UIUC Technical Presentation

1. UIUC Technical Presentation MISO Overview and Generation and Demand Management Improvement with Increased Variable Generation Li Zhang November 12, 2012

2. Outline of the Presentation • MISO Overview and its Market Structure • Efforts associated with the Increased Variable Generation • Day ahead Ramp Capability • Dispatchable Intermittent Resources • Stochastic Unit Commitment • Robust Unit Commitment • Demand Response Program • Pricing Improvement (Extended LMP, Scarcity Pricing Design)

3. MISO Overview

4. MISO Evolution • Franz Edelman Award for Operations Research April 2011

5. Adding Value Major Achievements • Entergy IntegrationExpected by end of 2013 • Market Enhancements • Extended LMP (implemented April 2014) • Dispatchable Intermittent Resources (implemented June 2011) • Resource Adequacy Construct (approved June 2011) • Franz Edelman Award for Operations ResearchApril 2011

6. MISO Overview MISO Reliability Coordination Area, January 2012 Independent, non-profit organization responsible for maintaining reliable transmission of power in 11 states and one Canadian province 2001 - Reliability Coordinator 2005 - Energy Markets 2009 – Ancillary Services First Regional Transmission Organization (RTO) approved by the Federal Energy Regulatory Commission (FERC)

7. Scope of Operationsas of June 1, 2012 • 5-minute dispatch • 1,936 pricing nodes • 1,258 generating units (market) • 6,060 generating units (network model) • $23.6 billion gross market charges (2011) • 356 market participants serving 38.9 million people • Generation Capacity • 132, 313 MW (market) • 144,599 MW (reliability) • Historic Peak Load(July 23, 2012) • 98,576 MW (market) • 104,669 MW (reliability) • 49,670 miles of transmission • 11 states, 1 Canadian province

8. MISO Role • Reliability Coordination • Wholesale Energy and Ancillary Service Market • Long Term Transmission Planning

9. MISO Market Overview FTR Day Ahead Real Time MISO’s Energy and Operating Reserves market consists of three components: • Day-Ahead Energy Market • Real-Time Energy Market • Includes regulation and spinning reserve products • Financial Transmission Rights Market (FTR)

10. Generators Offers Day-Ahead Schedules Bids MISO Day-Ahead LMPs 1100hr 1600hr Load Serving Entities Day-Ahead Market

11. Real Time Market Real Time Market • A continuous process of balancing generation and demand at least cost while recognizing current operating conditions • Manage congestion via Locational Marginal Pricing and Generation Redispatch

12. Energy Price (MEC) LMP Congestion (MCC) = Losses (MLC) Locational Marginal PricingLMP Components • Conceptually… • LMP = MEC + MCC + MLC

13. Financial Transmission Rights Provides a mechanism for Market Participantsto manage the risk of congestion FTRs apply to the Day-Ahead Market only Financial Mechanism ONLY(not tied to physical delivery) FTRs hedge against congestion only - not losses

14. The Energy Balance

15. Ancillary Services • Integrated into energy market operations Jan. 6, 2009 • Flexible capacity needed to maintain secure operation of power system • Loss or increase of load • Loss or increase of resources • Regulation Reserves • Contingency Reserves(sometimes called Operating Reserves) • Spinning • Supplemental (non-spinning)

16. Operating Reserves Supplemental Reserve (Schedule 6) Contingency Reserve Spinning Reserve (Schedule 5) Operating Reserves Markets Regulating Reserve (Schedule 3) Generation and Load Energy Markets

17. What is a Balancing Authority? • An electric power system or combination of electric power systems bounded by interconnection metering and telemetering • Balancing Authority duties • Balance Supply and Demand within their area • Maintain interchange of power with other Balancing Authorities • Maintain frequency of the electric power systemwithin reasonable limits

18. Congestion Management • Ensure transmissionsystem does not overload • Managed in real time • 5-minutegeneration dispatch

19. What is a Contingency? Transmission line tripping, generator tripping,loss of load or some combination of these events This contingency in turn causes other problems, such as a transmission line overload, an over or under voltage in an area, over or under frequency or frequency instability Contingency Reserves are a specified percentage of generation capacity resources held back or reserved to meet emergency needs

20. Day-AheadEnergy and Operating Reserve Market • Products cleared and priced hourly • SCUC Algorithm used to commit resources, select resources for regulation and select emergency resources. • SCED Algorithm used to clear and price energy and operating reserve.

