Integration of DER: Advanced Modeling and Simulation · © 2016 Electric Power Research Institute,...

© 2016 Electric Power Research Institute, Inc. All rights reserved.

Jeff Smith, ManagerPower System Studies

[email protected]

Matthew Rylander, Technical LeaderPower System Studies

[email protected]

Mobolaji Bello, Engineer IIIPower System Studies

[email protected]

Integration of DER: Advanced Modeling and

Simulation

P174A 2016 Fall Advisory Meeting

September 19, 2016

2© 2016 Electric Power Research Institute, Inc. All rights reserved.

Outline

Introduction: Past and PresentDER Impact Assessment (Hosting Capacity)

– Development of Methods– System-wide Methodology Functionality ImplementationDemonstrationEnhancement

Smart Inverter SettingsProtectionMember Roundtable


Power System Studies Team – Modeling and SimulationWe’ve grown in the past year

Jeff [email protected]

Matt Rylander, [email protected]

Roger [email protected]

Wes [email protected]

Huijuan Li, [email protected]

Alison O’Connell, [email protected]

Jouni Peppanen, [email protected]

Davis Montenegro-Martinez, [email protected]

Mobolaji [email protected]

Jason Taylor, [email protected]


Power System Studies Team – Modeling and Analysis

Integration of DER

Distribution Planning

Storage

Power Quality

Distribution Operations

Transmission


Overview of Project Set


Program 174 – Integration of DERProject Set A – Modeling and Analysis

P174A Overview: Analytics Mitigation Tools Training

Specific Topics: Hosting Capacity Smart invertersTarget Audience: Distribution planners Interconnection engineers Resource planners

Smart Inverters

Hosting Capacity

Close coordination with P94, P173, P1, 180A/F (P200 in

2017)


Advanced Modeling and Analysis of DER

Grid Grid

No changes Specific changes to serve load and DER

DER DERLimited to no control Increased control (watts

and vars)

Objectives ObjectivesAccommodate DER without changing distribution

Least-cost solutions for incorporating DER into distribution operations

Outcomes OutcomesBaseline hostingcapacity, no changes to distribution performance

Increased hosting capacity, improved distribution performance

DER var control

DER curtailment

Grid-side changes to voltage control

Protection changes

Additional voltage/var control (grid-edge control)

Storage

Customer load control

Means for Integrating DER

From Accommodating DER to Integrating DER

Accommodating Integrating


Key Highlights from the Past (EPRI-P174A)

Hosting Capacity Method Dev

Planning Limits with DG

PV Hosting Capacity Analysis > 35 feeders

Smart Inverter Impact Assessment Recommended Settings for Smart inverters

Streamlined Hosting Capacity

Method Dev

Commercial Tool Implementation of

Streamlined Hosting Method

Cost/Benefit Analysis

(Few Feeders)

Cost/Benefit Analysis

(System-wide)

DGScreener App

Methods

Analysis

Tools

Mitigation

2010 2015

PV Impacts

Training OpenDSS/ DER Integration Workshops

New Screening Methods

Modeling of smart inverters


Modeling and Analysis Topics

Analytics• Screening• Hosting Capacity• Reliability• DER/Grid Modeling

Mitigation• Smart inverters• Grid-side enhancements

Tools• Advancing commercial

tools• Open-source (OpenDSS)

Training/Education• Engineering Guidelines• Planning with DG


Modeling and Analysis - Analytics






Hosting Capacity is the amount of DER that can be accommodated on

a given feeder without impacting reliability or power quality.

After observing all issues and locations on a feeder, how much DER that can be

accommodated is different based on many factors including location.


Modeling and Analysis - Mitigation






Without smart invertersWith smart inverters

Increase in hosting capacity with smart inverters

In many cases, use of smart inverters can be the least-cost solution for

integration issues


Modeling and Analysis - Tools






Incorporating hosting capacity methods into existing utility planning

tools – no need to re-invent the wheel

Existing Distribution

Planning Tools(CYME, Windmil,

Synergi, PowerFactory, GridLab-D, DEW)

EPRI Hosting Capacity Module


Modeling and Analysis - Training

Providing utilities with onsite training for distribution

engineersAnalytics• Screening• Hosting Capacity• Reliability• DER/Grid Modeling






Modeling and Analysis of DER – Recent Projects


174A: 2016 Deliverables

Project Details

Implementation of Streamlined Hosting Method in Commercial Planning Tools

Application of new methods in existing planning tools*

Advancement of Hosting Capacity Methods in Planning ToolsInclusion of existing DER, smart inverters, portfolios of DER

