simplified flexibility parameters for evaluating renewable ... - 12 denholm (nrel).pdf · • 576...
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NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Simplified flexibility parameters for evaluating renewable integration
JRC Workshop on Addressing Flexibility in Energy Models
Paul Denholm
December 5, 2014
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2
Approaches to Capacity Expansion Planning
• Traditional load-duration curve approaches
o Screening curves identify least cost mixes based on levelized cost of energy
o Doesn’t incorporate any chronological (time-series) analysis
• Linear and Mixed-Integer optimization
o Finds lowest cost mix based on a life-cycle cost
o Can incorporate chronology in the objective function
o But full year hourly (or sub-hourly) simulations are computationally complex
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3
Approaches to Incorporating Time Series in Capacity Expansion Models
• Reduced set of time-periods o Full time-series for a few weeks that (hopefully)
represent the entire year
o Variable generation makes picking “typical” periods challenging
• Time-slice (non chronological) approach o Estimates typical dispatch characteristics in a set of
representative time periods
o Requires establishing parametric relationships for key parameters such as curtailment
o NREL approach in the ReEDS models
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4
NREL’s Grid Modeling Tools
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5
ReEDS Model
A spatially and temporally resolved model of capacity expansion in the U.S. electric sector.
Designed to explore potential electric-sector growth scenarios in the U.S. out to 2050 under different economic, technology, and policy assumptions.
(Regional Energy Deployment System Model)
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6
ReEDS Model: History and Team
Current team is ~10 staff (not all full-time on ReEDS).
Selected studies:
• 2008 20% Wind Vision
• 2012 Renewable Electricity Futures
• 2012 SunShot
• Various RPS, CES, PTC, … analyses
ReEDS has been in use for >10 years, with a steady increase in sophistication and capabilities over that time.
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7
What does ReEDS do?
• Each 2-year solve produces a set of new investments and described operation of new and existing fleet.
• Between solves, ReEDS updates: o Existing generator fleet, including retirements
o Existing transmission
o Performance of existing fleet
o Costs/performance of new technologies
o Electricity demand, reserve margin requirements
o Variable renewable capacity values, curtailment, operating reserve requirements
• Skip forward two years, and solve again.
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Reduced-form Dispatch
Seventeen time-slices: four seasons x four diurnal + one superpeak. Continuous units: minimum turndown, but no startup or shutdown, flat heatrate. Constraints guarantee adequacy requirements and ancillary services.
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9
Modeling Framework
Technology cost & performance Resource availability Demand projection Demand-side technologies Grid operations Transmission costs
Black & Veatch
Technology Teams
Flexible Resources
End-Use Electricity
System Operations
Transmission
ABB inc. GridView
(hourly production cost)
rooftop PV penetration
2050 mix of generators
does it balance hourly?
Implications GHG Emissions
Water Use Land Use
Direct Costs
Capacity & Generation 2010-2050
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10
Integration with Operational Model
• To supplement ReEDS’ reduced-form unit-commitment model, for the REF analysis we rebuilt ReEDS infrastructure in GridView, a commercial production cost model to test how the ReEDS-projected infrastructure might behave in an hourly dispatch.
• We are now automating the capability, using PLEXOS this time instead of GridView.
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11
UC on NREL’s HPC
Peregrine Characteristics: • 11520 Intel Xeon E5-2670 "SandyBridge" cores • 14400 next-generation Intel Xeon "Ivy Bridge"
core • 576 Intel Phi Intel Many Integrated Core (MIC)
core co-processors with 60+ cores each • 32 GB DDR3 1600Mhz memory per node • Peregrine will deliver a peak performance of 1
petaFLOPS
NR
EL P
IX 2
45
80
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12
Can we include more chronology in the objective function?
• Fundamental tradeoff between chronology and simplicity. o But can we incorporate full chronological simulations
but avoid many of the key complications?
• Unit commitment • Full storage optimization
• Can reduced form chronological simulations still
provide valuable insights? • And is 1-hour simulation good enough?
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13
Renewable Energy Flexibility (REFlex) Model
• Dispatch only model
• Block dispatch by generator type
• Simplify key parameters traditionally captured in unit commitment
o Minimum generation point for thermal generation
o Minimum thermal generation for ramp
• Simplified valley-filling algorithm for storage and DR dispatch
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14
What can this approach do?
