Graduate Student Symposium Moderated by: Alvaro Cardoza 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Graduations Since EPIC 2018 (Full Time)
Patrick Lewis Hepburn and Sons LLC
Resilient Cities Aryana Nakhai John Kieffer Grant Cruse Erick Bittenbender 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Technical & Economic Analysis of Renewable Energy Based Microgrid Case Study: Abyaneh Village, Iran Prepared by: Aryana Y. Nakhai M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Resilient Cities Analysis of Renewable Energy Based Microgrid
Case Study Analysis Peak hours: 6:00 pm-12:00am
Daily Peak: 2.1 kW Peak Energy: 13 kWh
Figure 1: Geographical Location of Abyaneh
Figure 3: Daily Load Profile in Abyaneh Village
Figure 2: View of Red Clay Architecture
Figure 4: View of Old Power Lines
5
Resilient Cities Analysis of Renewable Energy Based Microgrid
System Overview
6
Resilient Cities Analysis of Renewable Energy Based Microgrid
HOMER Simulation Results System Elements
Total Capacity
5 wind turbines
15 kW
30 solar panels
10 kW
1 Diesel Generator
10 kW
30 string battery
360 V
2 Converters
9 kW
Cost Element
Amount
Net Present Cost
$88,178.20
Cost of Energy
0.187 $/kWh
Table 2: Costs of Optimal Result
Table 1: System Parameters of Optimal Result
Figure 1: Power Generation throughout a Year
Figure 2: Power Generation per Component
7
Resilient Cities Analysis of Renewable Energy Based Microgrid
Economic Comparison with Traditional System Quantity
Value [kg/kWh]
Carbon Dioxide
0.2068
Carbon Monoxide
0.00156
Sulfur Dioxide
0.000508
Nitrogen Oxides
0.0001773
Table 1: Emissions Data Traditional System
Microgrid System
Overhead Line Cost [per km]
$16,000
Overhead Line Cost [per km]
$16,000
O&M Cost [per km]
$2,000
O&M Cost [per km]
$2,000
Distance [km]
80
Distance [km]
5
Total Network Cost
$1,440,000
Total Network Cost
$90,000
System Cost
$88,178
Total Cost
$178,178
Table 2: Economic Comparison with Traditional System Expansion
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Global and Local Partnerships for Sustainable Engineering Education Prepared by: John Kieffer M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Resilient Cities Sustainable Engineering Educational Opportunities
Why Scandinavia?
Figure 2: CO2 emissions (metric tons per capita) (source: data.worldbank.org) Figure 1: Middelgrunden Wind Farm
Figure 3: Renewable energy consumption (% of total final energy consumption) (source: data.worldbank.org)
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Resilient Cities Sustainable Engineering Educational Opportunities
Scandinavia Learning Opportunities
Figure 4: Middelgrunden Wind Farm
Students were able to witness: • Off-shore wind • District heating/CHP • Waste-to-power generation • Solar thermal • Manufacturing and development of: Transformers, FACTS, HVDC, and surge arrestors • University research labs
Figure 5: DTU High Voltage Lab
Figure 6: Jaegerspris district energy plant
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Resilient Cities Sustainable Engineering Educational Opportunities
Sustainable Engineering in Pittsburgh
Students will be able to witness: • Solar PV • Lithium energy storage • Grid-tied and battery back-up inverters • Lighting and control system • Power flow monitoring • Clean energy developed by and for community members
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Characterization of Power System Outages Caused by Hurricanes through Localized Intensity Indices Prepared by: Grant Cruse M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Resilient Cities Characterization of Power Outages Caused by Hurricanes
Overview Methods • Derive hurricane intensity based on certain metrics • Use nonlinear regression analysis to find relationships between metrics and measured hurricane attributes Goal • Provide basis for power system operators to anticipate logistical needs during hurricane recovery period
Outages in NC Due to Hurricane Dorian
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Resilient Cities Characterization of Power Outages Caused by Hurricanes
Definitions Outage Metrics - Data that represent how the power system was affected • Maximum Outage Incidence • 95% and 98% Restoration Times (days) • Average Outage Duration (hours) Hurricane Actions – Data that describe the hurricane • Storm Surge Height, H (feet) • Maximum Sustained Wind Speed, V (miles per hour) • Time Under Storm Conditions, T (hours) • Land Area Swept by Hurricane, A (square miles)
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Resilient Cities Characterization of Power Outages Caused by Hurricanes
Regression Analysis Basis Function – function of hurricane actions
where đ?‘?â„Ž are coefficients to be found through regression analysis Logistic Curve Forms – Based on distribution of collected data
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Resilient Cities Characterization of Power Outages Caused by Hurricanes
Regression Analysis Results
Logistic Curves with Corresponding Basis Functions and Coefficients of Determination
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Resilient Cities Characterization of Power Outages Caused by Hurricanes
Inclusion of Debris Data Motivations • Include other aspects of hurricanes • Increase the coefficients of determination Plan • Determine probabilistic model that describes the time required to clear debris from a road • Incorporate debris measurements from Hazus with the model • Generate new logistic curves Hurricane Isaac Storm Track from Hazus
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Critical Asset Identification Methods & Potential Methods for Testing Their Effectiveness Prepared by: Erick Bittenbender M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Resilient Cities Critical Asset Identification
Motivation: Blackout Impacts • Examination of NERC outage data (1984 to 2006) found: • Power law distribution of blackout size • Greater risk with large blackouts • No reduced frequency of large blackouts
