
Sta
tus
0h 3h 6h 9h 12h 15h 18h 21h 24h 3h 6h 9h 12h 15h 18h 21h 24hOFF
ON
SOC = 6 Units
SOC = 12 Units
SOC = 11 Units
SOC = 12 Units
More than 75,000
charging samples
(without control)
Single EV, 2 days
Crucial to understand EV users charging behaviour
2 daily conns
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 31
From Statistical Analysisto Realistic EV Models
0h 2h 4h 6h 8h 10h 12h 14h 16h 18h 20h 22h 24h0
1
2
3
4
Time of Day
EV
De
ma
nd
(kV
A)
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Div
ers
ifie
d E
V D
em
an
d (
kV
A)
EV Load 1
EV Load 2
EV Load 3
Diversified
EV Demand: ~1.2kW
Energy: ~15 kWh
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 32
ESPRIT-Based Control:Design Challenges
Hierarchical (corrective) disconnection
Hierarchical (preventive) reconnection
The number and which EVs will be managed
Effects on customers – charging delays
Feeder Level(per phase per feeder)
Transformer Level
Feeder Level(per phase per feeder)
Transformer Level
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 33
EV Management Using OpenDSS
4-Result Visualization
3-Power flow SimulationAdopting the control algorithm
2-Profiles Random Allocation
1-Input Data acquisition
OpenDSS driverRandom variables creatorControl ImplementationResults Analyser
DSS
COM
Time-Series, three-phase power flow solver
Simple
Can also be done using VBA,
Python, etc.
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 34
EV management Using OpenDSS
Random assignation of variables
Time-SeriesUnbalanced
DSSCOMInitialization
(Similar to the Impact
Assessment)
Control
Calculations Control settings
Time-SeriesUnbalanced
DSSCOM
Every control cycle
MATLAB is also used to extract monitors, assess, and visualise the results
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 35
Transformer
11/0.4 kVPLC
EV Management Using OpenDSS
Calculations Control settings
Time-SeriesUnbalanced
DSSCOM
Every control cycle
Hierarchical (corrective) disconnection
• Disconnection (Feeder)
Required to mitigate overload
Number of charging points with voltages below the limit
The maximum of both
• Disconnection (TX)
Required to mitigate overload
Which ones?
Problems?
Y
• Data collection
Phase current (head of feeder)
Busbar phase voltages
Charging point phase voltages
Charging time is updated for each EV
To select EVs to be managed (SOC
is unknown)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 36
EV Management Using OpenDSS
EV1 will be disconnected fist
Its charging time is longer than EV2
0
1
EV
Sta
tus
Time
EV1 EV2
Decision must be taken
Feeder Level: Each phase is treat independently
EVs in a phase without problems will not be affected
TX Level: Three-phase analysis
Every EV may be disconnected
Problems are fairly shared
The longer the charging time, the more likely it is to be disconnected
Calculations Control settings
Time-SeriesUnbalanced
DSSCOM
Every control cycle
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 37
Transformer
11/0.4 kVPLC
EV Management Using OpenDSS
Hierarchical (preventive) reconnection
Which ones?
Problems?
N
• Reconnection (TX)
Max # to keep TX loading below a security margin
The shorter the charging time, the more likely it is to be reconnected
• Reconnection (Feeder)
If phase current after reconnection will be below a security margin
If charging point phase voltages higher than a security margin
An EV will be reconnected if its reconnection does not violate feeder constraints
Available capacity is given to EVs with lowest charging time
Calculations Control settings
Time-SeriesUnbalanced
DSSCOM
Every control cycle
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 38
ESPRIT-Based Control: Assessment
Inputs
– Real LV networks
– Realistic domestic* and EV load profiles
Probabilistic Assessment
– Monte Carlo approach (uncertainty)
– Time-series analysis (unbalanced)
– Metrics
• Thermal overloads
• Voltage issues (BS EN 50160)
3.568 3.569 3.57 3.571 3.572 3.573 3.574 3.575
x 105
4.0135
4.014
4.0145
4.015
4.0155
4.016
4.0165
4.017
4.0175
4.018
4.0185x 10
5 Low Voltage Network
(m)(m
)
Feeder 1
Feeder 2
Feeder 4
Feeder 6
Feeder 3
Feeder 5
Example: real UK LV network, 6 Feeders, 350 single-phase customers
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 39
(kV
A)
0
200
400
600
800
w/o control
1 min control cycle
(p.u
.)
