Integration of DER: Advanced Modeling and Simulation

Transcription

1 Integration of DER: Advanced Modeling and Simulation P174A 2016 Fall Advisory Meeting Jeff Smith, Manager Power System Studies Matthew Rylander, Technical Leader Power System Studies Mobolaji Bello, Engineer III Power System Studies September 19, 2016

2 Outline Introduction: Past and Present DER Impact Assessment (Hosting Capacity) Development of Methods System-wide Methodology Functionality Implementation Demonstration Enhancement Smart Inverter Settings Protection Member Roundtable 2

3 Power System Studies Team Modeling and Simulation We ve grown in the past year Jeff Smith Jason Taylor, PhD Matt Rylander, PhD Roger Dugan Huijuan Li, PhD Wes Sunderman Mobolaji Bello Alison O Connell, PhD Aoconnell@epri.com 3 Davis Montenegro-Martinez, PhD dmmartinez@epri.com Jouni Peppanen, PhD jpeppanen@epri.com

4 Power System Studies Team Modeling and Analysis Integration of DER Distribution Planning Jeff Smith Roger Dugan Jason Taylor, PhD Huijuan Li, PhD Matt Rylander, PhD Distribution Operations Transmission Wes Sunderman Mobolaji Bello Alison O Connell, PhD Aoconnell@epri.com Davis Montenegro-Martinez, PhD dmmartinez@epri.com Jouni Peppanen, PhD jpeppanen@epri.com Storage Power Quality 4

5 Overview of Project Set 5

6 Program 174 Integration of DER Project Set A Modeling and Analysis P174A Overview: Analytics Mitigation Tools Training Smart Inverters Hosting Capacity Specific Topics: Hosting Capacity Smart inverters Target Audience: Distribution planners Interconnection engineers Resource planners Close coordination with P94, P173, P1, 180A/F (P200 in 2017) 6

7 Advanced Modeling and Analysis of DER From Accommodating DER to Integrating DER Accommodating Grid No changes DER Limited to no control Objectives Accommodate DER without changing distribution Outcomes Baseline hosting capacity, no changes to distribution performance Means for Integrating DER DER var control DER curtailment Grid-side changes to voltage control Protection changes Additional voltage/var control (grid-edge control) Storage Customer load control Integrating Grid Specific changes to serve load and DER Increased control (watts and vars) DER Objectives Least-cost solutions for incorporating DER into distribution operations Outcomes Increased hosting capacity, improved distribution performance 7

8 Key Highlights from the Past (EPRI-P174A) Analysis PV Impacts PV Hosting Capacity Analysis > 35 feeders Cost/Benefit Analysis (Few Feeders) Cost/Benefit Analysis (System-wide) Methods Planning Limits with DG Hosting Capacity Method Dev Streamlined Hosting Capacity Method Dev New Screening Methods Tools DGScreener App Modeling of smart inverters Commercial Tool Implementation of Streamlined Hosting Method Mitigation Smart Inverter Impact Assessment Recommended Settings for Smart inverters Training OpenDSS/ DER Integration Workshops 8

9 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 9

10 Modeling and Analysis - Analytics 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 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. 10

11 Modeling and Analysis - Mitigation 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 In many cases, use of smart inverters can be the least-cost solution for integration issues Increase in hosting capacity with smart inverters Without smart inverters With smart inverters 11

12 Modeling and Analysis - Tools 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 Incorporating hosting capacity methods into existing utility planning tools no need to re-invent the wheel EPRI Hosting Capacity Module Existing Distribution Planning Tools (CYME, Windmil, Synergi, PowerFactory, GridLab-D, DEW) 12

13 Modeling and Analysis - Training 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 Providing utilities with onsite training for distribution engineers 13