Demonstration of Improved DER Screening Through Hosting Capacity Method

Use cases for system-wide Hosting Capacity analysis

Solar PV Integration Using Advanced Inverters Recommended settings

Modeling of PV Inverters for Protection Studies Development of models for protection studies1

Aggregated Substation-Level Impacts of Multi-Feeder PV Penetration Impacts beyond the feeder

Comparing the cost benefit of guided vs. unguided PV deployment Locational impacts and economics2

Examining the Effects of Customer-Sited Solar+Storage on Distribution Energy storage in the portfolio3

Understanding PV Market Potential for Distribution Planning Distribution impacts vs. adoption2

* CYME Implementation

Focus of Today’s Discussion

Joint Deliverables1- 180A2- 174D3- P94


DER Hosting Capacity

Development Functionality Implementation

Demonstration Enhancement


What is Hosting Capacity and Why is it So Important?

Definition: – Hosting Capacity is the amount

of DER that can be accommodated without adversely impacting power quality or reliability under current configurations and without requiring infrastructure upgrades.

Hosting Capacity is– Location dependent– Feeder-specific– Time-varying

Hosting capacity considers DER interconnection without allowing– Voltage/flicker violations, – Protection mis-operation– Thermal overloads– Decreased safety/reliability/power quality

Hosting Capacity can be used to inform utility interconnection

processes and to support DG developer understanding of more

favorable locations for interconnection

Develop original method

Utility application

Streamlined method

development

Utility application

Vendor implementation

2010 2011 2014 2015 2016/17

Evolution of Hosting Capacity Methodology


Detailed Implementation of Hosting Capacity Assessment

Method Overview Select specific locations for DER “Iterate” through each case Solve 1000’s of load flows

Analysis of High-Penetration Solar PV Impacts for Distribution Planning: Stochastic and Time-Series Methods for Determining Feeder Hosting Capacity. EPRI, Palo Alto, CA: 2012. 1026640

Findings Results similar to detailed impact studies

– Accurate– Time-consuming/data intensive– Applicable to specific scenarios

Difficult to consider range of possible DER scenarios

– All locations (three-phase and single-phase)– Feeder reconfigurations– DER types

Not easily replicable across entire system– Typically have to limit the

cases/locations/scenarios considered– Can take hours to days to simulate a single

feeder depending upon feeder complexity


Modeling Requirements for Hosting Capacity Assessment

Depth

Increasing

granu

larity

BreadthIncreasing number of feeders

Few feeder models: highly detailed for demonstration projects

Portion of feeders modeled: sufficient for traditional planning

Most feeders modeled:sufficient for traditional planning

No feedermodels

Simplified Screening

Det

aile

d H

ostin

g C

apac

ity



Method Overview System-wide application Independent of modeling Feeder-level assessment Easily available data used

Findings Best feeder data is not used in assessment

– Electrical-based data– Model-based response of multiple

characteristics

No range in DER scenarios considered

Easily replicable across entire system– Over and under conservative results


Modeling Requirements for Hosting Capacity Assessment

Depth

Increasing

granu

larity

BreadthIncreasing number of feeders

Few feeder models: highly detailed for demonstration projects

Portion of feeders modeled: sufficient for traditional planning

Most feeders modeled:sufficient for traditional planning

No feedermodels


Streamlined Hosting Capacity

Det

aile

d H

ostin

g C

apac

ity


Streamlined Implementation of Hosting Capacity Assessments

Method Overview Solve base load flow/short-

circuit cases Increase DER at each location

on feeder Apply advanced algorithms to

calculate hosting capacity at each location

Baseline Power flow/short‐circuit

Select DER location

Increase DER

Apply Power System Criteria

Hosting Capacity Limit?

Y N

Findings Close approximation of DER impact

– Less time/data intensive– Not a replacement for detailed studies

Full range of possible DER scenarios can be considered

– All locations (three-phase and single-phase), feeder configurations, DER technologies and types (centralized vs distributed)

Easily replicable across entire system– Typically 3-5 minutes per feeder when

automated

Integration of Hosting Capacity Analysis into Distribution Planning Tools. EPRI, Palo Alto, CA: 2016. 3002005793


Functionality




Key Aspects of Hosting Capacity Method

Power System Criteria

Thermal

Substation transformer

Primary conductor

Service Transformer

Secondary Conductor

Power Quality/Voltage

Sudden (fast) voltage change

Steady‐state voltage

Voltage regulator impact

Load tap changer impact

Protection

Relay reduction of reach

Sympathetic tripping

Element fault current

Reverse power flow

Reliability/Safety

Unintentional islanding

Operational flexibility

DER Size and Location

Unique DER

Technology

Feeder Impacts


Example: Solving Primary Overvoltage Hosting Capacity

Volta

geOvervoltage Impact Threshold

DER

Si

ze/L

ocat

ion

Location from Source


Hosting Capacity Example

Node 6aNode 1a Node 2a Node 3a Node 4a Node 5a

a Node Hosting Capacity is dependent on DER at other nodes. That shown above is based on DER only at the specified Node.b Feeder Hosting Capacity is the Maximum/Minimum range of Node Hosting Capacity on the feeder.c Substation Hosting Capacity represents the Minimum of the Feeder Hosting Capacities.