21. Real-TimeEnergy and Operating Reserve Market • Products cleared and priced every five-minutes • SCUC Algorithm used to commit resources, schedule resources for regulation and select emergency resources. • SCED Algorithm used to clear and price energy every five-minutes

22. Real-Time SCED Algorithm • Uses an LP solver - Simplex Method • Objective Function: Minimize energy and operating reserve costs over a five-minute dispatch interval based on submitted offers and subject to constraints • Single offer price for operating reserve • Offer curve for energy • Constraints: • Resource Status - Hard • Resource Limit and Ramping - Penalized • Global Power Balance - Penalized • Selected transmission - Penalized • Market-wide Reserve Requirement - Demand Curve • Reserve zone Reserve Requirement - Demand Curve

23. Day-Ahead SCED Algorithm • Uses an LP solver - Simplex Method • Objective Function: Minimize energy and operating reserve costs over an hour based on submitted offers and subject to constraints • Single offer price for operating reserve • Offer curve for energy • Constraints: • Resource Commitment - Hard • Resource Limit and Ramping - Penalized • Nodal Power Balance - Penalized • Selected transmission - Penalized • Market-wide Reserve Requirement - Demand Curve • Reserve zone Reserve Requirement - Demand Curve

24. Day-Ahead SCUC Algorithm • Uses a MIP (mixed integer programming) solver with cutting planes • Objective Function: Minimize startup, no-load energy and operating reserve costs based on submitted offers • Single offer price for operating reserve • Offer curve for energy • Constraints: • Resource Availability - Hard • Resource Limit - Hard • Resource Inter-Temporal - Hard and Penalized • Global Power Balance - Penalized • Selected transmission - Penalized • Market-wide Reserve Requirement - Demand Curve • Reserve zone Reserve Requirement - Demand Curve

25. UIUC Technical Presentation Generation and Demand Management Improvement with Increased Variable Generation Li Zhang November 12, 2012

26. Wind Generation Locations However, much of the expansion of wind in the MISO footprint has been concentrated to the wind-rich Western portion of our region… …elevating the importance of accurate wind forecasting as variability can be high due to the limited geographical dispersion of wind locations.

27. MISO’s Generation Capacity by Fuel Type • Mainly Coal resource, around 50% of total resources • Addition of wind capacity is significant, increase from 2% in 2007 to more than 8% in 2012

28. MISO Generation MWh by Fuel Type • Heavily dependent on coal, however, reduced from 76% in 2007 to 64% in 2012 • Addition of wind capacity is significant (over 8% in 2012) • Natural gas unit increase in 2012 (>11%) due to relatively lower price

29. Wind Capacity Growth Registered wind capacity has been consistently increasing in the MISO footprint during the last several years, and is expected to continue on that trend in the future.

30. Operational Challenges of Wind Resources • Wind was not included in the Real-Time Market clearing software • No automated dispatch capability • Congestion Management process was manual and Operator-intensive • Phone calls, curtailment MW estimation, etc. • Market Pricing did not reflect wind resources when marginal for congestion or low-loads • Accurate wind forecast is difficult

31. Overview of the Efforts associated with the Increased Variable Generation • MISO has projects to improve uncertainty management and to encourage more flexible generation in operation • Improve Efficiency of Dispatch and Commitment • Day Ahead Ramp Capability (in production) • Dispatchable Intermittent Resources (DIR, in production) • Combined Cycle Unit Commitment (in development) • Look Ahead Commitment (in production) and Look Ahead Dispatch (in development) • Improve Handling of Uncertainty • Stochastic Unit Commitment (in development) • Demand Response Program (in production and further development) • Better forecasting • Improve Pricing • Extended LMP (in implementation), Scarcity Pricing Design • This presentation will cover DIR and Stochastic UC

32. Dispatchable Intermittent Resources

33. Dispatchable Intermittent Resources • Highlights • Uses forecasted maximum output (10 min ahead) as the Economic Max for the resource. • Enables wind to be automatically dispatched down or back up in real-time based on a resource-level offer price and system conditions. • Wind resources eligible to set prices and receive Revenue Sufficiency Guarantee payments (or uplift payment) in the energy market. • Benefits • Increased capacity factor for DIRs compared to other wind resources. • Improved congestion management (reduced manual curtailments). • Enhanced ability to manage Minimum Generation conditions. • Improved market price performance. • Reduced system regulation burden due to wind variability in dispatch time-frame. • Improved overall system control performance.