14 Modeling and Analysis of DER Recent Projects 14

15 174A: 2016 Deliverables Focus of Today s Discussion Project Implementation of Streamlined Hosting Method in Commercial Planning Tools Advancement of Hosting Capacity Methods in Planning Tools Demonstration of Improved DER Screening Through Hosting Capacity Method Solar PV Integration Using Advanced Inverters Details Application of new methods in existing planning tools* Inclusion of existing DER, smart inverters, portfolios of DER Use cases for system-wide Hosting Capacity analysis Recommended settings Modeling of PV Inverters for Protection Studies Development of models for protection studies 1 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 economics 2 Examining the Effects of Customer-Sited Solar+Storage on Distribution Energy storage in the portfolio 3 Understanding PV Market Potential for Distribution Planning Distribution impacts vs. adoption 2 * CYME Implementation Joint Deliverables 1-180A 2-174D 3- P94 15

16 DER Hosting Capacity Development Functionality Implementation Demonstration Enhancement 16

17 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 can be used to inform utility interconnection processes and to support DG developer understanding of more favorable locations for interconnection Hosting capacity considers DER interconnection without allowing Voltage/flicker violations, Protection mis-operation Thermal overloads Decreased safety/reliability/power quality /17 Develop original method Utility application Streamlined method development Utility application Vendor implementation Evolution of Hosting Capacity Methodology 17

18 Detailed Implementation of Hosting Capacity Assessment Method Overview Select specific locations for DER Iterate through each case Solve 1000 s of load flows Findings Results similar to detailed impact studies Accurate Time-consuming/data intensive Applicable to specific scenarios Add DER Run power flow Difficult to consider range of possible DER scenarios All locations (three-phase and single-phase) Feeder reconfigurations DER types N Criteria violated? Y Hosting Capacity Voltage Protection Power Quality Thermal 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 Analysis of High-Penetration Solar PV Impacts for Distribution Planning: Stochastic and Time-Series Methods for Determining Feeder Hosting Capacity. EPRI, Palo Alto, CA:

19 Modeling Requirements for Hosting Capacity Assessment Increasing granularity Depth Detailed Hosting Capacity Few feeder models: highly detailed for demonstration projects No feeder models Portion of feeders modeled: sufficient for traditional planning Most feeders modeled: sufficient for traditional planning Simplified Screening Breadth Increasing number of feeders 19

20 Simplified Screening 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 20

21 Modeling Requirements for Hosting Capacity Assessment Detailed Hosting Capacity Few feeder models: highly detailed for demonstration projects Increasing granularity Depth No feeder models Streamlined Hosting Capacity Portion of feeders modeled: sufficient for traditional planning Most feeders modeled: sufficient for traditional planning Simplified Screening Breadth Increasing number of feeders 21

22 DER Capacity DER Capacity Streamlined Implementation of Hosting Capacity Assessments Method Overview Solve base load flow/shortcircuit 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 Substation 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 singlephase), feeder configurations, DER DER Location technologies and types (centralized vs distributed) Easily replicable across entire system Centralized DER Typically 3-5 minutes per feeder when automated DER Location Y Hosting Capacity N Substation Limit? Distributed DER Integration of Hosting Capacity Analysis into Distribution Planning Tools. EPRI, Palo Alto, CA:

23 Functionality Development Functionality Implementation Demonstration Enhancement 23

24 Key Aspects of Hosting Capacity Method DER Size and Location Unique DER Technology Feeder Impacts Power System Criteria Thermal Power Quality/Voltage Protection Reliability/Safety Substation transformer Sudden (fast) voltage change Relay reduction of reach Unintentional islanding Primary conductor Steady-state voltage Sympathetic tripping Operational flexibility Service Transformer Voltage regulator impact Element fault current Secondary Conductor Load tap changer impact Reverse power flow 24

25 DER Size/Location Voltage Example: Solving Primary Overvoltage Hosting Capacity Overvoltage Impact Threshold 25 Location from Source