Minimum Voltage Time of day
6h 8h 10h 12h 14h 16h 18h 20h 22h 24h 2h 4h 6h0.85
0.90
0.95
1.00
1.05
Tx Loading
Network Performance (100% EVs)
Tx 500 kVA350 customers
1-min control cycle Problems solved! (in theory)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 40
(kV
A)
0
100
200
300
400
500
Individual EV Demand Time of day
(kV
A)
6h 8h 10h 12h 14h 16h 18h 20h 22h 24h 2h 4h 6h0
1
2
3
4
Aggregated EV Demand
19:15h
17:44h
20:24h00:13h
23:04h
Effects on EV Demand
Most EVs are charged before 6am
Expected time: 160 min( 2:40h)
Actual time: 389min (6:29h)
Charging Delay: 143.13%
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 41
Customer Impact Level 0 1 2 3 4
Additional Charging Time (%) 0 1-25 26-50 51-75 76-100
Customer Impact Level (CIL)
0 1 2 3 4 5 6 7 8 90
10
20
30
40
50Impact Level 100% EV penetration
Impact Level
Pro
bab
ilit
y (
%)
Customer Impact Level 5 6 7 8 9
Additional Charging Time (%)101-125126-150151-175176-200 > 200
Half of the EVs are not affected
30% EVs required less than twice the original time
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 42
Key Remarks
Cost-effective solutions are needed to truly deploy intelligent approaches
– Use of limited information / infrastructure
– Attractive to network operators
Trials are crucial to capture the actual behaviour of EVs
– 200+ domestic EVs
Impact of control strategies on customers is crucial
– This can ensure early adoption of the technology
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 43
Active Management ofLV Networks
Dr Andreas Procopiou (Past PhD Student)
Research Gate
A.T. Procopiou, L.F. Ochoa, Voltage control in PV-rich LV networks without remote monitoring, IEEE Trans. on Power Systems, Mar 2017
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 44
Intelligent PV Inverters
Residential-scale PV inverters
– Already embedded with power control functions
– Embedded PV inverter communication interfaces
– Can be used in either centralised or decentralised control approaches to solve:
• Voltage issues (Voltage rise due to PV generation)
• Thermal issues (Due to reverse power flow)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 45
Intelligent PV Inverters
3. Volt-Var Control Function
2. Volt-Watt Control Function1. Active Power Limit Function
0 240 480 720 960 1200 14400.0
0.5
1.0
1.5
2.0
2.5
3.0
Time (minutes)
Activ
e P
ow
er
(kW
)
PV System without limit
PV System with 50% limit
Time(hh:mm)
P(W
) -
Q(V
Ar)
- S
(VA
) in
p.u
.
