Graduate Student Symposium - University of Pittsburgh

11th Annual Electric Power Industry Conference

Swanson School of Engineering

Graduate Student Symposium

November 14th, 2016


Session Moderator:

Dr. Brandon Grainger




November 14th, 2016

Group 1 Analysis and Protection of Advanced Grids

Hashim Al Hassan, Rui Hu, Andrew Reiman, Matthieu Bertin, Joseph Petti

Protection and Distributed Control of Microgrids

Prepared by: Hashim Al Hassan

Ph.D. Student




November 14th, 2016

Analysis and Protection of Advanced Grids

Protection and Distributed Control of Microgrid

Research Objective and Motivation

4

1. Fault Detection for renewable-energy based microgrids:

2. Secondary Control of Microgrids



Distributed Control Using Cooperative Control of Multi-Agent System Theory

5 Source: Bidram, Ali. "Distributed cooperative control of AC microgrids." (2014)



Microgrid Protection

6



Microgrid Protection: Model-Based Fault Detection

7

Non-Faulted Faulted


Future work: 1. Perform extensive simulation and hardware studies to prove the

proposed fault detection method.

2. Develop System Identification techniques

3. Design distributed cooperative control and preform simulation studies


Results and Conclusion

8

Mixed Game Load Management Strategy For Wireless Communication Network Microgrid

Prepared by: Rui Hu

Ph.D Student




November 14th, 2016

10

• Develop a microgrid structure with renewable sources to enhance communication system availability and reliability.

• Model the load management problem as a multi agent game.

• Implementing indifferent principle to solve the game for each agent.

• Discuss whether the game solution reaches global optimal.

Research Objective and Motivation


Mixed Load Management Strategy

An undamaged cell site next

to destroyed utility poles

Base station microgrid

11

Load management problem and control objective



Player II

Player I

Strategic form

game

𝜎11 𝜎12

𝜎21 𝜎22

σ21 σ22

σ11 C1 C2

σ12 C3 C4

Load and solar power curves Battery SoC distribution

𝑂𝑏𝑗 = 𝑤𝑞𝜎 + 𝑤𝑎𝑓(𝑆𝑜𝐶)

12

• Indifference principle is a tool applied to solve for mixed strategy game.

• The mixed game is presented as a linear programming problem.

Indifference principle and implementation



Player II

Player I

𝑝(𝜎11)

p(𝜎12)

𝑝(𝜎21) p( 𝜎22)

Strategic-form game (mixed game)

Compute: 𝑍𝑃:= max z subject to 𝑥 𝑠𝐼 , 𝑠𝐼𝐼 𝑢 𝑠𝐼 , 𝑠𝐼𝐼 ≥𝑠𝐼∈𝑆𝐼

𝑍𝑃, ∀𝑠𝐼𝐼 ∈ 𝑆𝐼𝐼;

𝑥 𝑠𝐼 𝑢 𝑠𝐼 , 𝑠𝐼𝐼 = 1;

𝑠𝐼∈𝑆𝐼

𝑥 𝑠𝐼 ≥ 0, ∀𝑠𝐼 ∈ 𝑆𝐼. Compare of game and optimal solution

13

• Performance of game solution depends on initial battery stage and objective function.

• Introduce adaptive control to further enhance system adaptability.

• Modify game model setting and make ‘plug and play’ possible.

Game method performance and current work



Current work

Tentative adaptive control scheme

Distribution System Model Segmentation and Simplification

Prepared by: Andrew P. Reiman

Ph.D. Student




November 14th, 2016

Load-FlowLoad-Flow

Control Logic

Load-Flow

Time-Series

Control Logic

Time-Series

15

QSTS simulation is a three-loop iterative procedure which can become computationally burdensome.



• Hours per year: 8,760

• Seconds per day: 86,400

• Seconds per month: 2,592,000

• Seconds per year: 31,536,000

Load-Flow

Time-Series

Control Logic

16

Reducing the complexity of the load-flow problem decreases the computational burden of QSTS exponentially.



17

Simplification by induction: segments are constructed using I/O data from measurements or preliminary simulations.



