1. Impacts of Flexible Ramp Capability Products in MISO Adam Cornelius, Rubenka Bandyopadhyay, Dalia Patino-Echeverri Nicholas School of the Environment – Duke University USAEE, Pittsburgh October 26 2015 1

2. Net Load and Ramp requirements 2 BackgroundProblemMethodResultsDiscussion

3. MISO Unit Commitment/Economic Dispatch Model BackgroundProblemMethodResultsDiscussion Generator Costs Energy Cost Spinning Reserve Cost Startup Cost No Load Cost Day Ahead Unit Commitment Int Length: 1 hr # Intervals: 24 Real Time Economic Dispatch Int Length: 5 Mins # Intervals: 1 Repeat Every 5 Minutes Day Ahead System Reqs Forecasted Net Load Reserve Requirements Regulation Requirements Day Ahead Output/ Real Time Input Commitment (on/off) Schedule Real-Time Output Generator Dispatch Market Clearing Price Spin Reserve Dispatch and Price Generator Parameters Max Ramp Rates Min/Max Generation Min Uptime Min Downtime Real-Time System Requirements Actual Net Load Reserve Requirements Day Ahead Market Real Time Market Models Final Outputs Intermediate Outputs Inputs Key

4. Solution of Day Ahead Unit Commitment (DAUC) does not guarantee meeting intra-hour ramping needs DAUC sets dispatch level for whole hour System will procure sufficient ramp capability to get to next hourly interval, but may not be able to meet intra-hour fluctuations 4 BackgroundProblemMethodResultsDiscussion

5. Example of ramping problem Simple 2-Generator Example  G1: Cheap slow-ramping plant  G2: Expensive flexible plant Economic Dispatch, 5 Minute Interval 5 Cost ($/MWh) Ramp Rate (MW/min) Max Gen (MW) Min Gen (MW) G110110020 G215510020 Wind0--- 5-minute Interval1 Load (MW)140 Wind Available (MW)20 Net load (MW)120 G1 Generation (MW)100 G2 Generation (MW)20 Wind Curtailed (MW)0 Interval Cost ($)108.33 2 120 30 90 95 0 -5 79.17 12 140120 2030 12090 Total Cost ($)187.50 Dispatch for Single Interval With look-ahead and perfect foresight: 9590 250 00 110.4275 185.42 BackgroundProblemMethodResultsDiscussion

6. Currently, to respond to ramping shortages, MISO operators: 6 Check ramping capacity in the DA commitment analysis Manually dispatch out-of-merit-order generators  Give a “make-whole” or “uplift” payment to generators whose costs are higher than DA clearing price (RSG payment) Curtail wind as needed This approach hides the problem and does nothing to prevent it: Uplift payments are different for each generator and not transparent Do not create incentives to invest in fast-ramping resources Undesirable scarcity events, price spikes and wind curtailment BackgroundProblemMethodResultsDiscussion

7. Possible ways to deal with ramping shortages 7 Increase the requirements for other ancillary services  Commit more resources to provide frequency regulation Use regulation resources to ramp up and ramp down as needed  Increase spinning reserve requirements Use them to ramp up Use a time-coupled multi-interval dispatch model Modify Day Ahead UC-ED and Real Time ED to:  explicitly ensure ramp capability is provisioned  to estimate the opportunity cost of ramp capability and compensate generators accordingly (Navid and Rosenwald, 2012,2013) BackgroundProblemMethodResultsDiscussion But increases costs because resources are paid twice ! Will provide all the ramp needed with perfect forecast, but 1.Does not account for uncertainty 2.does not separate energy prices from ramp prices Miso’s proposal

8. One possible ways to deal with ramping shortages 8 Modify Day Ahead UC-ED and Real Time ED to:  explicitly ensure ramp capability is provisioned  to estimate the opportunity cost of ramp capability and compensate generators accordingly (Navid and Rosenwald, 2012,2013) BackgroundProblemMethodResultsDiscussion Miso’s proposal approved by FERC and schedule for implementation in 2016

9. MISO’s Flexi-ramp capability “products” proposal 9 Modify UC-ED algorithms to simultaneously co-optimize energy, ancillary services and ramp-capability Product 1: Up-Ramp Capability (URC) Product 2: Down-Ramp Capability (DRC) Features of these “products”:  Generators do not bid to provide ramp capability  Generators cannot opt out from providing ramp capability  By submitting an energy offer generators are offering to provide any combination of energy and demand capability  Generators are paid their opportunity cost if their dispatch changes Shadow price of the new ramp capability constraints BackgroundProblemMethodResultsDiscussion

10. Research questions: 10 Is flexi-ramp a solution?  Does it result in less scarcity-pricing events, higher reliability, lower emissions, lower wind curtailment?  How can the ramping targets (demand curves) be better determined  What is the interaction between reserves level, price cap for flexi- ramp and targeted amount How does flexi-ramp compare to other potential solutions like:  Time-coupled multi-interval dispatch with ramp reserve  Stochastic optimization Do flexi-ramp compensations improve profitability of flexible plants such as  CCS with amine storage  ISCC BackgroundProblemMethodResultsDiscussion Some results presented today Not included in today’s presentation