Feeder 1b (Max / Min)

Substation



Substation 1c (Min)


Threshold

Threshold

* Detailed Method

Validation

* Detailed Method


Implementation




Two Components Required

Interface to planning tool– This interface extracts the necessary data out of the planning tool and

models– Custom for each planning tool (CYME, Synergi, Milsoft, DEW, PVL,

Powerfactory, etc) Solution engine

– Performs the actual hosting capacity calculations– DRIVE “Distribution Resource Integration and Value Estimation Tool”

CYME

Synergi

Milsoft

Others…

Vendor software

Model Interface tool

CYME

Synergi

Milsoft

Others…

Compatible with all tools

DRIVE Core Engine

DRIVE: “Distribution Resource Integration and Value Estimation Tool”


Implementation To Date

Planning Tool Interface Hosting Capacity Solution Engine

CYME To be available this year through CYME

To be available this year through CYME

Synergi Developed with support and available to Level 3 funders of supplemental project*

DRIVE v1.0 available now through membership in Program 174A**

Milsoft Under development and will beavailable to Level 3 funders of supplemental project*


PowerFactory Under development and will beavailable to Level 3 funders of supplemental project*


GridLab-D Interface being developed by National Grid


Others… DRIVE v1.0 available now through membership in Program 174A**

*Supplemental project: Ongoing project to develop customized utility vendor planning tools for hosting capacity tool

**2016 Q3 Software Deliverable: Implementation of Streamlined Hosting Method in Commercial Planning Tools


Implementation Roadmap in NY

Stage 1 –Distribution Indicators

Stage 2 –Hosting Capacity Evaluations

Stage 3 –Advanced Hosting Capacity Evaluations

Stage 4 –Integrated DER Value Assessments

Defining a Roadmap for Successful Implementation of a Hosting Capacity Method for New York State, EPRI,

Palo Alto, CA: 2016. 3002008848

Increasing effectiveness, complexity, and data requirements


Hosting Capacity Implementation RoadmapStage Consideration Data Requirements Output

1 – Distribution Indicators Possible indicators such as- Estimated Minimum load

levels- Voltage class- Substations over a MW

threshold typically indicative of substation backfeed

- Currently available data- Understanding the

interconnection queue

- Provides an indication where certain substations/feeders may have high costs associated with interconnecting DER

2 – Hosting Capacity Evaluations – Radial Systems

- Feeder-level hosting capacity calculations based on power system impactevaluations

- Impact factors include voltage, thermal, and protection, safety/reliability

- All feeders modeled in service territory with regular updates for existing DER and queued DER mapped into planning models

- Feeder-level hosting capacity determinations

3 – Advanced Hosting Capacity Evaluations

- Refined nodal/section-based hosting capacity

- Possiblesubstation/transmission constraints

- Operational and planning flexibility for changing configurations

- Transmission assessments and mapping of distribution-level impacts to transmission

- Normal and reconfigured system models

- Refined hosting capacity evaluations that take into account additional criteria

4 – Fully Integrated DER Value Assessments

- Deferred or avoided planned capital upgrades

- Improve system efficiency- Enhanced power quality,

reliability, and resiliency

- Increased level of detail regarding distribution constraints, asset performance, and DER performance metrics

- Comprehensive hosting capacity and DER value assessments considering both distribution and transmission


Demonstration




Demonstration

Part 1 – Interface to Utility Model– Synergi Solver application– Synergi Standalone GUI application

Part 2 – Hosting Capacity Analysis


Centralized Large DERSystem View of 11 SubstationsSubstation Hosting Capacity reflects worst feeder connected

Hosting Capacity for Thermal and Voltage

*Initial analysis results from TVA/AEC, results not finalized

Thresholds:Thermal – 100% normal ratingVoltage – 3% change

*Hosting Capacitylower

higher

Hosting capacity can change when considering additional

issues.