Vin

Iin

Vout

Iout

𝑉𝑜𝑢𝑡𝐼𝑜𝑢𝑡=

𝑉𝑖𝑛 − 𝐼𝑖𝑛 ∗ 𝑍

𝐼𝑖𝑛 −𝑆

𝑉𝑖𝑛 − 𝐼𝑖𝑛 ∗ 𝑍

∗

S

Z

Simplification by induction is compatible with constant-power and ZIP loads.

18

The model is divided into segments and each one is simplified.



Buses Modeled: 3006

QSTS Benchmark: ~500 seconds

QSTS Max 𝑉𝑒𝑟𝑟: 0.000 pu

Buses Modeled: 15

QSTS Benchmark: ~8 seconds

QSTS Max 𝑉𝑒𝑟𝑟: 0.000549 pu

Methodology for Lightning Performance Improvement

Prepared by: Matthieu Bertin

M.S. Student




November 14th, 2016

20

Project


Methodology For Lightning Performance Improvement

• Main tool:

− OpenETran: software simulating lightning impacts on distribution lines

• Main tasks:

− C-language programming in OpenETran kernel to simulate a new physical model: Counterpoise

− Enhance user interface : Make interface & plots in Python v3

− Final documentation & soft deployment

21

Task 1: Counterpoise model implementation



• Counterpoise: Ground substitute for antennas, also grounding electrodes to reduce towers impulse resistance.




• Ancient model in OpenETran

• The model is linear

Source : “Effective length of counterpoise wire under lightning current”,

J. He, Y. Gao, R. Zeng, J. Zou, X. Liang, B. Zhang, J. Lee, S. Chang,

IEEE Transaction on Power delivery, Vol.20, No.2, APRIL 2005

• Counterpoise represented as a transmission line

• Leaked current Δi changes at each time step, making Ci & Gi change at each time step. The model is non linear




Previous Model

Counterpoise Model

Relay Panel Design for Microgrid Research Using Schweitzer Relays

Prepared by: Joseph J. Petti

M.S. Student




November 14th, 2016

Purpose

• The University of Pittsburgh is opening the Energy GRID Institute at the Energy Innovation Center (EIC) in downtown Pittsburgh

• Dominion Virginia Power has pledged to donate equipment to help the Energy GRID Institute's research efforts

• My thesis topic is adaptive microgrid control and I hope to utilize the Schweitzer equipment to help drive my research

25



Schweitzer Equipment

• Schweitzer’s relays are known as the industry standard

• Utilities around the world use their relays to protect their systems

• A single Schweitzer digital relay can perform the tasks of 20 electromagnetic relays

26



Features Useful For Micro Grid Research

• Fault location

• Precise metering

− Voltage, Current, Phasors, PF, Sequence Components

• Load shedding

• Communication

• Custom logic capabilities

27



Relays Chosen For the Panel

• SEL-351

− Overcurrent Protection

• SEL-411L(2)

− Transmission Line Protection

• SEL-710

− Motor Protection

• SEL-735

− Power Quality and Revenue Meter

• SEL-751

− Feeder Protection

• SEL-3530

− Real-time Automation Controller

28



Andrew Bulman, Samantha Morello,

Santino Graziani, Laura Weiserman




November 14th, 2016

Group 2 Implementation of Microgrids and Transient Protection Systems

Microgrid Case Study Analysis

Prepared by: Andrew R. Bulman

M.S. Student




November 14th, 2016

Project Overview


31

• Performing case studies on microgrid projects in the Pittsburgh region, in conjunction with the Pittsburgh District Energy Initiative, to analyze a variety of impacts of microgrids.

• A key goal is to gain a better understanding of how to take into account the non-economic factors to evaluate potential microgrid project in the Pittsburgh region.

Implementation of Microgrids and Transient Protection Systems

Case Studies: Millvale and Chatham University


32

• The University of Pittsburgh was asked to take the lead in a performing feasibility study of a 1.459 acre site owned by Millvale that could be a potential site of the solar farm and/or microgrid.