11. Previous studies on the effects of ramping products 11 Navid & Rosenwald, 2012 Market solutions for managing ramp flexibility with high penetration of renewable resource. IEEE Transactions on Sustainable Energy 3(4):p. 784-790 Navid & Rosenwald, 2013 Ramp capability design for MISO markets, white paper Xu & Tretheway, 2012 Flexible ramping products: second revised draft final proposal, white paper Wang & Hobbs, 2014 A flexible ramping product: can it help real-time dispatch markets approach the stochastic dispatch ideal? Electric power systems research, 109: p.128-140 Basic formulation and several small scale examples with 5 or fewer generators More detailed formulation other 5-generator examples 4 sample days all MISO CAISO’s model, a method to derive stepped demand curve and several small examples Compare deterministic model with flexi-ramp with stochastic

12. Simulate operations of a scale-system of MISO  UC and ED, hourly for Day Ahead,  10 minutes intervals for Real Time  With and without flexi-ramp Changing ramping capability targets by time of day, week/weekend, and season  With low, mid, and high levels of wind penetration  For a month in summer, fall-spring, winter 12 Method: BackgroundProblemMethodResultsDiscussion

13. UC Inputs Startup Cost Shutdown Cost No Load Cost Min Uptime Min Downtime Real Time Economic Dispatch Int Length: 10 Mins # Intervals: 1 ED Output/Input: Generation Schedule 30x/24x/6x Day Ahead Inputs Demand Forecast Wind Forecast Uncertainty RTED Inputs Actual Load Actual Wind Next Interval Forecast Uncertainty DA Output/ RT or ST Input Commitment Schedule DA Output Energy MCP & Schedule Spin Res MCP & Schedule Ramp Capability MCP & Schedule Wind Schedule/curtailment Price Spikes/Valleys RT Output Energy MCP & Dispatch Spin Res MCP & Dispatch Ramp Capability MCP & Dispatch Wind Generation/Curtailment Price Spikes/Valleys CO2 Emissions Settlement, including Make-whole Payments Day Ahead Market 13 Real Time Market Inputs to All Models Energy Cost Spinning Reserve Cost Generator Ramp Rates Reserve Requirements Value of URC/DRC Day Ahead Unit Commitment Int Length: 1 hr # Intervals: 24 Day Ahead Economic Dispatch Int Length: 1 hr # Intervals: 24 DAUC Output/ DAED Input Commitment Schedule DAUC Output/Input Ending Uptime, Downtime, Commitment 30x ED Output/ Input: Generation Schedule 30x Ramp Capability Model (for one month)

14. Day Ahead Unit Commitment (baseline) Planning Period: 24 hours Interval: 1 hour Minimize: Energy Costs + Spinning Reserve Costs + Startup Costs + Fixed Costs + OverGenerationPenalty + UnderGeneration Penalty + Scarcity of Reserves Penalty Subject to: Energy Generated = Forecasted Net Load Reserves Available >= Reserves Required Generator constraints Ramp rates Min up/down time Min/Max Generation Output: Planned Hourly Generator Schedules for:  Commitment (on/off)  Energy Produced  Spinning Reserves Provided 14 BackgroundProblemMethodResultsDiscussion

15. Day Ahead Unit Commitment (with ramp) Planning Period: 24 hours Interval: 1 hour Minimize: Energy Costs + Spinning Reserve Costs + Startup Costs + Fixed Costs + OverGenerationPenalty + UnderGeneration Penalty + Scarcity of Reserves Penalty – Benefits of Procured URC – Benefits of Procured DRC Subject to: Energy Generated = Forecasted Net Load Reserves Available >= Reserves Required System URC Procured <= URC Target System DRC Procured <= DRC Target Sum of Generator URC >= System URC Procured Sum of Generator DRC >= System DRC Procured Generator constraints Ramp rates (Accounting for URC and DRC) Min up/down time Min/Max Generation (Including URC and DRC) Output: Planned Hourly Generator Schedules for:  Commitment (on/off)  Energy Produced  Spinning Reserves Provide 15 BackgroundProblemMethodResultsDiscussion The corresponding DAED-R outputs the DA amount procured of URC and DRC