Event probability vs. blackout size (left in MW, right in customers affected)
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Resilient Cities Critical Asset Identification
Modelling Methods for Asset Identification Method
Failure Types
Underlying Theory
Random and cascading failures, attacks, n-k contingencies
Mapping power system to graph of nodes & edges
Cascading failures (random or intentional initiation)
System state transitions following failures
Attacks
Attackers inflict sys. damage, defender mitigates impact
Multi-Attribute Methods
Random failures, attacks
Technical, economic, and other factors assigned weights
Deterministic Guidelines
Random failures, attacks
Regulatory guidelines for identifying critical assets
Network Theory Probabilistic Graph Methods Game Theory
Summary for a selection of critical asset identification methods
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Resilient Cities Critical Asset Identification
Modelling Methods for Asset Identification Method
Failure Types
Underlying Theory
Random and cascading failures, attacks, n-k contingencies
Mapping power system to graph of nodes & edges
Cascading failures (random or intentional initiation)
System state transitions following failures
Attacks
Attackers inflict sys. damage, defender mitigates impact
Multi-Attribute Methods
Random failures, attacks
Technical, economic, and other factors assigned weights
Deterministic Guidelines
Random failures, attacks
Regulatory guidelines for identifying critical assets
Network Theory Probabilistic Graph Methods Game Theory
Summary for a selection of critical asset identification methods
22
Resilient Cities Critical Asset Identification
Graph Theory, Networks, and Extended Complex Networks • Graph theory used in computer networks, social networks, human brain • Networks are a set of nodes and edges (i.e. a graph) that describes a system being studied • Extended complex networks (ECNs) incorporate electrical properties into these otherwise abstract representations
Example of ECN that determines electrical distance between nodes
Application of ECN metric (electrical distance) yields ranking of critical nodes
23
Resilient Cities Critical Asset Identification
Future Work: Correlation Analysis • CEII limits availability of outage data and infrastructure information • Synthetic networks, simulated outage data provide interesting models • Examine how well metrics identify largest outages and long cascades from simulated dataset
ACTIVSg70k, a 70,000 bus synthetic model of Eastern US; no CEII constraints, generated from publicly available data
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Dynamic Grid Control Alvaro Cardoza Rui Hu Christian PERENYI Adam Emes 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Active Power Distribution Node Enhanced Reconfigurable Grids: Converter and Availability Performance Study Prepared by: Alvaro Cardoza Ph.D. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Dynamic Grid Control APDN: Converter and Availability Performance Study
Background/Motivation • The 21st century has witnessed a large growth in the penetration of DC-based generation and loads • Power system generation portfolios are becoming increasingly more diversified and distributed… • … In turn, the need for improving the electric grid’s power supply availability is vital • One solution for improving system availability is through developing reconfigurable distribution grids with the use of power electronics
DC Distribution Example – Data Center Telecom Network
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Dynamic Grid Control APDN: Converter and Availability Performance Study
Background/Motivation • The 21st century has witnessed a large growth in the penetration of DC-based generation and loads • Power system generation portfolios are becoming increasingly more diversified and distributed… • … In turn, the need for improving the electric grid’s power supply availability is vital • One solution for improving system availability is through developing reconfigurable distribution grids with the use of power electronics
Sources:
Reconfigurable Grid with Network of APDN
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
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Dynamic Grid Control APDN: Converter and Availability Performance Study
Background/Motivation • This work explores the use of a multiple-input, multiple-output (MIMO) DC-DC converter topology referred to as an Active Power Distribution Node (APDN) to demonstrate the availability benefits of reconfigurable distribution grids.
MIMO TMMC-APDN Converter Topology
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
29
Dynamic Grid Control APDN: Converter and Availability Performance Study
Availability Analysis The availability of an electrical system is defined as the probability that the system will be operational as desired over a specified period of time (typically with respect to a year).
Demonstration of Availability Analysis Using Minimal Cut Sets (MCS)
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Dynamic Grid Control APDN: Converter and Availability Performance Study
APDN Availability Test System •
The test system used to evaluate the availability and electrical performance of an APDN-integrated system
•
This simplified model represents a DC Information and Communication Technology (ICT) system, such as a datacenter or cell network, which is integrated with to a larger AC grid.
The Test System includes:
APDN Test System One-Line
•
Two APDNs
•
One DC Generation Source (DC System)
•
One AC Generation Source (AC Grid)
•
One System Energy Storage Unit
•
One Electrical Load
•
Two Rectifiers
31
Dynamic Grid Control APDN: Converter and Availability Performance Study
APDN Availability Test System •
The test system used to evaluate the availability and electrical performance of an APDN-integrated system
•
This simplified model represents a DC Information and Communication Technology (ICT) system, such as a datacenter or cell network, which is integrated with to a larger AC grid.
