Optimal Settings for MultipleGroups of Smart Inverters on

Secondary Systems UsingAutonomous Control

Mobolaji Bello,Electric Power Research Institute (EPRI)

April 25, 2017

Outline

• Use case feeder model• Smart inverter functionalities• Autonomous vs integrated control• Load and solar variability index• Methodology to derive settings• Illustrate feeder response to settings• Show feeder impact from various

inverter settings

• Method to select appropriate settings in a very simple way• Focus is on volt-var settings!

Secondary (LV) Overvoltage Due to Residential Rooftop PV

• Impact at the Customer Level– Customers with PV fed from

same service transformer– Light-load conditions– Each PV system tends to increase

secondary voltage– Results in overvoltage on

secondary

Number of utilities have seen secondaryover voltages where PV customers are fedfrom same transformer

Number of utilities have seen secondaryover voltages where PV customers are fedfrom same transformer

Feeder XYZ OverviewSubstation Transformer

• 69kV – 12.47kV (Delta-Wye)• 25MVA• X1 = 8.98%

Voltage Setpoints

• 1.03pu @ 69kV bus• 1.01pu @ unregulated 12.47kV bus

Voltage Control

• 5 capacitor banks• No feeder regulator

Load Modeling• kW based on reported AMI data• Residential loads assumed 0.9 PF• Commercial loads assumed 0.85 PF

XYZ Loading and Fault Levels

Fault levels• 3 ph. = 11953A• 1 ph. = 12635A

Fault levels• 3 ph. = 11953A• 1 ph. = 12635A

Peak PV• on 2016-04-25 @ 12:00:00• total PV is 2737.310kW and the net load is -307.537kW• total load is 2429.773kW and the net load is -307.537kW

Most back fed• on 2016-04-28 @ 12:00:00• total PV is 2690.920kW and the net load is -538.490kW• total load is 2152.430kW and the net load is -538.490kW

Heavy load• on 2016-06-20 @ 17:00:00• total PV is 775.200kW and the net load is 9102.193kW• total load is 9877.393kW and the net load is 9102.193kW

Fdrname

Loadingcondition Abbr kW

XYZ

Peak (PV-PV1,Net load-NL1,Total load-TL1)

PV1 2700NL1 -300TL1 2400

Most back fed(PV-PV2,Net load-NL2,Total load-TL2)

PV2 2650NL2 -500TL2 2100

Heavy load(PV-PV3, Netload-NL3,Total load-TL3)

PV3 770NL3 9100TL3 9900

Smart inverters

allows the powerfactor to be set at afixed value

Volt-Watt: Limits of Operation

allows the DER to manage poweroutput based on voltage

′ ′′1.0 pu

Power( )

Voltage( )

% A

vaila

ble

VARS

(Q)

Indu

ctiv

eCa

paci

tive

100%(nominal voltage)

Q = 0System Voltage

Dead Band

allows the DER to manage its own reactivepower output in response to local service voltage

Volt-VAR Example Characteristic

Autonomous vs integrated control

Distributionoperator

Substations

Feeders

Integrated control: MV node• Typical DERMS approach• Based on electrical proximity to group,

monitor and measure• Operation and coordination all the SI is key.

Autonomous: inverter node• Inverter measurement is based on

inverter node voltage!

Autonomous InfrequentCommunication

FrequentCommunication

Remote ON/OFF

Power Factor Control (can be schedulebased)

Volt – var (less effective) (more effective)

Volt – Watt

Communication Needs for Grid Support Functions

Project goal was to useAutonomous system !

Categories for Daily Variability Conditions: Sandia’s VI and CI applied

Clear Sky POA IrradianceMeasured POA Irradiance

Clear Sky POA IrradianceMeasured POA Irradiance

HighHigh

ModerateModerate

MildMildOvercastOvercast

ClearClear

VI < 2CI ≥ 0.5

VI < 2CI ≤ 0.5

2 ≤ VI < 5

5 ≤ VI < 10

VI > 10

Variability Conditions: AZVariability Conditions: AZ

Variability Conditions: NMVariability Conditions: NMVariability Conditions: NJVariability Conditions: NJ

Q2 2012 Q3 2012 Q4 2012 Q1 20130

20

40

60

80

100

Perc

enta

ge o

f Day

s (%

)

Season

Variability Conditions: TNVariability Conditions: TN

Q2 2012 Q3 2012 Q4 2012 Q1 20130

20

40

60

80

100

Perc

enta

ge o

f Day

s (%

)

Season

Q2 2012 Q3 2012 Q4 2012 Q1 20130

20

40

60

80

100

Perc

enta

ge o

f Day

s (%

)

Season

Q2 2012 Q3 2012 Q4 2012 Q1 20130

20

40

60

80

100

Perc

enta

ge o

f Day

s (%

)

Season

“PV Measures Upfor Fleet Duty” –IEEE Power/EnergyMarch/April 2013Credit: SANDIA

Best Settings Analysis Framework

0 5 10 15 20 25

1.024

1.026

1.028

1.03

1.032

1.034

1.036

1.038

1.04

1.042

1.044

Hour

Vol

tage

(pu)

Voltages with different voltvar settings

---- Voltvar

---- No PV---- PV base

What is the best voltage response?

Depends on what objective you want to achieve.

Discrete voltagechanges are due tocapacitor switching orinverter status change.