• Joint collaboration with Chatham University, Duquesne Light, and the University of Pittsburgh to investigate a potential microgrid project at the Eden Hall Campus.


Case Study: Pitt Ohio


33

• Project is almost completed and ready for final commissioning.

• Goal was to create a viable system architecture for integrating the existing AC power system with renewable energy resources (50 kW of solar power and 5 kW of wind power) distributed through a 380 Vdc backbone.


Case Study: DLC Woods Run Microgrid


34

Building #3

Building #2

Building #5

Building #1

Building #4

Building #6

• Partnership between Duquesne Light Co. and the University of Pittsburgh to experiment with new renewable energy technologies including the construction of a microgrid at the Woods Run Campus along the North Shore.


Energy Initiative for the Build Out and Optimization of Pittsburgh's Electrical Infrastructure

Prepared by: Samantha A. Morello

M.S. Student




November 14th, 2016

36

• The University of Pittsburgh’s Center for Energy is engaged in District Energy Initiatives throughout the Pittsburgh region.

District of Energy Initiatives

Build Out and Optimization of Pittsburgh’s Infrastructure

• The Center for Energy is currently serving as the technical lead for the City of Pittsburgh and Department of Energy Memorandum of Understanding. District Energy strategies include:

− Optimizing existing systems

− Integrate a next generation grid and building performance technologies to optimize energy use


37


Overview

• The University of Pittsburgh’s Center for Energy has partnered with Riverfront 47 LP to research the potential for a microgrid.

• The microgrid will be place with in a 47 acres of privately owned plot of land

− This land borders the communities of Sharpsburg, O’Hara, and Aspinwall along the Allegheny River.

− This project will include research from the University of Pittsburgh to propose different design considerations to utilize the land to install renewable generation.


38


The University of Pittsburgh’s Role

• Conduct a topline microgrid feasibility study through the use of modeling software

• The scope of work for this project will include:

− Different design scenarios and analysis for a usable microgrid

− Islanded and grid connected designs

− A feasibility study of the land and where certain renewable generations would be best placed

− A cost analysis of the proposed designs

− Emissions footprint

− Amount of area the design will occupy per requested amount of power generation

− Generation produced per month at PV/wind location.


39


HOMER Energy Software

• HOMER (Hybrid Optimization of Multiple Energy Resources) Energy is a software company which specializes in microgrid analysis and design.

• Software capabilities:

− Emissions percentages that the system releases into the atmosphere per year

− Net present cost based on load and generation selected for the design (grid connected or islanded)

− Total footprint that components will occupy

− Time series analysis of the system. This meaning the total amount of energy the components will generate per month

− Determine the maximum amount of renewable penetration of the system


OpenDSS Model for Analysis of Photovoltaic Inverter Transients: Testing

Prepared by: Santino F. Graziani

M.S. Student




November 14th, 2016

41


OpenDSS Model for Analysis of Photovoltaic Inverter Transients

Inverter Transient Testing Using EPRI PortoSag Generator

42

Single-Phase Inverter



Micro-Inverter



Open-Circuit Event Data



Micro-Inverter Three-Phase Inverter



Short-Circuit Event Data

43

OpenDSS Model for Analysis of Photovoltaic Inverter Transients: Modeling

Prepared by: Laura M. Wieserman

Ph.D. Student




November 14th, 2016

45



Interface Between Hammerstein-Wiener (HW) and OpenDSS Solution Variables

46

Model

Input

(voltage)

Model

Output

(current)

Input

Nonlinearity

Output

Nonlinearity

Transfer Function

(Filter)



Interface Between Hammerstein-Wiener (HW) and OpenDSS Solution Variables

47

OpenDSS Snapshot

Lab Data

Model Output


Micro-Inverter Example in OpenDSS


48

Example Feeder

Single-Phase Inverter Example in OpenDSS and Test Feeders



Group 3 Design and Analysis of Power Electronic Converters




November 14th, 2016

Ansel Barchowsky, Alvaro Cardoza, Jacob Friedrich, Chris Scioscia, Patrick Lewis

Modular, Multilevel, High Density DC-DC and DC-AC Converters using GaN HFETs

Prepared by: Ansel Barchowsky

Ph.D. Student




November 14th, 2016

51

Why GaN MMCs?

