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Information Backbone for a Sustainable Electric Power System

Information Backbone for a Sustainable Electric Power System. Rob Pratt Pacific Northwest National Laboratory. Information technology will profoundly transform the planning and operation of the power grid …. University of Washington Seminar December 2007. Today’s Discussion.

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Information Backbone for a Sustainable Electric Power System

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  1. Information Backbone for a Sustainable Electric Power System Rob PrattPacific Northwest National Laboratory Information technology will profoundly transform the planning and operation of the power grid … University of Washington Seminar December 2007 1

  2. Today’s Discussion • Introduction to the GridWise program at PNNL • Market-based demand response for managing peak loads • Demand response for reliability – Grid Friendly™ Appliances • GridLAB-D simulation of smart grid technologies and operations • Interoperability for integrating demand response and other distributed resources • Smart grid’s role in sustainable electric power system • Impact of high penetration of plug-in hybrid electric vehicles • Electric Infrastructure Operations Center and advanced transmission R&D 2

  3. DistributionLinemen Energy Service Co.s, Vendors, Utility Programs Customer Appliances, Equipment, Processes Communicate – With Whom? About What?Customer Perspective of a GridWise World capacity, avail-ability, price, forecast, contract terms , DG incentives audit results, retrofit opportunities, designs, costs, terms & conditions voltage, DG/storage status Gen, T, & D Suppliers grid status level, power/ end-use rations Aggregators billing, info access, attractive contracts, approvals, occupancy, performance EmergencyOperations power require-ments, forecasts, status, curtailment 3

  4. Fully-Engaging Demand Response in Grid Operations Rob Pratt PNNL Energy Sciences & Technology Directorate

  5. Value of Demand Elasticity: Lower Peak Demand & Stabilize Prices Price Demand (elastic) Demand Price($/MW) (inelastic) Price, mitigated Supply Quantity (MW) 5

  6. generation distribution 5% = ~400 hrs/yr (8,760 hrs) How Does Managing Peak ElectricalDemand Save Money? Hourly Loads as Fraction of Peak, Sorted from Highest to Lowest • 25% of distribution & 10% of generation assets (transmission is similar), worth of 100s of billions of dollars, are needed less than 400 hrs/year! 90% 75% 5% 6

  7. Pacific NW GridWise™ Testbed Projects Yakima Gresham Olympic Peninsula Olympic Peninsula GridWise Demonstration Grid Friendly™ Appliance Demonstration Two Current Projects: 7

  8. Olympic Peninsula Project Team 8

  9. IBM Invensys $ MW ancillary services distribution congestion transmission congestion wholesale cost Johnson Controls JohnsonControls Olympic Peninsula Demonstration Market $/kWh 0 6 12 18 24 Clallam County PUD Water Supply District 0.2 MW DR Internet broadband communications Clallam PUD & Port Angeles n = 120, 0.5 MW DR Sequim Marine Sciences Lab 0.3 MW DR 0.5 MW DG 9

  10. Economic Experiment Approach • Establish, offer and compare three retail contract types: fixed-price, time-of-use, real-time (5-minute) price • Provide automation so residents can configure space conditioning and water heating for their relative comfort vs. economy preferences • Establish a shadow market and compensate residents for the degree to which they respond to price signals (i.e., energy cost savings) • Residents receive “grant” with which to pay shadow market electric bill, and keep the balance (they pay their regular electric bill as usual) 10

  11. RTP Control with “Virtual Thermostat”(Cooling Example) Price User sets: Tdesired, comfort vs. economy setting These imply: Tmax, Tmin, k k Pavg + k Pbid Pavg Pclear Pavg - k Temperature Tmin Tset Tdesired Tcurrent Tmax Smaller k: lower comfort, higher demand response, higher savings Larger k: higher comfort, lower demand response, lower savings 11

  12. Managing a Transmission or Distribution Constraint with Energy Prices in a Series of Peak Load Events DG required above feeder limit Market failed to meet demand for one 5-min. interval in 3-day cold snap 12

