opendss: industry applications

© 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

[email protected]

13th May 2018

SBSE 2018, Niteroi - RJ, Brazil



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


The University of Melbourne, Australia

Professor of Smart Grids (Part Time)

The University of Manchester, UK

[email protected]

https://sites.google.com/view/luisfochoa

https://sites.google.com/view/luisfochoa


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

http://www.enwl.co.uk/lvns

http://www.myelectricavenue.info/

https://www.researchgate.net/project/Active-Management-of-LV-Networks

http://www.enwl.co.uk/smartstreet


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

http://www.enwl.co.uk/lvns

https://www.researchgate.net/project/Low-Voltage-Network-Solutions


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


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


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)


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


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


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


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


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



Average profile

0 50 100 150 200 250 3000

0.5

1

1.5

2

2.5

3


[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



Average profile

0 50 100 150 200 250 3000

0.5

1

1.5

2

2.5

3

3.5


[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


* The University of Manchester, “The Whitworth Meteorological Observatory.”[Online]. Available: http://www.cas.manchester.ac.uk/restools/whitworth/.

Realistic Load Profiles*



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


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


Voltage [

V]

Voltage at the last customer

Load+PV

Load

Load+PV

Load

Time-SeriesUnbalanced

Power Flows


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


Curr

ent

[A]

Summer Loads

+PVs

0 50 100 150 200 250 300230

235

240

245

250

255

260


Voltage [

V]

Summer Loads

+PVs

Extract results&

Analysis


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


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


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


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


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


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

http://myelectricavenue.info/

https://www.researchgate.net/project/My-Electric-Avenue-2



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


Control of EV Charging PointsGeneral Idea

Computer

100%

Challenges:

• Charging behaviour of EVs

• Modelling of customers


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


Geographical Extent of the Trial

112 Social Trials

109 Technical Trials

221 in total


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


Transformer

11/0.4 kVPLC

Substation

Infrastructure Overview

ROLEC* charging point

+EA TechnologyIntelligent Control Box

Real 500 kVA Transformer

* http://www.rolecserv.com/


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


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


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



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.


EV management Using OpenDSS

Random assignation of variables


DSSCOMInitialization

(Similar to the Impact

Assessment)

Control

Calculations Control settings


DSSCOM

Every control cycle

MATLAB is also used to extract monitors, assess, and visualise the results


Transformer

11/0.4 kVPLC




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)



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



DSSCOM

Every control cycle


Transformer

11/0.4 kVPLC


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



DSSCOM

Every control cycle


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


(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)


(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%


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


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


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

https://www.researchgate.net/project/Active-Management-of-LV-Networks


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)


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


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





LV 01527 - Tx: 250kVA




LV 02779 - Tx: 400kVA






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


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


# 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


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


# 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 (%)


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)


# 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


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


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


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



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



Thermal Issues - Transformer and Feeder Utilisation Levels

Voltage Issues - # of Customers Energy Produced


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


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


Combined Centralised Thermal and Decentralised Voltage Control

Thermal Issues - Transformer and Feeder Utilisation Levels

Voltage Issues - # of Customers Energy Produced


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

http://www.enwl.co.uk/smartstreet

https://www.researchgate.net/project/Smart-Street-2



Smart Street Project

– General Idea

– Voltage control in LV and MV

– Energy Reduction (CVR)

Optimal Control Using OpenDSS

– Example, Coordination

Key Remarks



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


V


253

216

VV

X

LYNX

X

WEEZAP WEEZAP

Cap

Capacitors help to bring back V in highly loaded feeders

Interconnection helps flattening voltages

V


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


Energy Reduction (CVR)

Lower energy bills

More LCTs

Lower voltages at customer sites

X

LYNX

X

WEEZAP WEEZAP

Cap

V

253

216

V


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)


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


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


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


(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)


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

https://sites.google.com/view/luisfochoa/publications/technical-reports

https://sites.google.com/view/luisfochoa/publications/journal-papers

https://sites.google.com/view/luisfochoa/publications/conference-papers


OpenDSS: Industry Applications

IEEE PES Rio de Janeiro Chapter

SBSE 2018


IEEE PES Distinguished Lecturer


[email protected]

13th May 2018

SBSE 2018, Niteroi - RJ, Brazil

opendss: industry applications

Documents