Distribution System Analysis for Smart Grid

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



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



Smart Grid Features

• Distributed Resources

– Generation

– Renewable Generation

• Variable sources

– Energy Storage

– Demand Response



Smart Grid Features, cont’d

• Communications and Control

– AMI deployed throughout the system

– High-speed communications to Metering and Controls

– State Estimation



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?



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



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



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



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



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?



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



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



Solar Ramping

Basic Solar Ramp Function

Regulators compensate for drop

Voltage Pushed over limit on recovery



Regulator Response for Series of Cloud Transients

Regulator Operations



“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??



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


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


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


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


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



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



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.



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



Load Shapes With and Without Storage

Mode=Peak Shave, Target=8000 kW, Storage=75 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

10

20

30

40

50

60

70

80

Base kW

Net kW

kWh Stored

Simple Substation Peak Shaving




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




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)



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



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



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



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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Distribution System Analysis for Smart Grid

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