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1S (VA)
P (W)
Q (VAr)
Limited Q at high
generation periods
PV Inverter Power Capability
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 46
Control Approaches Investigated
Decentralised Voltage Control
– Volt-Var and Volt-Watt Control Functions
Centralised Thermal Control
– Active Power Limit Function
Centralised Thermal and Decentralised Voltage Control
– Active Power Limit Function
– Volt-Watt Control Function
16 real French LV residential networks
– Only one for this presentation
LV 00010 - Tx: 160kVA
Feeder 1(1.19km, 15 customers)
LV 00016 - Tx: 250kVA
Feeder 1(2.43km, 68 customers)
Feeder 2(0.35km, 1 customers)
Feeder 3(0.80km, 20 customers)
Feeder 4(0.58km, 3 customers)
LV 01527 - Tx: 250kVA
Feeder 1(0.95km, 71 customers)
Feeder 2(0.64km, 31 customers)
Feeder 3(1.01km, 31 customers)
LV 02779 - Tx: 400kVA
Feeder 1(0.66km, 28 customers)
Feeder 2(1.73km, 86 customers)
Feeder 3(0.46km, 41 customers)
Feeder 4(0.19km, 7 customers)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 47
Decentralised Voltage Control
V/V Curve 1
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 2
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 3
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 4
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 5
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 6
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 7
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 8
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 9
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 10
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 11
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 12
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 13
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 14
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 15
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 1
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 2
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 3
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 4
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 5
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 6
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 7
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 8
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 9
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 10
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 11
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 12
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 13
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 14
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 15
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 1
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 2
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 3
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 4
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 5
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 6
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 7
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 8
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 9
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 10
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
V/V Curve 11
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 12
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 13
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 14
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100V/V Curve 15
Voltage (p.u.)
% o
f availa
ble
VA
Rs
0.88 0.92 0.96 1 1.04 1.08 1.12-100
-80
-60
-40
-20
0
20
40
60
80
100
… …
V/W Curve 1
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 2
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 3
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 4
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 5
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 6
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 7
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 8
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 9
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 10
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 11
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 12
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 13
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 14
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 15
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 1
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 2
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 3
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 4
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 5
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 6
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 7
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 8
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 9
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 10
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 11
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 12
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 13
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 14
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 15
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 1
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 2
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 3
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 4
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 5
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 6
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 7
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 8
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 9
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 10
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
V/W Curve 11
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 12
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 13
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 14
Voltage (p.u.)
% o
f P
max
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100V/W Curve 15
Voltage (p.u.)%
of
Pm
ax
0.88 0.92 0.96 1 1.04 1.08 1.120
10
20
30
40
50
60
70
80
90
100
… …
• 15 Volt-Var Curves, from the most to least sensitive
• 15 Volt-Watt Curves, From the most to least sensitive
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 48
PV Penetration level (%)
Energ
y P
roduced (
kW
h)
Volt-Watt Curve Analysis - Energy Production
0 10 20 30 40 50 60 70 80 90 1000
1000
2000
3000
4000
5000
6000
Decentralised Voltage Control:Results
PV Penetration level (%)
# o
f N
on-C
om
pliant
Custo
mers
Volt-Var Curve Analysis - Non Compliant Customers
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
No Control
Curve 1
Curve 2
Curve 3
Curve 4
Curve 5
Curve 6
Curve 7
Curve 8
Curve 9
Curve 10
Curve 11
Curve 12
Curve 13
Curve 14
Curve 15
PV Penetration level (%)
Losses (
kW
h)
Volt-Var Curve Analysis - Losses
0 10 20 30 40 50 60 70 80 90 1000
100
200
300
400
500
Not significant
benefit
Losses Increase by ~30% each penetration
PV Penetration level (%)
# o
f N
on-C
om
plia
nt
Custo
mers
Volt-Watt Curve Analysis - Non Compliant Customers
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
No Control
Curve 1
Curve 2
Curve 3
Curve 4
Curve 5
Curve 6
Curve 7
Curve 8
Curve 9
Curve 10
Curve 11
Curve 12
Curve 13
Curve 14
Curve 15
Losses decrease
PV hosting Capacity is shifted to
100%
Volt-Var
#o
f cu
sto
mers w
ith
vo
ltag
e p
rob
lem
s
Volt-Watt
#o
f cu
sto
mers w
ith
vo
ltag
e p
rob
lem
s
En
erg
y l
osses
(kW
h)
En
erg
y P
rod
ucti
on
(kW
h)
32% Curt @100%
PV Penetration (%) PV Penetration (%)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 49
Volt-Var Control with10% over-rated inverters
PV Inverter Power Capability
Time(hh:mm)
P(W
) -
Q(V
Ar)
- S
(VA
) in
p.u
.