  13. . Loads and Reserves on a Typical U.S. Peak Day Resident. (non-GFA) 12% GFA* potential exceeds US operating reserve requirements! Industrial 28% Residential (GFA*) 18% Operating reserves 13% Commercial 29% * GFA for: heat, AC, HW, refrigerators, freezers Grid Friendly™ Appliances (GFAs) Help Keep the Lights On! Grid Friendly Appliances sense grid frequency excursions & control region’s appliances to act as spinning reserve – No communications required! 13

  14. Stabilization Potential from Frequency Responsive Load Bus 25 frequency @ t = 1 sec: loads +5% @ t = 40 sec: loads +15% from Trudnowski et al. IEEE PES. 2005. (http://gridwise.pnl.gov/docs/pnnlsa44073.pdf) 14

  15. Grid Friendly™ Appliance Demonstration • Autonomously detects under-frequency events, sheds load for up to a few minutes • 150 new Whirlpool clothes dryers, 50 retrofitted water heaters • No one noticed in hundreds of curtailment events! • An ancillary service that can displace spinning reserves and increase reliability • Reacts within 1/2 second • Delays & randomizes service restoration to avoid shocking the grid, eases cold load pickup after an outage • Low cost: no communications required • Vast, inexpensive grid “safety net” “When the inevitable occurs … people get stuck in elevators and high-value uses of power are shut off along with all the lowest priority uses of energy. It's the meat-ax approach to interrupting power flows.” Dr. Vernon Smith, 2002 Nobel prize Winner, Economics 15

  16. All GFAs Respond Within Seconds of Event 16

  17. What we’ve learned Important insights from our Northwest demo project • Residential customers will respond to 5-min. real-time prices (RTP) if provided opportunity to save 10%, and with technology that makes it simple to automate their preferred response • Demand response + distributed generation provided sustained, local distribution peak load reduction (in addition to wholesale market benefits) • able to cap net demand at an arbitrary level, ~15% less than the normal annual peak; will respond for several sequential days • real capital cost savings when a $5-10M substation can be deferred or downsized • A portfolio of RTP, TOU, & fixed rates may be optimal • RTP focuses demand response just when it is needed • Time-of-use (TOU) resulted in efficiency gains • RTP and TOU can be implemented without an actual rate change, and with “no losers” compared to flat rates, by debiting against an up-front credit 17

  18. What we’ve learned (cont.)Important insights from our Northwest demo project • Using demand to provide short-term regulation is a simple, inexpensive byproduct of an RTP system (i.e., can help manage high penetration of intermittent wind generation) • can easily synchronize thermostatically controlled loads to follow grid’s need for regulation over the short term (minutes) • excursions from desired setpoints are very small • minimal if any discomfort means costs to buy response are very low • likely at far lower costs than power plants charge to ramp up/down • Autonomous under-frequency/under-voltage load shedding from Grid Friendly™ appliances was reliable and not noticed by users 18

  19. GridLab-D – a Collaborative, Open Source Environment for Simulating the Smart Grid Detailed, simultaneous simulation of power flow, end use, and market functions and their interactions Evaluate the potential of new technologies and distribution system operational strategies to: save capital costs improve reliability provide other benefits Craft and refine the characteristics of technologies and operational strategies to provide maximum benefit at lowest cost Understand and quantify the synergies of deploying a broad range of smart grid technologies Avoid unintended consequences that can result from utilizing distributed control systems Predict and evaluate results from deployment projects 19 19

  20. Planning Maintenance HVACControl Operations Accounting Billing GridWise Architecture Council Linking Islands of Enterprise Integration the Building+Grid Enterprise??? the Building Enterprise the Grid Enterprise OR with a minimal Transactive Interface Contract: commodity, price, data, terms and conditions… 20

  21. Smart grid technology provides the critical information backbone for a sustainable electric system • Deliver peak load demand reduction • Increase reliability benefits • Squeeze more power through existing assets • Deliver efficiency in addition to peak demand savings • Measure and validate carbon offsets • Integrate renewables • Demand response support high wind penetration • Reward solar photovoltaic's for peak availability 21

  22. Load Duration Curve and Carbon Intensity of Marginal Generation 2.0 Carbon Intensity of Marginal Plant Peaking Combustion Turbine Intermediate Combined Cycle Baseload Coal Steam Load Duration Curve 1.5 CO2 (lb/kWh) 1.0 0.5 0.0 22

