Economic Benefits of Balancing Area Consolidation

1. Economic Benefits of Balancing Area Consolidation By Todd Ryan Graduate Student Researcher and Ph.D. Student Dept. of Engineering and Public Policy Advised by Paulina Jaramillo and Gabriela Hug

2. Balancing Area Consolidation is one option for counteracting the variability of renewable generation • Wind quickly growing on the grid, from 6 GW in 2004 to 24 GW in 20081 • The variability of wind (and other renewables) makes balancing the power system more difficult2 • Balancing Area Consolidation allows Balancing Areas (BA) to reduce variability and cost3,4 • Based on summer capacity – EIA • “20% Wind Energy by 2030…” NREL (2008) • Milligan, M. et al (2010). “Combining Balancing Areas' Variability…” • Makarov, Y. et al; (2010). “Analysis Methodology for Balancing Authority …”

3. A Balancing Area is a region that has to stay… balanced Generation + Imports = Load + Exports Import Export • “Balanced” means meeting reliability standards

4. Frequency Regulation counteracts the short-term variability • Frequency Regulation a per MW balancing • Most expensive of the ancillary services • Based on Automatic Generation Control (AGC) • Not to Frequency directly • AGC signal based on the Area Control Error, a measure of balance (ACE) • Goal: reliability standards (CPS 1 & CPS 2)

5. How BA Consolidation Creates Cost Savings in two way • Physical Aggregation: coordinate all markets • Virtual Aggregation: coordinate a specific market Price Reduction Quantity Reduction Price Price Quantity Quantity

6. Balancing Areas sizes span 3° of magnitude Data Source: "NERC 2010 CPS 2 Bounds Report” • BA’s at different levels of consolidation ranging • Disaggregated: Small 100 MW generation-only BAs • Fully Aggregated: 100,000 MW peaking ISO/RTOs

7. Calculates the benefits of BA Consolidation by varying high-level parameters • Previous studies have shown the benefits via case-studies • This research aims to be find generalized cases where BA consolidation have the largest benefits • Look at fictitious BAs vis-à-vis varying high-level BA parameters • Models the cost of Energy and Regulation pre and post post consolidation • Future research will focus on modeling the cost of consolidation Parameters of BAs (pre aggregation): • Size of BAs {200; 2k; 7k; 12k} MW • Number of BAs {2, 3, 4} • Fuel Mix of each BA {US Avg; High Coal; High NG} • Wind Penetration {5%; 10%; 20%; 30%} By energy

8. This is a simple Co-Optimization Model • Minimizes the production costs of Energy and Regulation • Includes the opportunity cost of providing Regulation • Includes on Regulation market as it is used for addressing short-term variability • Does not include ramping or start-up constraints • Inputs • Load Data (NYISO 5-minute load) • Regulation Requirement (NYISO Hourly schedule) • Marginal Cost (Historic Bids and NEEDs) • Wind Data (EWITS)

9. Co-Optimization Formulation Min (Total Cost) Subject to: • Total Generation = Total Load • Total Regulation = Regulation Requirement • Geni ≤ Bid in amount of Generation • Regi≤ Bid in amount of Regulation • Geni + Regi ≤ Upper Operation Limit Total Cost = Cost of Energy + Cost of Regulation Regulation Cost includes Opportunity Cost

10. Two Sources for Marginal costs estimates • Historic Bids • Provides realistic numbers for market costs • Less flexible for use in modeling • NEEDS database • May not fully reflect what the real markets pay • Easy to use when modeling: • Can construct a fictitious fleet for each BA • Does not include regulation costs – need to estimate this based on plant characteristics

11. Creating BAs by dividing and combining NYISO Bids • Choose the number and sizes of BAs • Divide bids into equal segments • Combine segments to create different sizes NYISO Bids

12. Creating BAs from NEEDs and EWITS data by matching fuel mix, capacity, and Renewable % Balancing Area [Size & Fuel Mix] [Wind Penetration] Select Optimal Plants Selected by Capacity Factor Plants from NEEDs EWITS Wind Farms Photo Source: Microsoft Clip Art 2011

13. Initial Qualitative Results • Savings in Energy Market are greater than Regulation Market • But comes with additional cost of coordinating dispatch • Coordinate with someone different • Two’s company, three’s a crowd

14. BA Consolidation has a real benefit to society but.. • Not all BAs win • Kaldor-Hicks efficient, not Pareto efficient • Winner’s need to compensate the losers • Could have localized effects that need to be monetized • e.g., congestion, loop flows, inadvertent energy • What does it cost to share variability?

