Transcript Economic Benefits of Balancing Area Consolidation

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
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
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2.
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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 …”
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A Balancing Area is a region that has to stay…
balanced
Import
Generation + Imports
=
Load + Exports
Export
“Balanced” means meeting reliability standards
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Frequency Regulation counteracts the shortterm 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 Reduction
Price
Price
Price Reduction
Quantity
Quantity
• Physical Aggregation: coordinate all markets
• Virtual Aggregation: coordinate a specific market
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Balancing Areas sizes span 3° of magnitude
100%
Mean Value
7,500 MW
CDF Percen le
80%
60%
Median
2,188 MW
40%
20%
0%
10
100
1,000
10,000
100,000
Balancing Area Size (MW)
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
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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
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
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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
Regulation Requirement
Marginal Cost
Wind Data
(NYISO 5-minute load)
(NYISO Hourly schedule)
(Historic Bids and NEEDs)
(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 %
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
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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 Ryan
Graduate Student Researcher and Ph.D. Student
Engineering and Public Policy
Carnegie Mellon University
[email protected]
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 cooptimization
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Decomposition of the Co-Optimization allows
for easy formulation of the problem
Sub-Problem 1
Min Cost of Energy
s.t.:
Sub-Problem 2
Min Cost of Regulation
s.t.:
– Total Generation = Total Load
– Geni ≤ Gen Bidi
– Total Regulation = Regulation Req
– Regi ≤ Reg Bidi
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
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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 = $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)
With Opportunity Cost
Unit is in merit for energy but not
regulation
Without Opportunity Cost
Unit is in merit for energy and
regulation
Revenue: $10*100 MW = $1,000
Revenue: $10*90 + $15*10 = $1,050
Cost: $5*100 MW = $500
Cost: $5*90 + $16.50*10 = $615
Profit: = $500
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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Change in social cost
All Scenarios Result in a lower social cost
10%
RESULTS NOT REAL
0%
-10%
Scenarios Run
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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
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