The Test System includes:
APDN Test System One-Line
•
Two APDNs
•
One DC Generation Source (DC System)
•
One AC Generation Source (AC Grid)
•
One System Energy Storage Unit
•
One Electrical Load
•
Two Rectifiers
32
Dynamic Grid Control APDN: Converter and Availability Performance Study
APDN Test System - Simulation Model and Results
Case 3
Case 2
APDN Test System Parameters
Case 1
Sources: a
Two APDN Test System
33
Dynamic Grid Control APDN: Converter and Availability Performance Study
APDN Test System - Simulation Model and Results
Case 3
Case 2 Case 1
Sources: a
Two APDN Test System
APDN Simulation Results
34
Dynamic Grid Control APDN: Converter and Availability Performance Study
APDN Availability Calculations Using reliability metrics published in the Military Handbook MIL-KDBK217F as well as those commonly accepted in literature, the availability of the different components of the TMMC-APDN were calculated.
Literature Data On Component And System Reliability/Availability Metrics
APDN Availability Calculations
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Gaming and Learning: Energy Management for Islanded Microgrids Powered by Renewable Sources Prepared by: Rui Hu Ph.D. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Dynamic Grid Control
Energy Management for Islanded Microgrids
Microgrid Energy Management Objectives: • Maintaining bus voltage/frequency. • Meet load demand. • Preserve energy for future operation.
Communication microgrid
vs.
? ? ?
Microgrid with residents and critical load users
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Dynamic Grid Control
Energy Management for Islanded Microgrids
Multiplayer Game Approach
Load and solar power curves
Battery SoC distribution
đ?‘‚đ?‘?đ?‘— = đ?‘¤đ?‘ž đ?œŽ + đ?‘¤đ?‘Ž đ?‘“(đ?‘†đ?‘œđ??ś) Player II đ?œŽ21
Player I đ?œŽ11 đ?œŽ12
Ďƒ11 Ďƒ12
Ďƒ21 U1 U3
đ?œŽ22
Ďƒ22 U2 U4
Strategic form game
38
Dynamic Grid Control
Energy Management for Islanded Microgrids
Reinforcement Learning Approach • No prior power or load curve data is needed. • High flexibility and resilience to environment changes. • Requires a training phase to obtain optimal performance. Learning curve of one base station implemented with RL algorithm
Learning scheme of one base station
39
Dynamic Grid Control
Energy Management for Islanded Microgrids
Load-Ratio Learning Game • Updating the ratio believe in each BS. • Two-level decision making process: load-ratio learning and immediate load control game
Load ratio belief 5%
95% User 1
User 2
Load ratio belief 40%
60%
User 1
đ??żđ?‘? = [đ?’‘1 , đ?’‘2 , ‌ , đ?’‘đ?‘€ áˆż
User 2
40
Dynamic Grid Control
Energy Management for Islanded Microgrids
Latest Simulation Results
Performance comparison of different algorithms.
Converging speed comparison of RL and learning-gaming algorithm
41
Analysis of the Frequency Response of a Novel Self-Synchronizing Inverter in a High Renewable Penetration Grid Prepared by: Christian PERENYI M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Dynamic Grid Control
Frequency Response Self-Synchronizing Inverter
Motivation and Problem “In most power system applications […] the phase angle information is of critical significance. In PLL techniques, however, the phase and frequency are both estimated within a single loop. This causes spurious frequency transients during phase angle changes.” S. Golestan, F. D. Freijedo, A. Vidal, A. G. Yepes, J. M. Guerrero and J. Doval-Gandoy, "An Efficient Implementation of Generalized Delayed Signal Cancellation PLL," in IEEE Transactions on Power Electronics, vol. 31, no. 2, pp. 1085-1094, Feb. 2016.
Would replacing the PLL make the frequency response of the inverter more stable?
43
Dynamic Grid Control Frequency Response Self-Synchronizing Inverter
Traditional and adaptive controlled inverter
Traditional three-phase inverters with control scheme
Adaptive three-phase inverters with control scheme
44
Dynamic Grid Control Frequency Response Self-Synchronizing Inverter
Grid modeling
Inverter
Synchronous Generator
Load Three-phase grid model
45
Dynamic Grid Control Frequency Response Self-Synchronizing Inverter
Results for Pr=80%
Normalized frequency response to a 10% load change Pr=80% & H=6,4,2
46
Voltage and Frequency Support Capabilities of Impedance-Source and quasi-Impedance-Source Inverters Prepared by: Adam Emes M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Presentation Outline 1. Overview of z-source inverter (ZSI) 2. Overview of quasi-z-source inverter (qZSI) 3. Modeling progress 4. Proposed research topic
48
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Overview of Z-Source Inverter (ZSI) Conventional Inverters
[1] F. Z. Peng, “Z-Source inverter,” IEEE Trans. Ind. Appl., vol. 39, no. 2, pp. 504-510, Mar./Apr. 2003.
