Post on 24-Jul-2015
Grid Integration Group
Team leads: Michael Stadler, Emma Stewart
Support: Deborah Rabuco
Area leads: Michael Stadler, Emma Stewart, Samveg Saxena, Doug Black
Core team: Peter Alstone, Dan Arnold, Duncan Callaway, Gonçalo Cardoso, Spyridon Chatzivasileiadis, Nicholas DeForest, Girish Ghatikar, Emre Kara, Anna Liao, Salman Mashayekh, Nance Matson, Jason McDonald, Janie Page, Rongxin Yin
Affiliates: Thibault Forget, Nikky Avurila, Tim Schittekatte, Ryan Tulabing
Mstadler@lbl.gov or EStewart@lbl.govhttp://gig.lbl.gov/https://building-microgrid.lbl.gov/http://v2gsim.lbl.govhttp://powertrains.lbl.gov
Grid Integration Group Focus Areas at
LBNL
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•Multi-objective smart inverter control with micro-synchrophasor data
•FLEXLAB Pilot test facility
•VirGIL (Virtual Grid Integration Lab)
•Contact: Emma Stewart, EStewart@lbl.gov
Advanced Sensing Modeling and Short Term Control in
the Distribution Grid
•DER-CAM (Distributed Energy Resources Customer Adoption Model)
•Microgrid Design Tools
•Microgrid controller deployment
•Contact: Michael Stadler, Mstadler@lbl.gov
Microgrid Supervisory Control and Resource
Coordination
•EVs as storage and vehicle to grid integration
•EV smart charging and DR
•Automated DR technologies, tools, and standards (OpenADR)
•Contact: Doug Black, drblack@lbl.gov
Vehicle-to-Grid Integration and Demand Response
•Powertrain Modelling (not only EVs)
•V2GSim
•MyGreenCar
•Contact: Samveg Saxena, SSaxena@lbl.gov
EV Modeling and Simulation
Background
Vision for a future electricity grid in California and the U.S. involves
increasing the use of renewable generation on the distribution grid.
With large numbers of distributed generation units, including solar PV,
the future grid will have more complex analysis needs and development
of new control architectures.
The distribution system has more components than the transmission
system and therefore more unknowns and potential for error To facilitate
high penetration of DG, measured and modeled representations of
generation must be accurate and validated, giving distribution planners
and operators confidence in their performance
Berkeley Lab GIG Partners
Massachusetts Institute of Technology (MIT), EPRI,
Enernex, Arizona State University, Metropolitan
Washington Council of Governments, Brookhaven
National Laboratory, Fort Hunter Liggett, TriTechnic,
MIT Lincoln Laboratory, University of New Mexico,
Public Service New Mexico, Universidad Pontificia
Comillas – IIT, Xcogen Energy LLC, CSIRO, NEC,
Tesla, SolarCity, PSL, CIEE, SunEdison, Riverside
PU, SCE, PG&E
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Advanced Sensing, Modeling and
Control From a planning analysis standpoint, there are three related barriers to
the integration of renewables to the distribution grid:
The lack of tools to adequately represent high penetration levels and
advanced control strategies for distributed resources;
The lack of accuracy and trustworthiness of models , often due to limited
availability of data for their validation; and
The limited accuracy of measured data sources in and of themselves, for
control and validation purposes
Inaccurate distribution circuit models either over- or under-estimate DG
impacts, leading to:
Higher costs to utilities and customers from unnecessary or, worse,
inadequate mitigation measures
Compromised safety and poorer power quality
Ultimately, slowing down the rate of PV deployment
Development of pilot μPMU measurement
network at LBNL – ARPA-E Project
• Development of a network of high-precision
phasor measurement units (μPMUs) for
distribution systems (Power Standards
Laboratory developed the uPMUs)
• Key goal is to develop advanced
visualization, characterization and short
term control for distributed resources
• Measurements of voltage and current
magnitude and phase angle 512/samples
per cycle
• Evaluating the requirements for µPMU data
to support specific diagnostic and DG
control applications
• Exploring applications of μPMU data in
distribution systems to improve operations,
increase reliability, and enable integration of
renewables and other distributed resources
• Installed μPMU devices in 6 locations at LBNL from substation (feeder head) to Building 71
• Data collection is integrated with historian and sMAP interface
• First micro-PMU network to be installed on a real electrical grid, developing unique capabilities at LBNL
• Future objective to expand network lab wide
Research Question: Can synchronized distribution level phasor measurements enhance planning for power flow and system control, security and resiliency in the modernized grid?
Distribution (or µ) PMU Offer a Means for
Improving Distribution Planning Modeling
What is a distribution PMU?