26 Hosting Capacity Example Substation 1 c (Min) Feeder 1 b (Max / Min) Substation Node 1 a Node 2 a Node 3 a Node 4 a Node 5 a Node 6 a Feeder 2 b (Max / Min) Feeder 3 b (Max / Min) 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. 26

27 Validation * Detailed Method * Detailed Method Threshold Threshold 27

28 Implementation Development Functionality Implementation Demonstration Enhancement 28

29 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 Vendor software CYME Synergi Milsoft Model Interface tool CYME Synergi Milsoft DRIVE Core Engine Compatible with all tools DRIVE: Distribution Resource Integration and Value Estimation Tool Others Others 29

30 Implementation To Date Planning Tool Interface Hosting Capacity Solution Engine CYME Synergi Milsoft PowerFactory GridLab-D To be available this year through CYME Developed with support and available to Level 3 funders of supplemental project* Under development and will be available to Level 3 funders of supplemental project* Under development and will be available to Level 3 funders of supplemental project* Interface being developed by National Grid To be available this year through CYME DRIVE v1.0 available now through membership in Program 174A** DRIVE v1.0 available now through membership in Program 174A** DRIVE v1.0 available now through membership in Program 174A** DRIVE v1.0 available now through membership in Program 174A** 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 30

31 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 Increasing effectiveness, complexity, and data requirements Defining a Roadmap for Successful Implementation of a Hosting Capacity Method for New York State, EPRI, Palo Alto, CA:

32 Hosting Capacity Implementation Roadmap Stage 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 impact evaluations - 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/sectionbased hosting capacity - Possible substation/transmission constraints - Operational and planning flexibility for changing configurations - Transmission assessments and mapping of distributionlevel 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 32

33 Demonstration Development Functionality Implementation Demonstration Enhancement 33

34 Demonstration Part 1 Interface to Utility Model Synergi Solver application Synergi Standalone GUI application Part 2 Hosting Capacity Analysis 34

35 Hosting Capacity for Thermal and Voltage Thresholds: Thermal 100% normal rating Voltage 3% change Centralized Large DER System View of 11 Substations Substation Hosting Capacity reflects worst feeder connected *Hosting Capacity lower higher Hosting capacity can change when considering additional issues. *Initial analysis results from TVA/AEC, results not finalized 35

36 Hosting Capacity for Thermal and Voltage Thresholds: Thermal 100% normal rating Voltage 3% change Centralized Large DER Substation View of 9 Feeders Feeder Hosting Capacity reflects worst node connected *Hosting Capacity lower higher Not all feeders served from the same substation have limited hosting capacity. *Initial analysis results from TVA/AEC, results not finalized 36

37 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 DRIVE Delivery Type / Date Technical Brief, Q4 37

38 Enhancement Development Functionality Implementation Demonstration Enhancement 38

39 Enhancement of Hosting Capacity Methods in Planning Tools EPRI 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 39

40 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 nonwires alternative to upgrades Locational benefits Optimal settings 40

41 Existing DER Inclusion of Existing DER allows the Remaining Hosting Capacity to be determined. Baseline Hosting Capacity Remaining Hosting Capacity 0.5 MW added Remote node impacted as well 41

42 Utility Mitigation Substation LTC: 123V Utility control and power delivery elements have impact on hosting capacity. Substation Substation LTC: 125V 42

43 Customer Mitigation DER with Unity Power Factor Customer DER settings and control have impact on hosting capacity. DER with Inductive Power Factor 43

44 Methods to Determine Recommended Advanced Inverter Settings Improving Distribution Integration of PV 44

45 Solar PV Integration Using Advanced Inverters Enabling 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 In many cases, use of smart inverters can be the least-cost solution for integration issues Increase in hosting capacity with smart inverters Without smart inverters With smart inverters 45

46 Voltage (pu) Industry Landscape: New Distribution Resources Advanced inverters can improve integration of DER by reducing some of the adverse impacts from DER Primary Voltage 20% PV 20% PV with volt/var control Baseline No PV Mitigate voltage issues Provide least-cost solution Increase hosting capacity Hour 24 Hour Simulation 46