Z-Source Inverter
49
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Overview of Z-Source Inverter (ZSI) Drawbacks of Conventional Inverters • Can only buck or boost voltage, depending on the type of source • V-source and I-source main circuits are not interchangeable • Accidentally engaging a forbidden state will destroy the devices Benefits of Z-Source Inverter • Can buck and boost voltage, regardless of source • Can be fed by a voltage source or current source • There are no longer any forbidden states in the converter
[1] F. Z. Peng, “Z-Source inverter,” IEEE Trans. Ind. Appl., vol. 39, no. 2, pp. 504-510, Mar./Apr. 2003.
50
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Overview of quasi-Z-Source Inverter (qZSI) Z-Source Inverter
Quasi-Z-Source Inverter
Advantages of qZSI: • Control-wise and operationally it is identical to the ZSI • Continuous input current • Reduces input stress. For example, it can reduce the capacitance for the output of PV panels • The voltage on C2 is less than C1, meaning C2 can have a lower voltage rating than in the ZSI [2] Y. Liu, H. Abu-Rub, B. Ge, F. Blaabjerg, O. Ellabban, and P. C. Loh, “Voltage‐Fed Z‐Source/ Quasi‐Z‐Source Inverters,” in Impedance Source Power electronic Converters, 1st ed., John Wiley & Sons, Ltd., 2016, pp. 20–34
51
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Modeling Progress • ZSI correctly modeled using simple boost SPWM control • All measured voltages line up with equations from literature
52
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Modeling Progress • qZSI correctly modeled using simple boost SPWM control • All measured voltages line up with equations from literature
53
Dynamic Grid Control Voltage and Frequency Support Capabilities of ZSI/qZSI Inverters
Proposed Research Topic • With the ZSI/qZSI gaining attention in distributed generation applications, how will it perform in grids with high DG penetration? • Model a “low-inertia” microgrid • One with H-bridge inverters • One with Z-source topologies • One with quasi-Z-source topologies • Compare frequency, power and voltage fluctuations of each grid after a fault or sudden/significant load change • Benchmark performance of each inverter to modern standards (IEEE 1547, EPRI documentation, etc.)
54
Control and Design of Power Electronics Zachary Smith Mohammed Hatatah Santino Fiorello Graziani Thomas Cook 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Power Transfer Relationships for a Coupled, Current-Fed, MultiPort Dual Active Bridge Converter Prepared by: Zachary T. Smith Ph.D. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Control and Design of Power Electronics Current-Fed, Multi-Port Dual Active Bridge Converter
Current-Fed Behavior • Two methods for establishing current-fed behavior in dual active bridge converters:
Inductance before switching
Inductance after switching [1]
[1] Y. Shi and H. Li, “Isolated Modular Multilevel DC-DC Converter With DC Fault Current Control Capability Based on Current-Fed Dual Active Bridge for MVDC Application,” IEEE Trans. Power Electron., vol. 33, no. 3, pp. 2145–2161, 2018.
57
Control and Design of Power Electronics Current-Fed, Multi-Port Dual Active Bridge Converter
Converter Features • • • • •
Galvanic isolation Bidirectional power flow Centralized control Fault tolerance Reduced switching loss
• Network re-configurability
Current-Fed, Multi-Port DAB [2] [2] Z. T. Smith and B. M. Grainger, "Analytical Treatment of the Power Transfer Relationships for a Coupled, Current-Fed, Multi-Port Dual Active Bridge Converter," 2019 IEEE Electric Ship Technologies Symposium (ESTS), Washington, DC, USA, 2019, pp. 562-568.