• Analyze optimal mixes of VG in high penetration scenario
• Examine curtailment • Analyze impact of storage and DR • Run very fast
• What it can’t (probably) do:
o Optimize new conventional generation mix in low VG scenarios
o Basically assumes thermal fleet is relatively static or decaling
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Example – Curtailment Analysis
• At high penetration, economic limits will be due to curtailment
o Limited coincidence of VG supply and normal demand
o Minimum load constraints on thermal generators
o Thermal generators kept online for operating reserves
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16 16
Minimum Generation Levels Limited by Baseload Capacity
Price/Load Relationship in PJM
Below Cost Bids
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17 17
Min den depends on VG Mix
17
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Example – Curtailment as a Function of Penetration
0%
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80/20
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Reflex < 1 Minutes per run
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19
Results from Full UC/ED Model
PLEXOS Simulations (DAUC/SCED) ~5 Hours per run
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Example – Storage Dispatch
• Full storage optimization is computationally complex
• Valley filling (search) algorithms can be much faster
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21 National Renewable Energy Laboratory Innovation for Our Energy Future
REFlex CSP Dispatch
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Curtailed Solar
Dispatched CSP
Usable PV
Wind
Conventionals
Load
Non-Dispatched CSP
Dispatched CSP
Dispatch of CSP May 10-13
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22 National Renewable Energy Laboratory Innovation for Our Energy Future
PLEXOS CSP Dispatch
Dispatch of CSP in WWSIS-2 Study (July)
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Example –Energy Storage
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Fraction of System Electricity from Wind&Solar
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dNo Storage
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Example – Electric Vehicle Charging
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kW)
Time (In 5-Minute Intervals)
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SUV-20
Sedan-40
Sedan-20
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Vehicle Availability
High PV Impacts
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Example – Electric Vehicle Charging
Overnight optimized, uncontrolled daytime charging
Optimized overnight, imperfect foresight daytime
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What else can we ignore?
• Subhourly Dispatch?
• Incorporation of cycling costs into UC/ED process?
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27
Example: Western Wind and Solar Integration Study
•Phase 3: Frequency Response and Grid Impact
Phase 2: Cycling Cost and Emissions Impacts
What happens to the transmission grid’s frequency with high penetration of distributed PV at low load?
What happens to the grid when remote transmission lines are highly-loaded to move wind long distances?
From a system perspective, cycling costs are relatively small
Emissions impacts of cycling are relatively small
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5- minute dispatch
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0:00 0:20 0:40 1:00 1:20 1:40 2:00
1-Hour
5-Minute
UC based on this 1-hour ramp rate
This 5-minute ramp may exceed committed capability
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Methodology
• PLEXOS unit commitment and dispatch modeling
o Day ahead market (hourly)
– Coal and nuclear units committed
o 4 hour ahead market (hourly)
– Better forecasts
– Gas CC and steam units committed
o Real time market (tested hourly vs subhourly)
– Gas CT committed and dispatched
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WWSIS Core Scenarios
Wind Capacity (MW)
710 to 1,650
140 to 710
110 to 140
70 to 110
10 to 70
PV Cpacity (MW)
76 to 200
51 to 76
29 to 51
10 to 29
0 to 10
CSP Capacity (MW)
199 to 200
142 to 199
105 to 142
84 to 105
64 to 84
Reference 8% wind 3% solar
High Mix 16.5% wind 16.5% solar
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Consistency between cases
• Constant
o Commitment of non-CT generators
o Planned hourly hydro generation
o Reserve requirements
• Changes
o Interval of real-time dispatch (5-min and hourly tested)
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2-part heat rate curves
Another area of sensitivity analysis needed….
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Difference between hourly and 5-min net load
July 25-28
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Difference between hourly and 5-min net load
5 Minute net load and interpolated hourly net load (load – mind – PV)
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Difference between hourly and 5-min net load
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Run Times
• Day Ahead UC ~ 3 days
• 4-Hour Ahead UC ~1 Day
• 5-Minute Dispatch ~ 2 Days
o Approximately 12 times longer than 1-hour dispatch
o What do we get for this increase in run time?
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Results
• No unserved load
• Some change in unserved reserves
• Very little change in total production cost
• Occasionally significant change in LMPS
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Unserved load and reserves
• No unserved load in any scenario
• Reserve requirement totals ~40 TW-h
• Unserved reserves
HiMix Reference
RT – hourly resolution 138 MW-h 178 MW-h
RT – 5-minute resolution 263 MW-h
337 MW-h (0.0008%)
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5-minute resolution dispatch stack
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Hourly resolution dispatch stack
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Total production costs
HiMix Reference
RT – hourly resolution $11.03 billion $15.12 billion
RT – 5-minute resolution $11.02 billion
$15.13 billion
Changes in production cost between hourly and 5-min runs are within the range of uncertainty
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Generation by type
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Number of starts
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Curtailment
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Price differences
Demand response deployments
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My Opinions
• Full unit commitment is desirable, but unless we get a 100x plus increase in speed we need to simplify the problem
• Capacity expansion problems don’t lend themselves to traditional parallelization
• Simplification of UC with simplified parameters appears to produce reasonable representation of thermal fleet under high VG scenarios
• Simple time-shifting storage optimization appears to produce reasonable results
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Concluding Thoughts
• Does importance of accurate unit commitment simulation decrease in high renewable scenarios?
o Retirement of long-start units leads to UC being a hour-ahead problem vs. a day-ahead problem
• Increased importance of chronological simulation for demand response, energy storage
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Flexibility Metrics?
Input Metrics • Ramp Rate • Ramp Rate • Transition Time
• Start-up • Min Up/Down
Time
Output Metrics • Loss of Load
Expectation • Reserve Violations • Unmet ramp
requirement
Outcome Metrics • System
Costs/Benefits • Carrying Capacity