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Economic Benefits of Balancing Area Consolidation
By Todd RyanGraduate Student Researcher and Ph.D. Student
Dept. of Engineering and Public PolicyAdvised by Paulina Jaramillo and Gabriela Hug
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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
1. Based on summer capacity – EIA 2. “20% Wind Energy by 2030…” NREL (2008)3. Milligan, M. et al (2010). “Combining Balancing Areas' Variability…”4. Makarov, Y. et al; (2010). “Analysis Methodology for Balancing Authority …”
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A Balancing Area is a region that has to stay… balanced
Generation + Imports=
Load + ExportsImport Export
“Balanced” means meeting reliability standards
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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)
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How BA Consolidation Creates Cost Savings in two way
Quantity
Pric
e
Price Reduction
Quantity
Pric
e
Quantity Reduction
• Physical Aggregation: coordinate all markets
• Virtual Aggregation: coordinate a specific market
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Balancing Areas sizes span 3° of magnitude
• BA’s at different levels of consolidation ranging – Disaggregated: Small 100 MW generation-only BAs– Fully Aggregated: 100,000 MW peaking ISO/RTOs
Data Source: "NERC 2010 CPS 2 Bounds Report”
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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 consolidationParameters 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
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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)
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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
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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
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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
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Creating BAs from NEEDs and EWITS data by matching fuel mix, capacity, and Renewable %
Plants from NEEDs EWITS Wind Farms
Balancing Area[Size & Fuel Mix] [Wind
Penetration]
Select Optimal Plants Selected by Capacity Factor
Photo Source: Microsoft Clip Art 2011
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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
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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?
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Thank to:..Dept. of Engineering & Public Policy Paulina Jaramillo and Gabriela Hug
D.L. Oates and Allison Weis
And to you for listening!
Questions?
Todd RyanGraduate Student Researcher and Ph.D. Student
Engineering and Public PolicyCarnegie Mellon [email protected]
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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
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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
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Decomposition of the Co-Optimization allows for easy formulation of the problem
Sub-Problem 1Min Cost of Energys.t.:– Total Generation = Total Load– Geni ≤ Gen Bidi
Sub-Problem 2Min Cost of Regulations.t.:– Total Regulation = Regulation Req– Regi ≤ Reg Bidi
Coupling Constraint: Geni + Regi ≤ Upper Operation LimitCoupling constraint assigned to sub-problem 1
Sub-problems solved in parallel and after each iteration, decision variables and Lagrange multipliers are updated and exchanged
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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
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OC is needed to find the merit order
LMP = $10; RCP = $15Resource’s bid: 100 MW Energy at $5 Reg_bid: 10MW Regulation at $14Opportunity Cost: ($10-$5)(100-90) = $50 or $2.50 per MW RegulationTotal Marginal cost of Regulation = $16.50 ($14 + $2.50 of OC)
With Opportunity CostUnit is in merit for energy but not regulation
Revenue: $10*100 MW = $1,000
Cost: $5*100 MW = $500
Profit: = $500
Without Opportunity CostUnit is in merit for energy and regulation
Revenue: $10*90 + $15*10 = $1,050
Cost: $5*90 + $16.50*10 = $615
Profit: $435
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NEEDS Data
• National Electric Energy Data System• Includes basic 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
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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
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The PSD of wind speeds should follow the Kolmogorov Spectrum
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Truncating the spectrum due to grid scale leads to an underestimation of variability over many time-scales
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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
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Developed technique to extend EWITS
Matched the slope of the EWITS spectrum at high frequency
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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
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All Scenarios Result in a lower social cost
Scenarios Run
Chan
ge in
soc
ial c
ost
0%
-10%
10% RESULTS NOT REAL
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Consolidating BA with vast differences has the larger benefit
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Benefits diminish with increasing number of BAs
INSERT FIGURE
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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
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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