  23. Carbon “Supply Curve” Suggests Massive Efficiency Efforts will Come Early 23

  24. Potential Impacts of High Penetration of Plug-in Hybrid Vehicles (PHEVs) on the U.S. Power Grid* * PNNL study for DOE Office of Electricity The idle capacity of today’s U.S. grid could supply 73% of the energy needs of today’s cars, SUVs, pickup trucks, and vans… without adding generation or transmissionif vehicles are managed to charge off peak 73% electric (158 million vehicles) • Potential to displace 52% of net oil imports (6.7 MMbpd) • More sales + same infrastructure = downward pressure on rates • Reduces CO2 emissions by 27% • Emissions move from tailpipes to smokestacks (and base load plants) … cheaper to clean up • Introduces vast electricity storage potential for the grid 52% Source: EIA, Annual Energy Review 2005 24

  25. 1.20 1.00 Combined cycle Fossil steam Coal 0.80 Combustion Turbine Renewables normalized electric system load ` Hydro 0.60 Nuclear 0.40 0.20 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 hour of day Fundamental Approach 1: Determine Available Marginal Generation Dispatched generation to meet load (matches EIA 2002 annual totals) Regional peak day load profile (NERC 2002) Available generation (EIA 2002) • Assumptions: • No additional generation from existing: • Nuclear • Hydro • Renewables • Combustion Turbines (peaking plants) Regional avg. peak season load profile (NERC 2002) 25

  26. 1.20 1.00 Combined cycle Fossil steam Coal 0.80 Combustion Turbine Renewables normalized electric system load ` Hydro 0.60 Nuclear 0.40 0.20 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 hour of day Fundamental Approach 2:“Fill the Valley” in the Load Shape • Assumption: • Additional • valley-filling • generation • constrained to • lesser of: • Available marginal generation @ 85% capacity factor • Peak load 26

  27. 1.20 1.00 0.80 normalized electric system load ` 0.60 0.40 0.20 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 hour of day Coal, Nat. Gas Power Plants Fill the Valley nighttime valley-filling daytime valley-filling Summary • Determine size of valley in MWh • Floor: average day in the peak season • Ceiling: lesser of available marginal generation @ 85% or peak load • No marginal added generation in valley from: • Hydro • All other renewables • Nuclear • Peaking plants Combined cycle Fossil steam Coal 27

  28. 105% 57% Nighttime Charging Only (hrs 18 – 6) Daytime + Nighttime Charging (0 – 24 hrs) 135% 79% 80% MAPP 104% 45% 78% 61% 18% 46% 127% 10% NWP NPCC(US) 23% 73% MAIN ECAR 15% 52% 66% 31% SPP 100% CNV 39% AZN& RMP SERC 73% 86% 49% 57% 34% ERCOT Analysis by NERC Region* Summary • Midwest: support almost the entire LDV fleet • East: somewhat smaller potential • West: supports fewer vehicles % figures denote the percentage of LDV fleet supported by idle electric capacity 28

  29. Regional Emissions Impacts (Well-to-Wheel*) with Today’s Generation Mix Existing coal plants break even on greenhouse gases Nationally, greenhouse gases reduced 27% despite increased reliance on coal * Argonne National Laboratory’s GREET well-to-wheel model • Moving emissions from tailpipes to smokestacks: • solves an intractable problem for CO2 capture • improves cost effectiveness for other emissions Plant mix for valley fill Greenhouse gases Particulates SOx Urban: VOCs CO NOx Particulates SOx SOx from vehicles doubles: cap-and-trade will require investment in cleaner plants Urban air quality emissions greatly reduced: VOCs/CO/NOx > 90% SOx = 80% Particulates = 40% 29

  30. Information: The Virtual Electric Infrastructure FACT: In the next 20 years, the U.S. will spend $450B on electric infrastructure, just to meet load growth. CHOICE: Perpetuate a 20th Century solution OR Invest in a 21st Century system saving ratepayers $80B while increasing reliability and flexibility. : The choice is easy because… $ bits << $ iron Revealing Values + Communications + Advanced Controls ≡Electric infrastructure

