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Page 1: Assessing Opportunities to Exploit Stranded Power - Andrew Chien

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Assessing Opportunities to Exploit Stranded Power Andrew A. Chien *University of Chicago and Argonne National Laboratory w/ Fan Yang, Rich Wolski (UCSB), Victor Zavala, Kibaek Kim (Argonne) UCSB Institute for Energy Efficiency October 29, 2015

Outline • What is Stranded Power? • Understanding Stranded Power • Exploiting Stranded power • Looking Forward (Grid, RPS)

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Scopes of Energy Efficiency

• Rack, Data center • Power Distribution, Power Grid • Generators

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Talk Focus

Stranded Power

•  Grid economic management mechanisms can cause renewable power to be “stranded” •  Ramps, Transmission Congestion or Shortage, Correlated Production

•  MISO “down dispatched” 2.2TWH of wind in 2014 •  Equivalent to 251 MW average; ~2%, MISO: Success!

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UNDERSTANDING STRANDED POWER

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Mid-continent Independent System Operator (MISO)

•  15 states, serves 42M people

•  130 GW peak load, >500TWh/year

•  $37B Power (2014) •  413 Market participants •  >2,000 pricing nodes

•  >65,000 miles transmission •  $2.2B transmission

charges

•  ~10% Wind generated

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Transformation of the Power Industry and Power Grid

Power Grid in 1975

• Vertically integrated • Few, Large fixed

generation sites •  Large loads,

geographically fixed, 2x dynamic cyclic

Power Grid in 2015

• Separation of generation and distribution

• Explosion in large-scale distributed renewables

• Highly variable Generators

•  Loads same

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Central Control Economic Dispatch,

“open” market, Localized Pricing

Renewable Variation and Economic Dispatch in MISO

• Technical: Generators bid “Eco_max,min”, and price curves hourly

• Economic: Global optimization, focused on “Merit order”, Day-ahead Market, Real-time Market

• Control: 5-minute dispatch (with 4-second adjustments) • Payments determined by actual deliveries

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Capability Price info •  Feasible

•  Cheapest •  Green •  Fair •  Fast (5-minute increments)

•  + Reliable •  +“As much as you want”

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Locational Marginal Pricing and Local Outcomes

•  Market prices based on the “incremental” cost of each MWh •  Variation+Transmission+Economics: Complex, coupled behavior •  Produces wide variation at the edge (generators) •  LMP => the market price for the generator

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Generation Offered

Transmission Constraints

MISO Market Schedule & Prices

• Feast and famine • Variable Generation • Prices from $100 to -$50 [Range is wider now]

• Generator’s Goal: a good “Average” Price

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Wind Power Pricing Minnesota Hub

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<add python timeline plot>

Hours

Example Site - Timeline

• Price vs. Time [2015: Jan-April]

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• Price vs. Time, Unit 8238, January 2015

Example Site – Timeline (zoom)

<add python timeline plot>

Hours

$$/M

Wh

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• EcoMax (potential) vs Delivered [2015: Jan-April]

Example Site – Stranded Power

Stranded Power

• Precise model: Instantaneous •  Power with no economic value, for a 5-minute interval

• Define: LMP0 as LMP < $0 / MWh

• Define LMP(x) as LMP < $x / MWh

•  LMP0, LMP1, LMP2, ... LMP5 •  $32/MWh is average for MISO

•  So, LMP5 is power 6x lower than the average price

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Analysis of a Power Grid Market • MISO Market Records Properties

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Period Jan 1, 2013-Apr 14, 2015

28.5 Months

Generation Sites 1,259 Total 200 Wind

5-min Intervals 76.9M Total 36.6M Wind

Power 1,188 TWh Total 89 TWh Wind

$$ Revenue $95 Billion $4 Billion

Wind Sites, SP Duty Factor: LMP0

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LMP Stranded Power vs Price Threshold ($/MWh)

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Interval Sizes, LMP

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Best Single Site

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Stranded Power, V2 • A 2nd “stranded power” model

• NetPrice (average over a longer period) •  Offer a good deal to the generator for the entire period

•  Idea: smooth out the fluctuations

• NetPrice(C): NetPrice < C (C = $0,$1,...,$5)

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Wind Sites, SP Duty Factor: NetPrice0

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NetPrice Stranded Power vs Price Threshold ($/MWh)

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NetPrice Stranded Power: Intervals (by Count), NetPrice5