Hosting Capacity for Thermal and Voltage

Centralized Large DERSubstation View of 9 FeedersFeeder Hosting Capacity reflects worst node connected

Not all feeders served from the same substation have limited

hosting capacity.

Thresholds:Thermal – 100% normal ratingVoltage – 3% change

*Hosting Capacitylower

higher

*Initial analysis results from TVA/AEC, results not finalized


Demonstration of Improved DER Screening Through Hosting Capacity Method

The complexity of future DER scenarios require improved screening studies.

Scope• Demonstrate how EPRI’s DRIVE module

can be used for screening new interconnection requests.

Value• Evaluates current interconnection

requests• Allows for the planning of future DER

scenarios• Provides thorough validation of DRIVEDelivery Type / Date• Technical Brief, Q4


Enhancement




Enhancement of Hosting Capacity Methods in Planning ToolsEPRI’s DRIVE method expanded with additional functionality.

Scope• Incorporate methods for dealing with

• Existing DER• Advanced inverters

Value• Provide members with updated

methods that consider additional details that can impact hosting capacity.

Delivery Type / Date• Technical Brief, Q4


Enhancements

Existing DER

Calculate remaining hosting capacity

Customer Mitigation

Advanced inverters to increase distribution hosting capacity

Storage

Use-case impacts on hosting capacity and its value to the grid

Utility Mitigation

Power delivery and control adjustments to increase distribution hosting capacity

Value Assessments

Such as:• DER as a non-

wires alternative to upgrades

• Locational benefits

• Optimal settings


Existing DER Inclusion of Existing DER allows the “Remaining” Hosting Capacity to be determined.

0.5 MW added

Remote node impacted as well

Baseline Hosting Capacity Remaining Hosting Capacity


Utility Mitigation Utility control and power delivery elements have impact on hosting

capacity.

Substation LTC: 123V

Substation LTC: 125VSubstation


Customer Mitigation Customer DER settings and control have impact on hosting

capacity.

DER with Unity Power Factor

DER with Inductive Power Factor


Methods to Determine Recommended Advanced Inverter Settings

Improving Distribution Integration of PV


Solar PV Integration Using Advanced InvertersEnabling the full value of advanced inverters on the distribution system requires knowing the appropriate settings.

Scope• Determine recommended

settings/methods to improve system performance with advanced inverters.

Value• Provide widely applicable settings when

appropriate.• Provide methods to calculate settings

when settings are situationally dependent

• Methods use existing planning tools/data

Delivery Type / Date• Technical Update, Q3

Without smart invertersWith smart inverters

Increase in hosting capacity with smart inverters

In many cases, use of smart inverters can be the least-cost solution for

integration issues


Industry Landscape: New Distribution Resources

Advanced inverters can improve integration of DER by reducing some of the adverse impacts from DER.

Mitigate voltage issuesProvide least-cost solution Increase hosting capacity

Primary Voltage

0.9

0.925

0.95

0.975

1

1.025

1.05

0 4 8 12 16 20

Hour

Volta

ge (p

u)

Baseline – No PV

20% PV20% PV withvolt/var control

24 Hour Simulation


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)

---- Voltvar

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

Industry Challenge: How to Take Advantage of the Resources

There are numerous possible inverter settingsWrong settings can actually worsen grid performance

Blue lines indicate voltage response using

different volt-var settings.

Discrete voltage changes are due to capacitor switching or inverter

status change.


Location Does Matter

High X/R ratio High short-circuit strength DER doesn’t move voltageReactive power control not needed

High X/R ratioLow short-circuit strengthDER moves voltageReactive power control is effective

Low X/R ratioLow short-circuit strengthDER moves voltageReactive power control is not as effective

Substation

DER

Region A

Region B

Region C

Var control may not be needed due to minimal voltage rise caused by DER

Var control most effective due to high X/R

Var control not as effective due to low X/R

DER

DER

Single DER System and Location

End of feeder


Level Complexity Power Factor Volt-var Volt-watt

0 None Unity Power Factor Disabled,Unity Power Factor

Disabled,Unity Power Factor

1 Low Based on Feeder X/R Ratio Generic Setting Generic Setting

2 MediumBased on Feeder

Model and PV Location

Based on Feeder Model and PV

LocationNot Applied

3 HighBased on Feeder

Model and PV Location

Based on Feeder Model, PV Location,

and Service TransformerImpedance

Not Applied

Solution: Methods to Derive Inverter Settings


Power Factor Control

Level 1 settings:– Simple method required to determine settings– Setting is feeder specific, one power factor setting per feeder– Setting is based on the Mean X/R ratio on the feeder