Design and Analysis of Power Electronic Converters

Modular Multilevel GaN Converters

• The Objective

• Use GaN HFETs to achieve high density and efficiency in DC-DC and DC-AC conversion

• The Challenge

• Commercially available GaN HFETs are limited to about 650 V, 100 A

• The Solution – MMC

• Divides applied voltage across multiple submodules

• Extremely efficient and low volume requirements

EPC2014C eGaN HEMT

1.1 mm

1.7 mm

Single submodule test board

52

DC-AC Converter Design



Proposed DC-AC MMC structure

• Converter specifications:

• 450 VDC input, 240 VAC output, 2 kVA

• Output AC frequency of 60 Hz

• Switching frequency of 16 kHz

• Phase shifted pulse width modulation

• 14 submodules per arm using EPC 2014c devices and 1.6 mF capacitors

• Converter achievements:

• Peak simulated efficiency of 98%

• Less than 5% THD with only 1 µH arm inductor

• 100 W/in3 power to volume ratio

Vin

Cin

Cin

Lb Lb

Lb Lb

Vout

+

-

Low Voltage 1ph M2C Bridge:14 Level EPC2014c


53

DC-AC Converter Design



Proposed 1ph MMC structure

• Converter specifications:

• 200 VDC input, 600 VDC output

• AC tank frequency of 1 MHz

• Switching frequency of 1 MHz

• Nearest level QSW modulation

• 4 SM per input arm, 10 SM per output arm using EPC2016c HFETs

• Converter achievements:

• PCB-integrated planar ferrite transformer

• Efficiency up to 84% at 1 MHz

• 120 W/in3 power to volume ratio

Vin

Cin

Cin

Lb Lb

Lb Lb

VAC1

+

-


Vout

Cout

Cout

LbLb

LbLb

VAC2

+

-

High Voltage 1ph M2C Bridge:10 Level EPC2016c

HF Transformer

54

Hardware Prototyping



Top and side view of MMC arm PCB

• Initial prototype Design:

• 14 SM DC-AC arm board under development

• 4 arm PCBs form full converter

• Initial low voltage testing underway

2-level output voltage of single arm test

Power Source Buffering using a Triangular Modular Multilevel Converter with Energy Storage

Prepared by: Alvaro Cardoza

PhD Student




November 14th, 2016

56

Background/Motivation

Sources: http://www.wfs.org/futurist/july-august-2012-vol-46-no-4/smart-house-networked-home

http://www.stratacore.com/the-advisor/data-center-providers-ma-activity-2015


Power Source Buffering using a TMMC with Energy Storage

• As grid development continues to incorporate DC technologies and diversify its generation sources with renewables, it is becoming increasingly more important to develop new interfacing devices to ensure adequate control and stability of the power grid.

