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?

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 Universitytoddryan@andrew.cmu.edu

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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