RT15 Berkeley | Grid Intergration Group - Lawrence Berkeley National Lab
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The document describes the Grid Integration Group at Lawrence Berkeley National Laboratory (LBNL). It includes: 1) The team leads and core members of the group, focused on areas like smart inverter control, microgrid design, vehicle-to-grid integration, and EV modeling. 2) Key projects and research areas of the group, including developing microgrid control technologies, modeling distributed energy resources, testing smart inverters, and using electric vehicles for grid services. 3) Partnerships and collaborations with other institutions to advance research in grid integration challenges like improving distribution system modeling and planning tools.
RT15 Berkeley | Grid Intergration Group - Lawrence Berkeley National Lab
- 1. 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 [email protected] or [email protected] http://gig.lbl.gov/ https://building-microgrid.lbl.gov/ http://v2gsim.lbl.gov http://powertrains.lbl.gov
- 2. Grid Integration Group Focus Areas at LBNL 2 •Multi-objective smart inverter control with micro-synchrophasor data •FLEXLAB Pilot test facility •VirGIL (Virtual Grid Integration Lab) •Contact: Emma Stewart, [email protected] 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, [email protected] 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, [email protected] Vehicle-to-Grid Integration and Demand Response •Powertrain Modelling (not only EVs) •V2GSim •MyGreenCar •Contact: Samveg Saxena, [email protected] EV Modeling and Simulation
- 3. 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
- 4. 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 4
- 5. 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
- 6. 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?
- 7. 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. 7
- 8. A 6 to 8 kW, 3-phase load behind Bank 514 that oscillates at 10 Hz tripped from the voltage sag We also see the voltage sag occur before the current increase benefit of high accuracy time synchronized data
- 9. 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
- 10. 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
- 11. 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
- 12. 12 ● 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
- 13. 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) 13
- 14. 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 14 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
- 15. Thanks! 15