58
Control and Design of Power Electronics Current-Fed, Multi-Port Dual Active Bridge Converter
Converter Analysis đ?‘›
đ?‘Ł2 = đ??žđ?›˝2 đ??ż 1 đ?‘Ł1 +
đ??žđ?›˝2 đ?‘›2 +đ??ż2 đ??żđ?›ź2
1
đ?‘›
đ?‘Łđ??ż1 =
đ?‘›
�3
đ?‘›
−
đ??žđ?‘›1 đ?‘›2 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š2 đ??żđ?›ź2
đ??ż
đ??žđ?‘›1 đ?‘›2 đ?‘Ł1 đ??ż1 đ?›ź2
đ?‘Łđ??ż2 = đ??ż 2 đ??ż
đ??žđ?‘›1 đ?‘›3 đ?‘Ł1 đ??ż1 đ?›ź3
đ?‘Łđ??ż3 = đ??ż 3
�1 = ‍׏‏
đ?‘Łđ??ż1 đ?‘‘đ?‘Ą ; đ??ż1
đ??žđ?›˝3 đ?‘›3 +đ??ż3 đ??żđ?›ź3
�2
đ??ż1 −đ??žđ?‘›12 đ?‘Ł1 đ??ż1
−
đ?‘Łđ?‘Žđ?‘&#x;đ?‘š3
đ??žđ?‘›1 đ?‘›3 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š3 đ??żđ?›ź3
+
đ??žđ?‘›22 −đ??żđ?›ź2 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š2 đ??żđ?›ź2
+
+
đ??žđ?‘›2 đ?‘›3 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š2 đ??żđ?›ź2
đ??žđ?‘›32 −đ??żđ?›ź3 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š3 đ??żđ?›ź3
�2 = ‍׏‏
P2
đ?‘Łđ?‘Žđ?‘&#x;đ?‘š2 + đ??žđ?›˝2 đ??ż 3 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š3
đ?‘Ł3 = đ??žđ?›˝3 đ??ż 1 đ?‘Ł1 + đ??žđ?›˝3 đ??ż 2 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š2 + 1
P1
đ?‘Łđ??ż2 đ?‘‘đ?‘Ą ; đ??ż2
+
�3 = ‍׏‏
đ??žđ?‘›2 đ?‘›3 đ?‘Łđ?‘Žđ?‘&#x;đ?‘š3 đ??żđ?›ź3
P3
đ?‘Łđ??ż3 đ?‘‘đ?‘Ą đ??ż3
đ?‘ƒ1 = đ?‘Ł1 đ?‘–1 ; đ?‘ƒ2 = đ?‘Ł2 đ?‘–2 ; đ?‘ƒ3 = đ?‘Ł3 đ?‘–3 đ?‘ƒ1 = đ?‘ƒ2 + đ?‘ƒ3 .
Current-Fed, Multi-Port DAB [2] [2] Z. T. Smith and B. M. Grainger, "Analytical Treatment of the Power Transfer Relationships for a Coupled, Current-Fed, Multi-Port Dual Active Bridge Converter," 2019 IEEE Electric Ship Technologies Symposium (ESTS), Washington, DC, USA, 2019, pp. 562-568.
59
Control and Design of Power Electronics Current-Fed, Multi-Port Dual Active Bridge Converter
Component Size Analysis • Ldc and L affect converter behavior • Port voltage waveform shape • Power transfer • Analyzed converter performance vs ratio of mutual inductance to transformer winding inductance (Ldc/L)
Current-Fed, Multi-Port DAB [2]
60
Control and Design of Power Electronics Current-Fed, Multi-Port Dual Active Bridge Converter
Component Size Analysis
Z. T. Smith and B. M. Grainger, "Analytical Treatment of the Power Transfer Relationships for a Coupled, Current-Fed, Multi-Port Dual Active Bridge Converter," 2019 IEEE Electric Ship Technologies Symposium (ESTS), Washington, DC, USA, 2019, pp. 562-568.
61
Control and Design of Power Electronics Current-Fed, Multi-Port Dual Active Bridge Converter
RTDS Simulation • Real-Time Digital Simulation (RTDS) invertergrid model developed • Supplements University of Louisville research: • Self-synchronizing control of a 3-phase grid-connected inverter • Co-author - APEC 2020 • Future Work: • RTDS model of the current-fed, multi-port, DAB converter within a small grid network
62
LQR Approach for Regulating Voltage and Power of a Medium Voltage Quad Active Bridge Solid State Transformer Prepared by: Mohammed Hatatah Ph.D. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Control and Design of Power Electronics LQR Approach for Regulating Voltage and Power of QAB based SST
Introduction •
A challenge identified from within the literature is balancing the voltages for each of the ports on the MV side of the QAB unit, labeled b, c, and d in Figure 1.
•
The control system implemented in this work makes use of linear quadratic regulator (LQR) based state feedback approaches to perform the regulation. SST architecture using the QAB module for the dc-dc.
64
Control and Design of Power Electronics LQR Approach for Regulating Voltage and Power of QAB based SST
Proposed power balance control •
The duty cycle of each MV bridge will have a nominal value and dynamic component defined by (1) where đ??ˇ1 and đ??ˇ2 are calculated with (2) .
•
The duty cycle đ??ˇ2đ?‘? , đ??ˇ2đ?‘? and đ??ˇ2đ?‘‘ are updated with the LQR algorithm. Two operation scenarios of đ?‘–đ?‘– were examined analytically (3), as presented in Figure 2.
Current waveform according to the change of ∆đ??ˇ2đ?‘– .
65
Control and Design of Power Electronics LQR Approach for Regulating Voltage and Power of QAB based SST
Proposed power Distribution control •
Using LQR technique, we can get the state feedback matrix. Equations (4) and (5) bridge đ?‘Łđ?‘? , đ?‘Łđ?‘? , đ?‘Łđ?‘‘ and đ?‘ƒđ?‘?2 , đ?‘ƒđ?‘?2 , đ?‘ƒđ?‘‘2 .
•
The duty cycle for the inner DC-DC converter is obtained to balance the voltage as shown in Figure 3.
Structure of State Feedback Control.
66
Control and Design of Power Electronics LQR Approach for Regulating Voltage and Power of QAB based SST
Results
Measured voltages on the MV and LV side.
Power references and measurements on the MV side.