Deta

iled

feed

er m

odel

Blue lines indicatevoltage response usingdifferent volt-varsettings.

1a. Performance Objective

Benefits

Site

Efficiency Power Quality Asset Life Deference ofCapital Spending Reliability Enabling

Redu

ced

dist

ribu

tion

line

loss

es

Impr

ove

cust

omer

effi

cien

cy C

VR

Flat

ter v

olta

ge p

rofil

e

Impr

oved

har

mon

ics

Volt

age

flick

er

Ove

rvol

tage

Redu

ce LT

C ta

p ch

ange

s

Redu

ce li

ne r

egul

ator

tap

chan

ges

Redu

ce s

witc

h ca

p ch

ange

s

Def

er c

apac

itor a

ddit

ions

Def

er li

ne r

egul

ator

s

Def

er re

cond

ucto

ring

Def

er s

ubst

atio

n up

grad

es

Supp

ort d

urin

g m

omen

tary

Supp

ort d

urin

g au

tom

atio

n

Hig

her

Pene

trat

ion

of P

V

Network Deta

iled

feed

er m

odel

1a. Key performance objective for XYZ

• MeanPCCv – average PV interconnectvoltage

• Voltage Variability Index – Voltagevariability index at the inverter terminal

• Consumption – End-use consumption inkWh

• Losses – Total feeder losses in kWh

• Time Above – Seconds that any point onthe feeder is above 105% nominal

• Time Below – Seconds that any point onthe feeder is below 95% nominal

• Max V – Maximum voltage at any point onthe feeder

• Min V – Minimum voltage at any point onthe feeder

• Difference btw Feeder-wide Max and Mini.e. flattened voltage profile (Vdiff.)

Metric based on primary voltage Deta

iled

feed

er m

odel

1b. Detailed feeder model

• Model prep and data cleaning up is huge!• Get a snap shot for the real peak load• Load shape must reflect values for at least one year

Deta

iled

feed

er m

odel

1c. Detailed feeder model – Smart Inverter location

Autonomous system analyzed!

Feeder XYZ

S.Inv Units 48Size(kVA) 6PVs (all units) 512

Deta

iled

feed

er m

odel

Deta

iled

feed

er m

odel

Powerfactor

Voltwatt

Vot-var

• OpenDSS used to take advantage of smart inverter models!• Simulation is conducted at the minute resolution for a 24 hr

period in each combination of scenario.• With the inverter settings in, then, crunching can start ..…

• OpenDSS used to take advantage of smart inverter models!• Simulation is conducted at the minute resolution for a 24 hr

period in each combination of scenario.• With the inverter settings in, then, crunching can start ..…

1d. Simulated Inverter Settings

2. Smart inverter modeling

An automated routine for thousands of analyses. Takes a while becauseof the total number of runs and combinations considered………

Run

quas

i sta

tic ti

me

serie

s mod

el

3. Data processing for performance metrics (load/solar probability)

Determine peak tooff-peak ratioBased on one yr SCADA data

Feeder head identified that midday feeder load level is 30% ofthe time.

• Weighted average of days considered over one year• To examine multiple feeder impacts simultaneously• Weighted average of days considered over one year• To examine multiple feeder impacts simultaneously

Proc

ess r

esul

ts

Process Network Response (illustration)Optimized PerformanceMetric• Black dashed line

indicates rank based onperformance metric

• Trends because controlsettings utilized differentxtics involving set points,bandwidths and slopes

Voltage Constraints• Blue circles indicate no

voltage violations• Green circles indicate

improvement in voltagebut still has someviolations

• Red circles indicateadditional voltageviolationsDifferent Control Settings

(110 unique settings analyzed)

Best

Worst

Proc

ess r

esul

ts

XYZ- Control settings

187 unique settings analyzed187 unique settings analyzed

Sele

ct b

est s

ettin

g

VdiffTime belowANSI

Losses VoltageVariabilityIndex

Recommended settings (Voltvar) –XYZ

Sele

ct b

est s

ettin

g

How much improvements did these provide? XYZ

depends on feeder characteristicsdepends on feeder characteristics

Sele

ct b

est s

ettin

g

Variable day_peak

Variable day_offpeak

Overcast day_peak

Overcast day_offpeak

Clear day_peak

Clear day_offpeak

Recommended Control Settings…XYZ

depends on solar profile

Sele

ct b

est s

ettin

g

Recommended Control Settings…

Performance objective

Recommended Control Settings…

Seasonal Variations

Summary

• Methodology can be applied to determine best recommended settings• There is a unique feeder impact for each of the different control types and

settings.• Periodical updating of settings can be beneficial• Best inverter settings are dependent on

– Performance metric (losses, voltage difference, time below ANSI, VVI)– Feeder characteristics– Load and solar condition (peak load, variable solar; off peak load, clear solar

etc.) Solar and load measurements could be used to automatically update the inverter

settings to best correlate with the current field conditions Communication with the inverters can be used to update settings based on

operator command or automatically based on SCADA measurements.– PV size Total smart inverters on the circuits are needed to see more feeder impact and

effect!

Jeff Smith

jsmith@epri.com

865.218.8069

Mobolaji Bello

mbello@epri.com

865.218.8005

Ben York

byork@epri.com

865.218.8187

Davis Montenegro

dmmartinez@epri.com

865.218.8091