34. DIR Design Highlights • A DIR is a new Resource type in MISO’s market • Resource-owner registers the resource as “DIR”, rather than “Intermittent” • Registration rules align resource firm transmission service with continued registration as Intermittent Resource • Beginning 3/1/2013, wind resources without firm service, and with in-service date after 4/1/2005, may no longer participate as Intermittent

35. DIR Design Highlights • DIR is a platform for participation consistent with current generation resources to the greatest extent possible • Day-Ahead and Real-Time Energy Offers • Resource Modeling • Ramp rates • Capacity limit • Operational Parameters • Communication Requirements • Settlements Implementation • Real-Time update of maximum capacity limit provides full participation for DIRs • Forecast provided by Resource owner, with MISO back-up available

36. DIR Participation Participation continues to increase since DIRs began participating in June, 2011

37. Wind Utilization *Sum of Hourly ICCP data *On June 01, 2011, MISO successfully launched Dispatchable Intermittent Resources (DIRs), allowing participation in the Real-Time energy market. **Hourly ICCP data. Source: MISO Real-Time Operations and Transmission Asset Management Departments

39. Annual Wind Curtailment Summary

40. Market Pricing and Efficiency Due to DIR • Manual Curtailments during June-July 2010 • $15.15  Average LMP of curtailed wind resource (using manual process) • DIR Dispatch during June-July 2011 • Average LMP of -$43.03 during DIR downward dispatch • DIRs have been the system marginal resource during multiple low-load periods • Improve the price signals

41. Summary of the Dispatachable Intermittent Resources (DIR) Production • Some observations: • Congestion management seemed improved • Some DIRs followed dispatch quite well at certain periods • With this addition, operation has more means to manage the system • When wind generation drops suddenly, operation can commit off-line fast start CTs • Wind generation (DIR) will be dispatched down if the level of wind generation causing trouble, instead of curtailment • The DIR capacity will increase to 6084MW this October

42. Brief Review of Reliability Assessment Commitment

43. Brief Review of Reliability Assessment Commitment (RAC) • A commitment process after Day Ahead clearing and throughout the operating day • The purpose of the RAC process is to ensure that sufficient capacity is available to meet Real-Time demand for energy and reserves. • Forward RAC is run prior to the operating day for the entire day. • Intra-day RAC is run periodically during the day and covers a period from current hour to the end of the day. • RAC depends upon forecasts of Demand, Net Scheduled Interchange, Intermittent Resource Availability, etc. • Considerable uncertainty can exist in the forecasts given that they cover periods that may be several hours in the future.

44. Dealing with Uncertainty • The current RAC formulation employs a deterministic unit commitment • How to deal with uncertainty? • Allocating enough operating reserve • Usually to cover the worst case scenario, can be expensive • Operator’s judgment and response to uncertainty • Characteristics of the resources for commitment can be quite different • Slow start resources, long notification time and may require hours to come on-line • Fast start, can be on line within 10 or 30 minutes etc • Commitment of fast start resources can wait till real time, after uncertainty resolved.

45. Dealing with Uncertainty • Ideally, we should commit resources taking into account the uncertainties around future conditions (Demand, NSI, Intermittent Resource Availability, etc.) at the time. • The state at time t will consist of the demand, NSI, intermittent resource availability, etc. at time t as well as the states (demand, NSI, intermittent resource availability) that were occupied in times 1 through t-1 • As time moves forward, past states will be known and future states will be subject to uncertainty. • Initially, we will assume that we have an estimate of the probability that the system will be in a given state at a particular time given the states prior to that time. • Probabilistic optimization is a natural framework to deal with such a problem.

46. Probabilistic Optimization Framework

47. Framework • Assume that we will run commitment and dispatch problems at times 1, 2, … T. • At time 0 (prior to the operating day) we will run a commitment problem only. • Suppose that we are at time t: • Outcomes for all conditions at time = 1, 2, …, t are known. • Commitment and dispatch actions taken at time = 0, 1, … t-1 are fixed. • Given the state at time t, we have estimates of the probability distribution for states at time t+1. • Similarly, for each state at time t+1, we have probability distributions for states at time t+2; etc.

48. Framework • The next slide shows the tree of possible future states starting at time 0. • At time t, we know the state of the system. Pruning the tree to start at this state and moving to times t+1,…, T shows the possible future states and their probabilities. • The part circled in red, shows the tree of possible future states starting from time 2 assuming that we are in state 1 at time 2.

49. Probability of transition from State 1 at time t1 to State 1 at time t2 1 … 2 1 3 4 … 5 2 6 7 … 3 8 9 Framework Probability of transition from State at time0 to State 1 at time t1 Stateat Possible States at Possible States at Possible States at Possible States at t0 t1 t2 … T-1 T

50. Framework • At time t, we want to determine: • Resources to which we should send start signals at time t • Dispatch instructions to resources on line at time t to meet requirements at time t • We want to minimize: • The cost of commitment and dispatch actions taken at time t plus • The expected costs of commitment and dispatch actions that we will take at times t+1,…, T to meet requirements in the future. • We minimize expected production cost from time t through T given the state at time t.