67
A Flying Capacitor Multi-level Flyback Converter (FCMFC) Prepared by: Santino F Graziani Ph.D. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Control and Design of Power Electronics A Flying Capacitor Multi-level Flyback Converter
Motivation • Increased power electronic efficiencies are partly the reason for flat energy consumption
Organization for Economic Co-operation and Development
69
Control and Design of Power Electronics A Flying Capacitor Multi-level Flyback Converter
Motivation • Demand for efficient and small power conversion ✓ Renewable energy (solar, wind, tidal, energy storage) ✓ Aerospace ✓ Transportation ✓ Telecommunications
Solar and Wind
Tidal
Batteries
70
Control and Design of Power Electronics A Flying Capacitor Multi-level Flyback Converter
A new power conversion topology • A flyback converter is modified to operate with multiple output stages • Voltage gain increases significantly Ll Vin
D1 IL
Lm 1:n
S
…….. D(N-1) Co
C1 …….. S1
L O A D
+ V -
S(N-1)
General Form for N-level FCMFC
Voltage Gain
Voltage Gain Plotted for multiple N
71
Control and Design of Power Electronics A Flying Capacitor Multi-level Flyback Converter
Benefits • Voltage stress spread across multiple stages • Lower switching Losses
Equivalent Output Circuit
đ?‘ƒđ?‘†đ?‘œ
đ??źđ??ż 2 1 ′ = đ?‘…đ?‘‚đ?‘ 2 đ?‘ − 2 đ??ˇ + đ?‘‰đ??ź(đ?‘Ąđ?‘&#x;đ?‘–đ?‘ đ?‘’ + đ?‘Ąđ?‘“đ?‘Žđ?‘™đ?‘™ )đ?‘“đ?‘ đ?‘› 2 Output Switching and Conduction Loss
đ?‘ƒđ?‘† = đ?‘…đ?‘† đ??źđ??ż Main Switching Stress vs Gain
2
1 đ??ˇ + đ?‘‰đ??ź(đ?‘Ąđ?‘&#x;đ?‘–đ?‘ đ?‘’ + đ?‘Ąđ?‘“đ?‘Žđ?‘™đ?‘™ )đ?‘“đ?‘ 2
Input Switching and Conduction Loss
72
Control and Design of Power Electronics A Flying Capacitor Multi-level Flyback Converter
Benefits • Higher N-level converters tend towards continuous conduction mode • Lower inductance required 2đ??żđ?‘š đ??ž= đ?‘…đ?‘‡đ?‘ đ??ž>
1−đ??ˇ đ?‘› đ?‘ −1
đ??żđ?‘š = 2
đ?‘‰đ?‘–đ?‘› đ??ˇ ྍđ?‘ − 1)đ?‘“đ??šđ??śđ?‘€đ??šđ??ś ∆đ??źđ??ż
= đ??žđ?‘?đ?‘&#x;đ?‘–đ?‘Ą (đ??ˇ, đ?‘›, đ?‘ )
Boundary Conduction Curves (Kcrit)
Required Inductance Normalized
73
Control and Design of Power Electronics A Flying Capacitor Multi-level Flyback Converter
Future Plans Prototype PCB Designs Using Altium Designer N = 2, 3, 4, 5 Level FCMFCs Validate gain and BCM trends Analyze loss mechanisms • Compare to current loss models • Use these insights to fuel a large scale design optimization • • • • •
Ll Vin
D1 IL
Lm 1:n
S
…….. D(N-1) Co
C1 …….. S1
L O A D
+ V -
S(N-1)
General Form for N-level FCMFC
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Evaluation of Point of Load Converters for Space Computational Loads Prepared by: Thomas Cook M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Control and Design of Power Electronics Evaluation of Point of Load Converters for Space Computational Loads
Motivation • SHREC Space Processor (SSP) • 10cm x 10cm reconfigurable hybrid space processor • High efficiency, power dense converter modules • GaN Radiation Performance • Schottky metal gate reduces chances of Total Ionizing Dose (TID) • Wide band-gap requires high energy particles to achieve ionization Collaborative Project between Electric Power Systems Lab and NSF Center for Space, High - Performance, & Resilient Computing
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Control and Design of Power Electronics Evaluation of Point of Load Converters for Space Computational Loads
Converters Under Test
• 5Vin to 1.0V, 1.8V or 3.3Vout • LTC3833 and GaN HEMTs • Rad-hard TPS50601A-SP, ISL70001ASEH • LTC7151S • GaN HEMTs • EPC 2014C • EPC 2015C • GaN Systems GS61004B
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Control and Design of Power Electronics Evaluation of Point of Load Converters for Space Computational Loads
PCB Layout of Converters Linear Technologies LTC3833 EPC 2014C
Renesas ISL70001ASEH (Rad-hard)
Linear Technologies LTC3833 GaN Systems GS61400B
Texas Instruments TPS50601A-SP
Linear Technologies LTC3833 EPC 2015C
Linear Technologies LTC7151S
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Control and Design of Power Electronics Evaluation of Point of Load Converters for Space Computational Loads
Switching Waveforms Load change transient of 6A with maximum output voltage sag of 18.017mV
Gate drive voltage signals for top gate (yellow) and bottom gate (red)
Average voltage of 3.3V with ripple value of 2.5mV
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Control and Design of Power Electronics Evaluation of Point of Load Converters for Space Computational Loads
SSIVP Performance 85%
Average Efficiency
• Consistent output voltage and current • No observance of single event fault • No change when operating during South Atlantic Anomaly • No change in performance from Total Ionizing Dose over time of operation