16. Real-Time Economic Dispatch (with flexiramp prods) Planning Period: 10 Minutes Interval: 10 Minutes Minimize: Generation Costs + No load costs + Spinning Reserve Costs + OverGenerationPenalty + UnderGenerationPenalty +SpinningReservesScarcityPenalty – Benefits of Procured URC – Benefits of Procured DRC Subject to: Energy Generated = Actual Net Load Reserves Available >= Reserves Required System URC Procured <= URC Target System DRC Procured <= DRC Target Sum of Generator URC >= System URC Procured Sum of Generator DRC >= System DRC Procured Generator Parameters not violated Ramp rates (including URC/DRC) Min up/down time Min/Max Generation (including URC/DRC) Output: Generator Dispatch levels for:  Energy Produced  Spinning Reserves Available  URC/DRC Market Clearing Price for  Energy  Spinning Reserves  URC/DRC 16 BackgroundProblemMethodResultsDiscussion Targets are determined based on expected ramp and historical variability of ramp Benefits or URC and DRC are lower than over or under generation penalty, or scarcity of reserves penalty, so RC is third in priority Shadow prices of URC and DRC constraints are the payments to generators re- dispatched and reduce the need for uplift costs

17. Consider expected ramp:  Dispatching for 3:10 pm  Consider net load forecast for 3:20 17 How much ramp capability to procure? BackgroundProblemMethodResultsDiscussion

18. consider uncertainty surrounding forecast in both directions 18 BackgroundProblemMethodResultsDiscussion How much ramp capability to procure? 2.5 standard deviations from historical analysis

19. How much ramp capability to procure? Procure enough Up-Ramp and Down Ramp Capability to meet uncertainty levels Quantity will vary by season & time of day 19 BackgroundProblemMethodResultsDiscussion

20. Test System ~6% Scaled version of 2009 MISO System 20 MISO Territory Wind Locations BackgroundProblemMethodResultsDiscussion 44 coal and NG plants, representative of MISO (chosen with clustering analysis by ramp rate and capacity,eGrid)

21. Data: 21 BackgroundProblemMethodResultsDiscussion Coal and Gas Generators: Name Plate Capacity Average Heat Rate Emissions Min Generation  Northwest Power and Conservation Council Min Up/Down Time  SPP Pancaking study / CRA intl Start-up Costs  NREL Power Plant Cycling Costs No Load costs  LBNL Ramp-rates  EIA Fuel costs  from average heat rates and EIA fuel prices Spinning reserve costs  20% of energy marginal costs eGrid 2012

22. Data (2): 22 BackgroundProblemMethodResultsDiscussion Wind Generators: Real time 10 minute power generation  EWITS Day ahead hourly forecast  EWITS 10-minute forecast  Generated assuming 4% error Electrical Load: Real time 10-min load data  MISO – LCG consulting Day ahead forecast  Generated assuming 1% forecast error 10 minute forecast  Generated assuming 0.2% forecast error System Parameters: Overgeneration penalty  $500/MWh Undergeneraton penalty  VOLL = $3500/MWh Spinning Reserve Scarcity Price  $1100/MWh URC and DRC benefits  $10/MW

23. Load data 23 BackgroundProblemMethodResultsDiscussion Low Wind High Wind

24. Observed historically variability of ramp in net load for high wind 24 Up variability is higher in the morning for all seasons and days of week (except winter weekends) Down variability is higher at night for all seasons and days of week

25. Ramp Capability procurement in day- ahead economic dispatch 25 BackgroundProblemMethodResultsDiscussion

26. Ramp Capability procurement in real- time economic dispatch 26 BackgroundProblemMethodResultsDiscussion

27. Reliability: Shortages of energy and reserves 27 BackgroundProblemMethodResultsDiscussion

28. Prices: During non-shortage intervals and overall 28 BackgroundProblemMethodResultsDiscussion RC products result in lower overall real time energy MCP But higher MCP’s under normal conditions

29. Costs: Uplift and Total 29 BackgroundProblemMethodResultsDiscussion RC products result in lower overall real time energy MCP But higher MCP’s under normal conditions

30. Environmental Perf: Wind curtailment 30 BackgroundProblemMethodResultsDiscussion

31. Environmental: Mixed results about CO 2 reductions 31 BackgroundProblemMethodResultsDiscussion

32. Coal Generation, CO 2 Emissions 32 BackgroundProblemMethodResultsDiscussion ramp capability provides reliability benefits  Fewer shortages  Especially with high wind ramp capability reduces MCP  lower average prices  Fewer price spikes  Slightly higher prices under normal (non-shortage) conditions Ramp capability reduces wind curtailment More research needed to see what happens to CO 2 emissions  Depends on specific star-up and operating emissions of CT, NGCC and Coal plants  Working on it!

33. Thanks to my students Co-authors:  Adam Cornelius (MEM – 2014), Rubenka Bandyopadhyay (PhD student) Others who helped:  Ali Daraeepour (PhD student)  Kyra Holt (PhD student)  Eric Chen (undergrad)  Terry Conlon (undergrad) 33 Thank you! dalia.patino@duke.edu

34. Demand “curve” Ensures the system does not procure ramp capability at the expense of energy or operating reserves in current interval  Demand curve (set by ISO) acts as upper-bound on price system will pay for ramp capability  Ramp capability target (forecast + uncertainty) acts as upper bound on the quantity system will pay for There are four possible situations: 34 BackgroundProblemMethodResultsDiscussion