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
S (VA)
P (W)
Q (VAr)
PV Penetration level (%)
# o
f N
on-C
om
plia
nt
Custo
mers
Volt-Var Curve Analysis (10% Over-rated) - Non Compliant Customers
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
No Control
Curve 1
Curve 2
Curve 3
Curve 4
Curve 5
Curve 6
Curve 7
Curve 8
Curve 9
Curve 10
Curve 11
Curve 12
Curve 13
Curve 14
Curve 15
PV Penetration level (%)
Losses (
kW
h)
Volt-Var Curve Analysis (10% Over-rated) - Losses
0 10 20 30 40 50 60 70 80 90 1000
100
200
300
400
500
600
PV Penetration level (%)
Tx U
tilis
ation (
%)
Volt-Var Curve Analysis (10% Over-rated) - Tx Utilization
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
120
140
160
180
200
220
240
#o
f cu
sto
mers w
ith
vo
ltag
e p
rob
lem
s
En
erg
y l
osses
(kW
h)
Tx
Uti
lisati
on
Level
(%
)
Better Management
of Voltage Issues
Losses Increase
even more
Assets are overloading
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 50
Volt-Var control
– Normal Inverters - Not effective (limited Var capability)
– Over-rated Inverters – Effective
• Increases Loading of Assets
Volt-Watt control
– Effective (curtailment is required)
Decentralised Control
– Practical to implement (no additional infrastructure)
– Thermal issues are not addressed
Decentralised Voltage Control:Summary
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 51
Centralised Thermal Control
Control logic to manage thermal issues in LV networks
Uses only local measurements (P,Q, Irradiance)
Calculates a set-point to limit generation capability of each PV
Flat signal for all PV systems per feeder
RTU
20/0.4kV
Pyranometer
Transformer
Transmitter
MCU
Distribution Network Data Flow
Signal Receiver DevicePV Inverter
PV Generation Limit
Irradiance
Monitored Power
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 52
Centralised Thermal Control
Thermal Issues - Transformer and Feeder Utilisation Levels
Voltage Issues - # of Customers Energy Produced
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 53
Centralised Thermal Control:Summary
Thermal Issues are solved
Better utilisation of assets
Voltage issues are not directly addressed
Requires limited network information
– Local Measurements
– PV Installed Capacity
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 54
Combined Centralised Thermal and Decentralised Voltage Control
Volt-Watt Control (Decentralised)
Active Power Limit (Centralised)
To manage
thermal issues
To manage
voltage issues
RTU
20/0.4kV
Pyranometer
Transformer
Transmitter
MCU
Distribution Network Data Flow
Signal Receiver DevicePV Inverter
PV Generation Limit
Irradiance
Monitored Power
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 55
Combined Centralised Thermal and Decentralised Voltage Control
Thermal Issues - Transformer and Feeder Utilisation Levels
Voltage Issues - # of Customers Energy Produced
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 56
Optimal Voltage Management in MV-LV Networks
Mr Luis Gutierrez (PhD Student)
Smart Street Projectwww.enwl.co.uk/smartstreet ; Research Gate
L. Gutierrez, L.F. Ochoa, CVR assessment in UK residential LV networks considering customer types, IEEE/PES ISGT Asia 2016, Dec 2016
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 57
Management of Electric Vehicles
Smart Street Project
– General Idea
– Voltage control in LV and MV
– Energy Reduction (CVR)
Optimal Control Using OpenDSS
– Example, Coordination
Key Remarks
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 58
Smart Street Project
6 Primary Substations
• 11 MV feeders
• 7 MV capacitors
38 Secondary Substations
• 163 LV feeders
• 84 LV capacitors
• 5 LV OLTCs
• 80x3 LYNXs
• 163x3 WEEZAPs
~67,500 customers
www.kelvatek.com
First fully centralised MV/LV network management and automation system in GB
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 59
V
Smart Street Project
253
216
VV
X
LYNX
X
WEEZAP WEEZAP
Cap
Capacitors help to bring back V in highly loaded feeders
Interconnection helps flattening voltages
V
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 60
Voltage Control inMV and LV networks
Optimal Voltage Management
Spectrum
HV OLTC
MV OLTC
MV OLTC
WEEZAPs
LYNXLYNX
MV Cap
LV Cap
MV Breaker
WEEZAPs
Comms
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 61
Energy Reduction (CVR)
Lower energy bills
More LCTs
Lower voltages at customer sites
X
LYNX
X
WEEZAP WEEZAP
Cap
V
253
216
V
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 62
Optimal Control Using OpenDSS
SCADA
NMS
Optimisation
Engine
Interface
Real world
• Measurements• Current set points
• New set points• Control actions
• Measurements• Forecast (Loads+DG)
• Set points• Control actions
• 1 or 5 min res.