Efficiency Over Time 85% 84% 84% 83% 83% 82%
Date of Measurment
Future Work
LTC3833 EPC Efficiency TI LM25141-Q1 EPC Efficiency
LTC3833 Teledyne Efficiency
• LANSCE radiation testing of LTC3833 and GaN HEMTs in November 2019
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Modern Grid Challenges Thibaut HARZIG Ryan Brody Jenna DeLozier Corey Weimann 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Sequence Model of a Grid-Tied Current Controlled Inverter Prepared by: Thibaut Harzig Ph.D. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Modern Grid Challenges Sequence Model of a Grid-Tied Current Controlled Inverter
Research Motivation • More Inverter-Based Generations are connected to the Grid due to the increasing penetration of Renewables • Symmetrical Components were developed in a time where rotating machines were the dominant Power Sources • This project aims at proposing a new Sequence Model for a Current Controlled Inverter • The proposed model predicts the asymmetry of the Inverter’s Output Current as well as the fault current resulting from a Single Line-to-Ground Fault (SLGF)
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Modern Grid Challenges Sequence Model of a Grid-Tied Current Controlled Inverter
Grid-Tied Current Controlled Inverter • Regulation of the Positive Sequence Output Current using References in Synchronously Reference Frame (Itd, Itq) • Voltage across the Capacitor C used as a Feedforward signal to act on the voltage across L1
Grid-Tied Current Controlled Inverter
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Modern Grid Challenges Sequence Model of a Grid-Tied Current Controlled Inverter
Classical Model From Power System Theory • The Current Controlled Inverter is assumed to provide a positive sequence Current even during the Single Line-to-Ground Fault • The Pre-Fault Current Magnitude is negligible compared to the fault Current Magnitude
Classical Sequence Model of the Grid-Tied Current Controlled Inverter experiencing a SLGF
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Modern Grid Challenges Sequence Model of a Grid-Tied Current Controlled Inverter
Proposed Model • Resistance R in series with L1 and L2 placed in parallel with Rv is the critical difference • Inverter supplies a Zero Sequence Current resulting in imbalances in the output current of the inverter
Proposed Sequence Model of the Grid-Tied Current Controlled Inverter experiencing a SLGF
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Modern Grid Challenges Sequence Model of a Grid-Tied Current Controlled Inverter
Simulation Results • The Inverter’s Output Current is no longer balanced • Zero Sequence Component Output Current can be predicted by the Proposed Model and match the Simulation Results from Inverter Model Simulation on PLECS
Simulation of Inverter Current under SLGF
Comparison of Classical Model, Proposed Model and Simulation Results
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High Power Density, High Efficiency Motor Drive for Electric Vehicle Applications Prepared by: Ryan Brody M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Modern Grid Challenges High Power Density, High Efficiency Motor Drive for EVs
Important Metrics to Optimize in Power Electronic Systems • Weight and losses decrease EV mileage. • EVs usually operate at low power, where converters are least efficient.
Variables to be optimized in PWM converters [1]
Factors affecting converter volume [1]
[1] J. W. Kolar et al., "PWM Converter Power Density Barriers," 2007 Power Conversion Conference - Nagoya, Nagoya, 2007, pp. P-9-P-29.
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Modern Grid Challenges High Power Density, High Efficiency Motor Drive for EVs
Typical Components of EV Powertrain • Controlling different modules cooperatively could improve system level power density and efficiency.
Main components of EV powertrain – a battery, DC/DC converter, DC/AC inverter and a permanent magnet synchronous motor (PMSM) [2] [2] H. Chen, H. Kim, R. Erickson and D. Maksimović, "Electrified Automotive Powertrain Architecture Using Composite DC–DC Converters," in IEEE Transactions on Power Electronics, vol. 32, no. 1, pp. 98-116, Jan. 2017. doi: 10.1109/TPEL.2016.2533347
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Modern Grid Challenges High Power Density, High Efficiency Motor Drive for EVs
Open-end Winding PMSM (OW-PMSM) • Enables high speed operation without increasing the battery voltage. • Can integrate high power charging. • Differing the voltage applied to each terminal affects the THD.
đ?‘‰đ?‘Ž = đ??źđ?‘Ž đ?‘?đ?‘Ž + đ??¸đ?‘–đ?‘Ž đ?‘‰đ?‘? = đ??źđ?‘? đ?‘?đ?‘? + đ??¸đ?‘–đ?‘? đ?‘‰đ?‘? = đ??źđ?‘? đ?‘?đ?‘? + đ??¸đ?‘–đ?‘?