15. Thank to:..Dept. of Engineering & Public Policy Paulina Jaramillo and Gabriela HugD.L. Oates and Allison WeisAnd to you for listening!Questions? Todd Ryan Graduate Student Researcher and Ph.D. Student Engineering and Public Policy Carnegie Mellon University toddryan@andrew.cmu.edu

16. Bids • Anonymously released by ISO’s on a six month lag • Includes: (bold values are used in my model) • Date/Time • Resource ID • Market • Self-Schedules • Dispatch Type • Energy Bids • Regulation Bids • Spin/Non-Spin Bids • Upper Operating Limits • Emergency Max • Start-up Cost • Min. Gen and Cost

17. Total Cost • Cost of Energy • Cost to Market = LMP*(Total Generation) • Cost to Suppliers = Σ(marginal costi x MWi) • Cost of Regulation • Cost to Market = RCP*(Total Regulation) • Cost to Suppliers = Σ(marginal costi+ OCi)(Mwi) Easier to formulate costs to suppliers because it doesn’t depend on the marginal prices; OC is tough to incorporate in the simplest formulation; therefore using a decomposition method simplifies this co-optimization

18. Decomposition of the Co-Optimization allows for easy formulation of the problem Sub-Problem 1 Min Cost of Energy s.t.: • Total Generation = Total Load • Geni ≤ Gen Bidi Sub-Problem 2 Min Cost of Regulation s.t.: • Total Regulation = Regulation Req • Regi ≤ RegBidi Coupling Constraint: Geni+ Regi ≤ Upper Operation Limit Coupling constraint assigned to sub-problem 1 Sub-problems solved in parallel and after each iteration, decision variables and Lagrange multipliers are updated and exchanged

19. Opportunity Cost • Opportunity Cost is the margin that a producer loses by using capacity for reserves instead of producing energy • It is a real cost, but is difficult to bid in as a producer because it is a function of the energy price (unknown until that interval) • OC = (LMP – E_bid)(MW* - MW_act) • MW* = MW if only providing Energy • MW_act = anticipated MW production the resource will output including energy and reserve dispatch

20. OC is needed to find the merit order LMP = $10; RCP = $15 Resource’s bid: 100 MW Energy at $5 Reg_bid: 10MW Regulation at $14 Opportunity Cost: ($10-$5)(100-90) = $50 or $2.50 per MW Regulation Total Marginal cost of Regulation = $16.50 ($14 + $2.50 of OC) Without Opportunity Cost Unit is in merit for energy and regulation Revenue: $10*90 + $15*10 = $1,050 Cost: $5*90 + $16.50*10 = $615 Profit: $435 With Opportunity Cost Unit is in merit for energy but not regulation Revenue: $10*100 MW = $1,000 Cost: $5*100 MW = $500 Profit: = $500

21. NEEDS Data • National Electric Energy Data System • Includesbasic description of plants • Heat-rate, fuel, and size can be used to estimate marginal cost of energy • Marginal cost of Regulation is estimated by regression analysis of historic bids for energy and regulation

22. Future Research on quantitating cost side of the benefit-cost analysis • Between now and Quals • Run model using NEEDS data • Finish parametric analysis • Post Quals • Electric modeling to assess costs of localized effects of BA consolidation e.g., congestion, loop flows, inadvertent energy

23. The PSD of wind speeds should follow the Kolmogorov Spectrum

24. Truncating the spectrum due to grid scale leads to an underestimation of variability over many time-scales

25. Up-Sampling of EWITS results doesn’t work • Up-Sampling technique (Rose et al) assumes that the the wind speeds follow the Kolmogorov spectrum • Up-Sampling technique results in a discontinuous PSD

26. Developed technique to extend EWITS Matched the slope of the EWITS spectrum at high frequency

27. Researchers Need Better Data • EWTIS / WWSIS are currently the best data sets • 30,000+ sites; 3 years of data; covering most of the United States • All studies that are based on these data sets underestimate the effects of wind • Re-running the study with a smaller grid scale may not be feasible (cost and computation) • Making empirical data available may be the quickest, cheapest, and best from a scientific perspective

28. All Scenarios Result in a lower social cost 10% RESULTS NOT REAL 0% -10% Change in social cost Scenarios Run

29. Consolidating BA with vast differences has the larger benefit

30. Benefits diminish with increasing number of BAs INSERT FIGURE

31. EWITS is the best data set out there • Needed to model renewable penetration • Select sites in order of capacity factor until penetration level is met • Many positive attributes • 1,300+ sites, 3 years of data • Multi-regional • Siting and sizing determined to hit 30% renewable • Tried up-sampling technique to get 4-second data in order to model Regulation • Technique didn’t work – EWITS didn’t match physics (more later) • We no longer could model the Automatic Generation Control and Regulation

32. Working on estimating Regulation cost based on NEEDS data • NEEDs data only includes marginal cost of Energy, no marginal cost of Regulation • Relationship between energy and regulation bids fills this gap