Power flow
• Solve optimisation
problem for
control purposes
in next control
cycle
• Calculates
impact metrics
• Produce
forecast
Modelling/Simulations
• Measurements• Current set points
• New set points• Control actions
• Measurements• Forecast (Loads+DG)
• Set points• Control actions
(SCADA) (Interface) (NMS Opt Engine)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 63
Optimal Control: Example
OPF-Based Centralised close to real-time NMS optimisation engine to minimise DG curtailment by actively managing voltages and congestion issues
– Measurements collected each control cycle (e.g., 5 min)
– Decisions to solve the seen network issues (Deterministic)
– Control Action finds the optimum set points
– OpenDSS-VBA-AIMMS
Distribution Network
Measurement (SCADA)
NMS Optimization Engine: Optimal setpoints for active
elements (OLTCs, DG)
Dis
trib
ution N
MS
New
setP
oin
ts (
SCAD
A)
Yes
No
Nom
Constraints violations
DG setpoints
Off Nom
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 64
Coordinated, HierarchicalOptimal Control
Coordination to achieve system-wide objectives
– Network areas?
– Hybrid optimisation?
• AC OPF
• DMPC
– Advanced rules?
– Control cycles?
– Interactions among voltage levels?
– Communication networks?
Master Controller
HV NMS HV NMS
LV NMS LV NMS LV NMS LV NMS
Setp
oin
ts
Control cycle ??
Control cycle ??
Control cycle ??
Control cycle ??
Control cycle ??
33kV
11kV
LV
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 65
Key Remarks
Observability is a currently barrier but soon to be overcome
– Cost, ICT aspects, data management
Complexity of solutions will increase with more flexibility
– The extent to which simple rules can be used is unknown (but it is preferred by the industry)
Coordination among solutions is key in BAU implementation
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 66
(Some) Conclusions
OpenDSS is a very flexible and comprehensive power flow engine
– Interfaces via COM server with Matlab, MS Excel VBA, Python, etc.
– Time-series three-phase power flows
– Models for network devices (OLTCs, switches), load models
– Models for new devices (DG units, storage, etc.)
OpenDSS can be used for sophisticated Smart Grid studies
– Minute by minute simulations, large number of nodes
– New technologies (e.g., PVs, EVs, wind, storage, etc.)
– Probabilistic studies (e.g., Monte Carlo)
– Optimisation studies (e.g., AIMMS-OpenDSS)
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 67
Technical Reports and Publications
Technical Reports (most publicly available):
https://sites.google.com/view/luisfochoa/publications/technical-reports
List of Publications (most publicly available):
Journal Papers
https://sites.google.com/view/luisfochoa/publications/journal-papers
Conference Papers
https://sites.google.com/view/luisfochoa/publications/conference-papers
© 2018 L. Ochoa - The University of Melbourne OpenDSS: Industry Applications, May 2018 68
OpenDSS: Industry Applications
IEEE PES Rio de Janeiro Chapter
SBSE 2018
Prof Luis(Nando) Ochoa
IEEE PES Distinguished Lecturer
Professor of Smart Grids and Power Systems
13th May 2018
SBSE 2018, Niteroi - RJ, Brazil