Circuit diagram and phase voltages of a conventional PMSM
(đ?‘‰đ?‘Ž1 −đ?‘‰đ?‘”1 ) − (đ?‘‰đ?‘Ž2 − đ?‘‰đ?‘”2 ) = đ??źđ?‘Ž đ?‘?đ?‘Ž + đ??¸đ?‘–đ?‘Ž (đ?‘‰đ?‘?1 −đ?‘‰đ?‘”1 ) − (đ?‘‰đ?‘?2 − đ?‘‰đ?‘”2 ) = đ??źđ?‘? đ?‘?đ?‘? + đ??¸đ?‘–đ?‘? (đ?‘‰đ?‘?1 −đ?‘‰đ?‘”1 ) − (đ?‘‰đ?‘?2 − đ?‘‰đ?‘”2 ) = đ??źđ?‘? đ?‘?đ?‘? + đ??¸đ?‘–đ?‘?
Circuit diagram and phase voltages of an OW-PMSM
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Modern Grid Challenges High Power Density, High Efficiency Motor Drive for EVs
Future Work 1.
Develop a control algorithm that varies the DC link voltage of the OWPMSM dual inverter drive to operate more efficiently at low power. 2. Demonstrate benefits of using said control algorithm. 3. Add integrated, high power DC and AC charging and V2G capabilities
PLECS Model of EV Powertrain using OW-PMSM
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Electric Vehicle Charging Impact on Distribution Feeder Model and Mitigation Techniques Prepared by: Jenna DeLozier M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Modern Grid Challenges Electric Vehicle Charging Impact on Distribution
Overview • • • •
Electric Vehicles are becoming more mainstream Transmission is monitored at a higher level than distribution Distribution will need to be monitored Research uses vehicle projections, line loading, and battery capacity projections
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Modern Grid Challenges Electric Vehicle Charging Impact on Distribution
Considerations EV Purchase Rate:
Miles per Battery:
Load per Home:
Charging Rates • Case 0: No charging • Case 1: Low charging, charge at 1/8 of battery • Case 2: Medium charging, charge at 1/2 of battery • Case 3: High charging, charge every day
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Modern Grid Challenges Electric Vehicle Charging Impact on Distribution
Simulation Results Loading Percentage
Dove 2.4 Comparison Unbalanced 1.60E+00 1.40E+00 1.20E+00 1.00E+00 8.00E-01 6.00E-01 4.00E-01 2.00E-01 0.00E+00
Dove 2.4 TFMR off Dove 2.4 TFMR on Dove 2.4 Line off Dove 2.4 Line On 0
1
2
3
Charge Case
Loading Percentage
Dove 2.4 Comparison Balanced 1.00E+00 8.00E-01 6.00E-01
Dove 2.4 TFMR off
4.00E-01
Dove 2.4 TFMR on
2.00E-01
Dove 2.4 Line off Dove 2.4 Line On
0.00E+00
0
1
2
3
Charge Case
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Modern Grid Challenges Electric Vehicle Charging Impact on Distribution
Conclusion and Further Research • Line loading in terms of ampacity is the limiting factor for EV charging • No one solution will fix this problem, ie reconductoring or EV scheduling • Proposed combinational solution • Reconductor main lines, monitor if reconductoring is impossible
Example Logic Diagram
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DC Arc Flash Experimental Testing and its Need in Workplace Safety Prepared by: Corey Weimann M.S. Student 14th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 28th, 2019
Modern Grid Challenges DC Arc Flash Incident Energy Calculation Summary
Motivation • • • •
Increase in PV and Plug-In Electrical Vehicles Safety Arc Flash Incident Energy is Established for AC (IEEE 1584 – 2018) NFPA 70E
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Modern Grid Challenges Common DC Arc Flash Incident Energy Calculation Methods
Max Power Transfer Methods Doan’s Method •
Based on Max Power Transfer
•
Developed Conservative Incident Energy Equation
NFPA 70E 2015 Method •
Electrical Safety in the Workplace
•
Provides Arcing Current and Incident Energy Equations
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Modern Grid Challenges Current Research
Experimental Research Goals • Minimum Short Circuit Level for 125 VDC Systems • Testing at: • 125 and 130 VDC • 0.5 to 27 kA • Vary Short Circuit Level and Bus Gap
• Conduct Experiment Using a Case Study • Power Generating Facility Control System • Compare Results of Testing to ETAP
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Modern Grid Challenges Current Research
Test Setup • Power is Supplied via Rectifier • Bus Bar Will be Shorted via Pilot Wire • VCBB Electrode Configuration
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Modern Grid Challenges DC Arc Flash Incident Energy Calculation Summary
Conclusion • Need Accurate Fault Current Calculations • Higher PPE Requirements • More Testing • Standard for DC Arc Flash Calculations Grid Tie
PV Plant Microgrid Disconnect
~ Commercial Load and PV Generation =
~
Community Energy Storage
=
Residential Load and PV Generation ~
=
~
~
=
=
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Thank You!