47 Voltage (pu) Industry Challenge: How to Take Advantage of the Resources There are numerous possible inverter settings Wrong settings can actually worsen grid performance Voltages with different voltvar settings No PV ---- PV base ---- Voltvar Blue lines indicate voltage response using different volt-var settings. Discrete voltage changes are due to capacitor switching or inverter status change Hour

48 Location Does Matter Substation Single DER System and Location DER DER DER Region A Var control may not be needed due to minimal voltage rise caused by DER Region B Var control most effective due to high X/R Region C Var control not as effective due to low X/R High X/R ratio High short-circuit strength DER doesn t move voltage Reactive power control not needed High X/R ratio Low short-circuit strength DER moves voltage Reactive power control is effective Low X/R ratio Low short-circuit strength DER moves voltage Reactive power control is not as effective End of feeder 48

49 Solution: Methods to Derive Inverter Settings 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 Medium Based on Feeder Model and PV Location Based on Feeder Model and PV Location Not Applied 3 High Based on Feeder Model and PV Location Based on Feeder Model, PV Location, and Service Transformer Impedance Not Applied 49

50 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 1 Mean 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 Power factor X R mean 2 X R + 1 mean 50

51 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 51

52 High-Level Key Takeaways for Distribution-Focused Inverter Settings Inverter headroom for providing reactive power control at full output is critical Many settings are feeder and/or location dependent Optimizing 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 52

53 Fault Current Modeling of PV Joint Project with Distribution Planning (180A) Mobolaji Bello, Roger Dugan 53

54 2016 Effort 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 Tech update Incorporate test results into the model to predict system response. Finalize lab testing Lab Testing 2015 Technical Update Develop OpenDSS Model Incorporate Tx modeling efforts* In collaboration with P173 (Bulk Renewables) 2016 Technical Update 54

55 Inverter Testing Model Phases kw Enphase M215 (18) 3 (208 V) 4.05 Eaton PV (208 V) 5.0 Schneider Conext TX 2800 NA 1 (240 V) 2.8 Power One UNO OUTD-S-US 1 (240 V) 2.0 Fronius IG2500-LV NEG 1 (208 V) 2.0 Solectria PVI (240 V) 3.0 SMA SB-3000TL-US-22 1 (240 V) Phase Fronius Inverter from EPRI 3(208 V) 10KW 55

56 Testing and modeling results (Fault at Inverter terminals) Fronius Three-Phase Eight inverters tested are clustered into categories of behavior 56

57 Testing and modeling results (Fault on Transmission) Fault Ride-Through Remaining energized Inverters have different fault responses during voltage sags as well 57

58 The Hammerstein-Wiener framework u(t) X Y X Y Z -1 W(t) x(t) y(t) Table for Piecewise Linear Expressions transfer function expression Table for Piecewise Linear Expressions Piecewise Linear function representing input nonlinearity Piecewise Linear function representing input nonlinearity Step Response of Linear Block Piecewise Linear Function representing output nonlinearity separates linear dynamics and non-linearities in the inverter model. 58

59 Hammerstein-Wiener model in Discrete Time Domain o o o Inject a u(t) waveform, into the input nonlinearity, BP1. Running inside OpenDSS, u(t) is generated from the most recent phasor voltage solution. 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 voltagecontrolled current source (VCCS). 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. 59 Transient testing, non-linear dynamic modeling approach

60 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. Build 7.6.5_18 *Unstable current waveform due to bad data 60

61 Outcomes Value: Improved modeling of PV inverters during fault conditions allows utilities to better quantify impacts to existing protection. Improved identification of changes to protection settings can be made Software vendors can now adopt these models for their platforms (CYME, Synergi, etc) 61

62 Member Roundtable 62

63 Questions Jeff Smith, Matthew Rylander, PhD, Mobolaji Bello, 63