57

Full Testbed System

TMMC with Energy Storage and PV source

0

0

IndCurrent_3_1

IndCurrent_2_2

IndCurrent_1_1 Ind_Current1_2 IndCurrent_1_3

IndCurrent_2_1

GAIN

Voltage_Controller1_Kp

I

Voltage_Controller1_Ki

VC1_out

21.844

VSUM_row1

189.86Vref1_SUM

0.13809

Vref2_SUM

-3.3677

VSUM_row2

193.37

CONST

V_ref1

IND_MEASURE_2_1

0

IND_MEASURE_1_1-0.1366

-0.1366

IND_MEASURE_1_2

GAIN

Voltage_Controller2_Kp

I

Voltage_Controller2_Ki

VC2_out

2.9071

CONST

V_ref2 TPH1

TPH3

TPH2

VC1_out1

50.428

IND_MEASURE_1_5

28.573

IND_MEASURE_1_4

28.573

TPH6

TPH5

GAIN

Voltage_Controller1_Kp1

I

Voltage_Controller1_Ki1

VSUM_row3

186.29Vref1_SUM1

3.7121

CONST

V_ref3

IND_MEASURE_1_3

28.573

C9

TPH4

C10C11

+

V

V_input1

0

+

V

Vctrl_input

-380

R2

380

126.67

E1

Vctrl_Step

EC1

11.026

0

Vout_pos

CapVoltage_3_1

IndCurrent_3_1

Vin_pos_Vout_neg Vin_neg

U1

TMMC Submodule1

Vout_pos

CapVoltage_2_1

IndCurrent_2_1


U2

TMMC Submodule2

Vout_pos

CapVoltage_2_2

IndCurrent_2_2


U3

Vout_pos

CapVoltage_1_1

IndCurrent_1_1


U4

TMMC Submodule4

Vout_pos

CapVoltage_1_2

IndCurrent_1_2


U5

TMMC Submodule5

Vout_pos

CapVoltage_1_3

IndCurrent_1_3


U6

+

V

Vcap_row3

97.563

+V

Vcap_row2

95.805

+V

Vcap_row1

94.057

R1

92.231

A

AM1

0

A

AM2

0

A

AM3

0

STATE_33_1

TRANS8TRANS7

TRANS6TRANS5

STATE_11_6

STATE_11_5

C1

UC_Discharge_3_1

0

L7

UC_Charge_3_1

0

R5

+

V

Vucap_row3

47.998

Rf1

D13

D14

PWM

PWM1

D2

D3

Rf2

+

V

Vucap_row1

47.998

R3

UC_Charge_3_2

0

L1

UC_Discharge_3_2

0

C2

D4

D5

Rf3

+

V

Vucap_row2

47.998

R4

UC_Charge_3_3

0

L2

UC_Discharge_3_3

0

C3

C4

UC_Discharge_3_4

0

L3

UC_Charge_3_4

0

R6

+

V

Vucap_row4

47.998

Rf4

D6

D7

C5

UC_Discharge_3_5

0

L4

UC_Charge_3_5

0

R7

+

V

Vucap_row5

47.998

Rf5

D8

D9

C6

UC_Discharge_3_6

0

L5

UC_Charge_3_6

0

R8

+

V

Vucap_row6

47.998

Rf6

D10

D11

STATE_11_1

STATE_11_2

TRANS1 TRANS2

TRANS3TRANS4

STATE_33_2

STATE_11_3

STATE_11_4

TRANS9 TRANS10

TRANS11TRANS12

STATE_33_3

L8

EQU

IF (Time<=0.25)

{ Rload:=2.12; } ELSE

{ Rload:=2.12*1; }

W +

WM4

S4

S3

Rd

0

Lf0

Cf

0

PVout+

PVout-

TotalPVSystem

PV, Boost, & BB Reg

PV_Disconnect

W

+

WM5

PV Input 1747 W, 1000 W/m2

Module 3,1

Module 2,1

Module 2,2

Module 1,1

Module 1,2

Module 1,3

PI Controllers

Hysteresis Current

Controllers

sESS 3,1

sESS 2,1 sESS 2,2

sESS 1,1 sESS 1,2 sESS 1,3

3rd Row sESS State Machine

2nd Row sESS State Machine

1st Row sESS State Machine

“Grid” Input

“Load” Output



58

Simulation Results: Test Cases 1-3

1. Test One – PV Step Increase, Load Step Increase

a. Irradiance: 1000 W/m2 (Switched in at 150ms)

b. Load Step: 2.12 Ω -> 1.06 Ω @ 250ms

2. Test Two – PV Step Increase, Load Step Decrease


b. Load Step: 2.12 Ω -> 4.24 Ω @ 250ms

3. Test Three – PV Step Increase, PV Step Decrease


0 W/m2 (Switched out at 250ms)

b. Load Step: 2.12 Ω (No Change)



59

Test 1 – PV System Switched-In and Load Increase

With Ultracapacitor ESS Without Ultracapacitor ESS

With Ultracapacitor ESS Without Ultracapacitor ESS

118.5 V

55.5 V 106.9 V

71.4 V



• Irradiance: 1000 W/m2 (Switched in at 150 ms)

• Load Step: 2.12 Ω -> 1.06 Ω @ 250 ms

60

Test 1 – PV System Switched-In and Load Increase

With Ultracapacitor ESS Ultracapacitor ESS Current Injection



Quantifying DC Electric Ship Performance and Emission Reductions through Modular, Integrated Resonance Units with Parallel Energy Storage

Prepared by: Jacob H Friedrich

M.S. Student




November 14th, 2016

62

• Navy Sea System Command states the design philosophy is as follows:

“The primary aim of the electric power system design will be for survivability and continuity of the electrical

power supply.”