  31. Energy Sciences and Technology Directorate Electricity Infrastructure Operations Initiative Electricity Infrastructure Operations Center • Fully capable grid control center for training and backup • Live data resources from actual grid operations nationwide • State-of-the-art grid operation & modeling tools (AREVA T&D partner, $3M in-kind software) • Supporting network hardware and computation capabilities • Access to advanced computers and high speed networks Unique platform to research, develop & test next generation tools and concepts for operating the energy infrastructure 31

  32. Energy Sciences and Technology Directorate Electricity Infrastructure Operations Initiative Electricity Infrastructure Operations Center An industry-native working environment: • understand and quantify current operations as a baseline point-of-departure • conduct R&D for new control and operation technologies • evaluate and quantify their benefits • transfer technology to … conduct advanced training for … and obtain feedback from … power system operators • understand human factors and develop visualization techniques that enhance situational awareness. 32

  33. High-Speed Grid Computing Research Once the cascade began, the 2003 blackout swept from Ohio to NY in nine seconds! Point-of-departure: Static State Estimation Data collection cycle Static States Actual conditions Data point Resolving state-estimates & computing contingency analysis takes 2-4 min Resolving state-estimates & computing contingency analysis takes 2-4 min Operators had no way to see imminent instability! Operators had no way to see imminent instability! Resolved state estimate Time 2-4 min 4 sec 8 sec 12 sec Electricity Infrastructure Operations Initiative Point-of-departure: Static State Estimation Curtain Once the cascade began, the 2003 blackout swept from Ohio to NY in nine seconds! Data collection cycle Static States Measured data ± error Presumably quasi-steady state Resolved state estimate Time 2-4 min 4 sec 8 sec 12 sec 33

  34. High-Speed Grid Computing Research (cont.) Resolved dynamic state estimates & parameters Data collection cycle Dynamic States Look-ahead provides time to react & stabilize grid Look-ahead simulation Calibrated dynamic model Time 4 sec 8 sec 12 sec 1 min Electricity Infrastructure Operations Initiative Goal: Dynamic Situational Awareness at Data Cycle Speed • Advanced scientific computing architectures & algorithms • Dynamic state estimation • Dynamic stability & contingency analysis: off-line → real-time • Engage DOE Office of Science More throughput and more reliability means $$$ Curtain Resolved dynamic state estimates & parameters Data collection cycle Dynamic States • Minutes → secondsmeans 102 speed-up • New phasor data cycle is 1/30 sec means104 Look-ahead provides time to react & stabilize grid Look-ahead simulation Calibrated dynamic model Time 4 sec 8 sec 12 sec 1 min 34

  35. Watershed Management/Hydro Operations Research Goal: Scientific basis for real-time, joint optimization of hydro & fish • Real-time assimilation of remotely sensed data • Multi-scale analysis • 7000 sub-basins down to 10 m • Ensemble climate & streamflow forecasting • Multi-objective optim-ization supported by high-speed computing • Synexus Global partnering with their existing BPA model • Real-time hydraulic modeling of fish conditions: static→dynamic constraints HUC 5 HUC 6 10 m Mixing at Confluence of Snake & Clearwater Rivers Electricity Infrastructure Operations Initiative Goal: Scientific basis for real-time, joint optimization of hydro & fish • Real-time assimilation of remotely sensed data • Multi-scale analysis • 7000 sub-basins down to 10 m • Ensemble climate & streamflow forecasting • Multi-objective optim-ization supported by high-speed computing • Synexus Global partnering with their existing BPA model • Real-time hydraulic modeling of fish conditions: static→dynamic constraints Mixing at Confluence of Snake & Clearwater Rivers 35

  36. Rob Pratt, Program Manager Pacific Northwest National Laboratory robert.pratt@pnl.gov 509-375-3648 U.S. Department of Energy Office of Electric Delivery and Energy Relibility http://www.oe.energy.gov/ Electric Distribution/GridWise http://www.electricdistribution.ctc.com/ GridWise Architecture Council http://www.gridwiseac.org/ GridWise at PNNL http://gridwise.pnl.gov/

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