Level Method Data Requirements Power Factor Setting

1Mean X/R Ratio of all 3-phase MV nodes to determine power factor

Primary node X/R ratios on feeder, number of phases at each node

Single Setting on each feeder


Volt-var Control

Level 1 settings*: – Wide bandwidth (does nothing when within 2% from nominal)– Maximum reactive power output equivalent to 90% power factor when

real power is at full output (assumed that the inverter is 10% larger than the PV system rating)

Same settings as proposed within P1547 WG. Analysis performed here confirmed effectiveness and no adverse impact


High-Level Key Takeaways for Distribution-Focused Inverter Settings Inverter “headroom” for providing reactive power control at full

output is criticalMany settings are feeder and/or location dependentOptimizing smart inverter settings can be complex Simplified approaches for determining appropriate settings have

been developed and can be applied Default settings can be found that improve integration of PV


Fault Current Modeling of PV

Joint Project with Distribution Planning (180A)

Mobolaji Bello, Roger Dugan


Tested additional islanding scenarios, e.g. fault current contribution, islanding, open conductor, etc. Tested additional inverter

models to establish a representative sample Developed inverter

models with verified data. New inverter libraries

available in OpenDSS.

2016 Effort 2014 Tech update

2015 Technical Update

Incorporate test results into the model to predict system response.

Lab Testing

Develop OpenDSS Model

Finalize lab testing

2016 Technical Update

Incorporate Txmodeling efforts*

In collaboration with P173 (Bulk Renewables)


Inverter Testing

Model Phases kW

Enphase M215 (18) 3 (208 V) 4.05

Eaton PV 250 3 (208 V) 5.0

Schneider Conext TX 2800 NA 1 (240 V) 2.8

Power One UNO-2.0.1-OUTD-S-US 1 (240 V) 2.0

Fronius IG2500-LV NEG 1 (208 V) 2.0

Solectria PVI-3000 1 (240 V) 3.0

SMA SB-3000TL-US-22 1 (240 V) 3.0

3-Phase Fronius Inverter from EPRI 3(208 V)


Testing and modeling results (Fault at Inverter terminals)

Eight inverters tested are clustered into categories of behavior

Fronius Three-Phase


Testing and modeling results (Fault on Transmission)Fault Ride-Through

Remaining energized

Inverters have different fault responses during voltage sags as well


The Hammerstein-Wiener framework

Step Response of Linear Block

Piecewise Linear Function representing output nonlinearity

Piecewise Linear function representing

input nonlinearity

Piecewise Linear function representing

input nonlinearity

separates linear dynamics and non-linearities in the inverter model.

X Y X Y

Table for Piecewise Linear Expressions

Table for Piecewise Linear Expressions

W(t) x(t) y(t)u(t)

Z-1

transfer function expression


• Transient testing, non-linear dynamic modeling approach

• HW: models a SISO system by breaking it into in/output nonlinearity, via linear “Z-1”

• MATLAB Toolbox comes with HW functions, with limited scale.

• Used PV inv transient data (lab) to ID HW transient models, then feed to OpenDSS.

• 5/10 kHz filter freq = sampling rate of power quality monitor used to collect lab data.

• Transient testing, non-linear dynamic modeling approach

Hammerstein-Wiener model in Discrete Time Domain

o Inject a u(t) waveform, into the input nonlinearity, BP1.

o Running inside OpenDSS, u(t) is generated from the most recent

phasor voltage solution.

o Pre-event inverter power output is a fixed input parameter (not shown)

Filter order ranges from 4 up to the MATLAB limit of 52

o BP2 output, y(t), is a current waveform injected by the inverter

into the grid.

o I (RMS) calculated as shown, (assume pf=1), and inject that

current into OpenDSS as a voltage-controlled current source (VCCS).


In OpenDSS

Available as a new element (v7.6.5_18)

OpenDSS runs at a 1-ms step to 10-ms for a phasor-dynamic solution.

Can also run in snapshot mode

In the plotted currents,

– RED trace* = what OpenDSS will inject into the feeder (current envelope).

– BLACK trace = internal HW current waveform.

– BLUE trace = a peak detector for output only.

*Unstable current waveform due to bad data

Build 7.6.5_18


Outcomes……

.

etc)

Value:

• Improved modeling of PVinverters during faultconditions allows utilities tobetter quantify impacts toexisting protection

• Improved identification ofchanges to protection settingscan be made

• Software vendors can nowadopt these models for theirplatforms (CYME, Synergi,etc)


Member Roundtable


Questions…

Jeff Smith, [email protected] Rylander, PhD, [email protected] Bello, [email protected]

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