Research Motivation and Objective


Quantifying DC Electric Ship Performance and Emission Reductions

• Based on Naval Ship study:

− Fault identification

− Integrate energy storage with series resonate converter

− Evaluate behavior of the modular unit

− Quantify the expected emission profiles for DC based ship

63

Fault Identification

Design and Analysis of Power Electronics Converters

~- G

~- M

~

G

-

- - - - - -

- - - - - -

Vital Load -~

Load

- - - - - -

- - - - - -

-~

Load

Vital Load

DC/DC Converters DC/DC Converters

DC/DC ConvertersDC/DC Converters

MVDC Starboard

Bus

Port Propulsion

Motor

~ M

-

Auxiliary AC

Generator

AC Breaker

DC Cable

Overhead

Line

Auxiliary AC

Generator

AC Breaker

Inverter Drive

Inverter Drive

DC

Disconnect

DC

Disconnect

DC Cable

Overhead

Line

N

N

Line-to-Line

Fault

Line-to-Ground

Fault

1

2

3

4

5

6

7

8

910

11

12

13

14

15

Ship Architecture Layout and Fault Locations


• Determine a system characteristic signature for fault section identification

• The focus for the core simulation analysis will be on current and voltage profiles before, during, and after a contingency event throughout one zone of an electric ship

64

• Input voltage increases the efficiency profile and keeps its general form but translates to a lower efficiency

• The maximum efficiency can be reached by adjusting the equivalent load that the SRC processes

Series Resonate Converter


SRC performance over specified load range for

varying input voltages

System Overview


65

• Study needs to be done to ensure that the modular integrated storage architecture can handle the expected mechanical thermal stresses with dynamic load changes

• A design criteria for optimal performance or stable operation can be seen in the image

Power Semiconductor Reliability


Ramp Rate Guidelines


66

• Under Executive Order, emissions need reduced and energy performance needs to be increased

• Maritime transport emits roughly 1000 million tons of CO2 annually

• Emissions produced by power production process will be analyzed compared to emissions produced by engine fuel consumption

• Holistic assessment and comparison of lifecycle

Emission Profiles for DC Based Ship



Supplementing Resonant Converter Performance with Parallel Energy Storage

Prepared by: Christopher Scioscia

M.S. Student




November 14th, 2016

68

• Photovoltaics generation continues to penetrate the electric market

• Energy storage required due to intermittency

• Ability of energy storage to supplement the performance of an individual converter

• Additional benefits of power rerouting, load sharing, reliability, and operational flexibility

Research Motivation


Resonant Converter Performance with Parallel Energy Storage

DC system overview

69

• Improved performance over wider range of loads

• Efficiency profile for converter hill shaped

• Top curve at 90 V input, bottom curve at 150 V input

• Assistance sinks or sources current from front and or back to improve performance

Battery Assisted Performance



SRC efficiency versus load range

Solar Array

Resonant Converter

Load

Energy Storage

89

90

91

92

93

94

95

96

0 500 1000 1500 2000 2500 3000 3500

Effi

cien

cy (%

)

Power Processed (W)

Converter Load Profile

90 V

100 V

110 V

150 V

Back Assist

Front Assist

70

• Operating at light load efficiency

• Algorithm converges on current that results in optimal efficiency

• Improves unassisted efficiency from 83.8% to 90.5%

Back End Support



Resonant Converter

Storage

89.5

90

90.5

91

91.5

92

92.5

93

93.5

94

0 500 1000 1500 2000 2500 3000 3500

Eff

icie

nc

y (

%)