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Best Single Site

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NetPrice Stranded Power: Intervals (by time), NetPrice5

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Best Single Site

Lots of Stranded Power

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Dispatchable Loads: Approach •  Idea: Create on-demand computational loads that can

exploit the stranded power •  Reduce grid waste and increases stability •  Provides greater financial return for renewable generators •  A means to achieve higher RPS and lower grid costs

• Two types •  Spot Instances, Preemptible instances, Peer to Peer, Desktop Grid •  High-Performance Computing

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DISPATCHABLE COMPUTING LOADS: CLOUD SERVICES�

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Zero-carbon Cloud (ZCCloud)

• Build distributed computing servers that act as a “dispatchable load” •  Start on demand when there is stranded power •  Consume the power, continuously while available •  Shutdown gracefully when the power is no longer available

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Amazon’s Spot Instance Model

• Execute a “virtual machine” or “container” at a time when the computing is below a bid price

• Start time + End time: dictated by the system • Queue of “persistent” and “new” spot requests

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(Courtesy Amazon)

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DISPATCHABLE COMPUTING LOADS: HIGH-PERFORMANCE COMPUTING�

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Batch High-Performance Computing Model

•  Execute a parallel job (array of machines or VM’s), 1-16,384 •  Start time + End time: dictated by the system (jobs request runtime) •  Queue of “new” parallel job requests

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Large Parallel jobs

Batch Scheduler Parallel

Computing Resources

Results

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Traditional Data Center System Limits

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Augmenting Mira with ZCCloud

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10 Petaflops, 4MW, 786K Cores 2nd System of the Same Size 10 Petaflops, 4MW, 786K Cores

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ALCF Workload Traces • Mira: #5 Supercomputer in the World •  IBM Blue Gene/Q System, 768K Cores, 10 Petaflops

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Period Dec 31, 2013 – Dec 30, 2014

# of Jobs 78,795

Runtime (Hours) Range: 0.004 to 82 Average: 1.7, StDev: 3.0

# Nodes Range: 1 – 48K Average: 1,975, StDev: 4,100

Basic Performance, by Workload Shape

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•  Understanding Intermittent Resources •  Adding resources translates into Improved Performance •  Even better than basic capacity increase (1.5x) •  Shape doesn’t matter

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Performance, by Job Size

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•  Understanding Intermittent Resources •  Performance improvement Overall, but varies with size •  Benefit Weighted heavily to “large, capability jobs”

•  Insights for system mgmt?

Performance, by “Ontime” or “Late”

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•  Ontime jobs can complete in the current “volatile resource” available window

•  Late jobs cannot (arrived too late) •  Ontime jobs benefit more! But Late jobs benefit as

well...

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Performance, by Stranded Power Model

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•  LMP models lower duty factor produces less benefit •  Some loads are infeasible •  Netprice high duty factors provide growing benefit

Performance, Equal Duty Factor

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•  LMP models provide lower duty factor •  LMP models perform worse than periodic model with

equal duty •  NetPrice models can provide higher duty factors •  Can provide better performance

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Related Work •  Integration of Renewables into the Power Grid

•  Wind Vision, CAISO, Grid expansion, Power Markets

• Energy-efficiency in Data Centers •  Power Distribution, Dynamic Management, Energy Proportional

Computing, “Drowsy” management

• Solar Augment and LT-Purchase for Wind, Colocation •  A good thing “generally”, support the finances of renewable power •  Create covariation with renewables in the grid: counter-variation of

load (bad)

• Follow the Sun •  Closest, create co-varying load (good)

• ZCCloud: New “volatile” computing resource model

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Summary • Stranded power is a growing challenge for renewables

• Computational Dispatchable loads are promising • Benefits grids and renewable generators

• Cloud Services Evaluation •  Initial Economic Evaluation

• High Performance Computing Evaluation •  Intermittent resources can be gracefully integrated • Stranded Power models are important

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Looking Forward • What other uses of stranded power are feasible?

•  Lightweight manufacturing •  Opportunistic tasks: Carbon removal? Reduction?

• What opportunities are there to create higher value computing services from intermittent energy? •  Combining sites •  Intelligent migration

• What benefits can “dispatchable loads” accrue to the grid? •  Increased stability, reduced operating margin •  Increased ability to exploit renewable power •  Partial Load shifting

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BENEFITS OF DISPATCHABLE LOADS FOR GRID

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