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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