Power Processed (W)


Light Load

SRC Power Processed

SRC Efficiency

71

• Operating with heavy insolation, high input voltage

• Algorithm sinks current to reduce input voltage to target threshold, 90V

• Improves unassisted efficiency from 83.8% to 92%

Back End Support



89

90

91

92

93

94

95

96

0 500 1000 1500 2000 2500 3000 3500

Effi

cien

cy (%

)

Power Processed (W)


90 V

150 VHigh Irradiance

Resonant Converter

Storage

SRC Front Voltage

SRC Efficiency

72

• Algorithm realized within greater mode hierarchy

• Autonomous assistance, load sharing, and voltage support

Mode Hierarchy



Mode Transition States

𝑀𝑜𝑑𝑒 𝐼 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑡𝑎𝑛𝑑𝑏𝑦

𝑀𝑜𝑑𝑒 𝐼𝐼 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐴𝑠𝑠𝑖𝑠𝑡

𝑀𝑜𝑑𝑒 𝐼𝐼𝐼 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶ℎ𝑎𝑟𝑔𝑒

𝑀𝑜𝑑𝑒 𝐼𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒

𝑀𝑜𝑑𝑒 𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐸𝑚𝑝𝑡𝑦 𝑆𝑡𝑎𝑛𝑑𝑏𝑦

Inverter Grid Support and Electro-Thermal Device Analysis in Microgrids

Prepared by: Patrick T. Lewis

Ph.D. Student




November 14th, 2016

Inverter Grid Support and Electro-Thermal Analysis in Microgrids

Research Motivation

• With increasing penetration of distributed resources on the distribution grid and microgrids of the future, reactive compensation will be expected from DG inverters.

• Microgrid system resiliency in fault or mode transition islanding events

• Power electronics reliability for DG inverters, dictated by thermal performance characteristics

• Trade-off: optimizing reliability while maintaining resiliency


Grid Tie

PV PlantGrid Interface

Converter~=

Community Energy Storage ~

=

Commercial Load and PV Genertation

Residential Load and PV Genertation

~=

~=

~=

~~

Example of Community Microgrid

74

• During transition ride through event, injecting sufficient reactive current must be determined according to the measured voltage at the grid

𝑘 =(𝐼𝑞 − 𝐼𝑞0)/𝐼𝑟𝑎𝑡𝑒𝑑

(1 − 𝑣𝑔), 𝑤ℎ𝑒𝑟𝑒 𝐼𝑞 < 𝐼𝑟𝑎𝑡𝑒𝑑

Resiliency - Microgrid Inverter Modeling



Source: Seal B., “Common Functions for Smart Inverters, Version 3” Electric Power Research Institute, Feb 2014

Source: Yongheng Yang; Huai Wang; Blaabjerg, F., "Reactive Power Injection Strategies for Single-Phase Photovoltaic Systems Considering Grid Requirements,“

in Industry Applications, IEEE Transactions on , vol.50, no.6, pp.4065-4076, Nov.-Dec. 2014 75


Reliability - Device Performance


• Reliability of the power electronic converter depends primarily upon the thermal stress placed upon the devices

76 Voltages Vgs and Vds, and Current ID for both MOSFET turn on (top) and turn off (bottom) device operations

Switching Times tr Rise Time tf Fall Time

• Modeling of the switching energy and power losses of power MOSFETS utilizing a device characterization tool in ANSYS Simplorer

Reliability - Thermal Stress and Characterization



77

3φ AC Grid Tie

Load

DC Source(PV emulator)

3φ DG Inverter

Transformer

MG Disconnect

Transient Thermal Impedance J-C

• Modeling of the switching energy and power losses of power MOSFETS utilizing a device characterization tool in ANSYS Simplorer

Reliability - Thermal Stress and Characterization



78 Device Transfer Characteristics I-V

3φ AC Grid Tie

Load

DC Source(PV emulator)

3φ DG Inverter

Transformer

MG Disconnect

Documents

Graduate Student Symposium - University of Pittsburgh