A PMU is a Phasor Measurement Unit
Measures time synchronized voltage and phase angle at high sample rates ~30/second for
transmission and 120/second for distribution
The µ-PMU is a power quality recording instrument with GPS receiver to enable highly accurate time
stamping for voltage and phase angle measurement
Conventional PMUs in use for the transmission system have ± 1o accuracy; µPMU have 0.01o
Higher degree of accuracy is required for distribution as the angle differences and changes are
significantly smaller than in transmission because of the different X/R ratios
Why are we using them for this project?
Measurement of phase angle and difference in angle between points provides the ability to calculate
impedance not possible without the PMU
Phase angle also gives information on the direction of power flow for analysis of topology changes or
errors
Line level measurement represents an improvement over smart metering for estimating loads on a
per phase basis.
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A 6 to 8 kW, 3-phase load behind Bank 514 that oscillates at 10 Hz tripped from the voltage sagWe also see the voltage sag occur before the current increase
benefit of high accuracy time synchronized data
Cyber Security of Power Distribution
Systems
• Purpose: Use physical measurements from
µPMUs to detect cyber attacks against the
distribution grid aiming to disrupt safe and normal
operation of substation components or mask
consequence of malfunctioning substation
components, or disrupt key communications
through denial-of service cyber attacks.
• Challenge: Model expected state of distribution
grid and determine appropriate locations to
capture distribution network readings in order to
detect deviations.
Supporting Cyber Security of Power Distribution
Systems by Detecting Differences Between Real-time
Micro-Synchrophasor Measurements and Cyber-
Reported SCADA
VirGIL Co-Simulation Framework
• Simulation of advanced
communication and control
parameters is key for future distributed
resource integration
• Current commercial power system
simulation tools do not consider
demand response, electric vehicles,
and communication in concert
• VirGIL integrates these key parameters
for optimizing technical capabilities of
inverter and distributed resources
• Platform for LBNL tools for buildings,
PV inverters and demand response can
be integrated on commercial power
system simulation software
Overview of Smart Inverter Control
Project• Control of an advanced PV inverter storage systems and load using data collected from the LBNL
distribution μPMU network at the LBNL test bed, the Facility for Low Energy Experiments (FLEXLAB)
• Conduct applied research using the integrated PV system with relevant conditions or anomalies on LBNL
distribution feeders requiring mitigating strategy, including voltage regulation and sags/swells, reverse
power flow, and local thermal impacts
• Using the Virtual Grid-Integration Laboratory (VirGIL) co-simulation platform and validate it with the
μPMU data
• Using this demonstration system, we design and enable multi-objective control functionality for both
mitigation and control of voltage and variability issues in high-penetration scenarios, while optimizing
economic operation for zero net energy (ZNE) commercial facilities
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● Investment & Planning: determines optimal equipment combination and operation based on
historic load data, weather, and tariffs Microgrid Design Tool
● Operations: determines optimal week-ahead scheduling for installed equipment and
forecasted loads, weather and tariffs Controller
Vehicle-to-Grid Integration and
Demand Response Increasing penetration of renewable energy and electrifying transportation are
major components of aggressive state and federal GHG reduction initiatives
High penetration of RE requires energy storage, which EVs can provide
EVs have potential to provide needed storage, but present unique challenges in that they are not in
fixed locations, not continuously connected, and must meet transportation needs
High penetration of EVs requires charging control that minimizes impact on
distribution points and the grid overall
Beyond minimizing the impact of electrified transport on the grid, EVs can
benefit the grid by providing needed grid services and DR resources
The uncertain impact that REs and EVs will have on net loads (i.e. the “duck”
curve) requires automated control of demand response resources
Research focuses on EVs as storage and vehicle to grid integration, EV smart
charging and DR, Automated DR technologies, tools, and standards (OpenADR)
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V2G integration provides rapidly-responding energy storage resources to the grid via markets that can generate revenue for EV owners
Los Angeles Air Force Base:
- V2G with 42 EVs participating in day-ahead CAISO ancillary services market requiring 4-second response
63rd RSC Army Reserve:
- V1G coordinated with building loads to participate in day-ahead hourly transactions with CAISO
EVs for Grid Storage and Services
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Front end interface and databases for PEV fleet management and tools for charging services.
Fleet Management
Optimal scheduling of the PEV fleet using Distributed Energy Resource Customer Adoption (DER-CAM) Model.
Simulation/Modeling
Participate in DR and Ancillary Services markets using the U.S. Smart Grid standard, OpenADR.
OpenADR (DRAS)
Fleet
Management
Thanks!
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