1. Zonal Electricity Supply Curve Estimation with Fuzzy Fuel Switching Thresholds
North American power grid is “the largest and most complex machine in the world” Amin, (2004)
Mostafa Sahraei-Ardakani
Seth Blumsack
Andrew Kleit
Department of Energy and Mineral Engineering
Penn State University

2. Motivation
How to analyze supply and demand policies considering the transmission constraints ?
Pennsylvania’s Act 129: Energy conservation and peak demand reduction in Pennsylvania.
What would happen to the prices in PA?
What would happen to the prices in other states?
What would happen to the emissions?
Carbon tax:
What would happen to the prices?
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3. Dispatch Curve Model
What would happen to electricity prices if a CO2 price was imposed?
Engineers
Very complex model
Data may not be publicly available
Policy analysts
Collect marginal cost data from power plants
Collect fuel price data
Form a supply curve by sorting generators from cheap to expensive
Ignore transmission network
Each point represents a single power plant
Newcomer et al., 2008
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4. Different Models Engineering models Econometric models Dispatch curve
Too complex
Data may not be available
Takes a long time to converge
Econometric models
Estimate prices well
Do not do a good job in estimating
fuel mix and emission impacts of policies.
Dispatch curve
Ignores transmission system and how congestion makes prices different.
Our model
Needs no more data than a dispatch curve
Implicitly accounts for transmission constraints
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5. Other Approaches Econometric models
Predict prices well.
Do not do a good job on estimating fuel utilization.
Engineering models: Power Transfer Distribution Factor (PTDF)
Need detailed data which is not publicly available.
They are complex and take a lot of time to converge for large power systems.
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6. Our Approach For each zone we want to identify:
Thresholds where the marginal fuel changes (Coal, Gas, Oil)  CMA-ES
Fixed and variable thresholds
The slope of each portion of the overall dispatch curve.  OLS
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7. Fuzzy Thresholds qT Variables to be estimated: Fuzzy Thresholdsqi qT
ΔC/G
100% Natural Gas
100% Coal
50% Coal, 50% Natural Gas
Observations
100% Oil
Fuzzy Gap
qT,C/G
qi,G/O
Variables to be estimated:
Relative fuel price threshold for having the fuzzy gap
Fuzzy gap width coefficient
Fuzzy Thresholds
Summer 2008
Deterministic
Thresholds
GAS
COAL
Summer 2011
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8. Implementation in PJM Seventeen PJM utility zones Data: (2006-2009)
Hourly zonal load
Hourly zonal prices
Fuel prices
Insufficient data for
nodal level modeling
Robustness Check:
Linear and quadratic
curves
Fixed and Variable
Thresholds
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9. Results: Thresholds Price ($/MWh) Marginal Fuels in PSEG
Load in PSEG (GW)
Total Load in PJM (GW)
Price
($/MWh)
Gas
Coal
Oil
Gas-Oil
Fuzzy Region
Coal – Gas Fuzzy Region
PSEG demand= 5.8 GW
PJM demand= 118 GW
APS price=80 $/MWh
$/MWh
PSEG= Public Service Electric and Gas Company
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10. Results: Supply curve projection
Central Pennsylvania and West Virginia
Philadelphia
Zonal price differences are captured.
50 $/ton carbon tax
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11. Results: Marginal Fuel Shares
DUQ in western PA is a coal dominated zone.
RECO in northern NJ is a natural gas dominated zone.
Natural gas often sets the prices in PJM.
Another robustness check
Natural Gas often sets the prices.
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12. Results: Prices BGE is in eastern PJM (Baltimore).
DUQ is in western PJM (Pittsburgh).
Our model captures zonal price differences.
50 $/ton carbon tax would increase prices by about 70%.
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13. Application: Pennsylvania Act 129
Act 129 is a wide-reaching energy policy initiative in Pennsylvania. Among other things, Act 129 requires all Pennsylvania utilities to:
Reduce annual electricity demand by 1%
Reduce “peak” demand (highest 100 hours) by 4.5%
We will estimate the impacts of Act 129 on total electricity costs, fuels utilization and greenhouse gas emissions in the PJM territory, using our model and the “dispatch curve” model that I discussed earlier. We use 2009 as our “base” year.
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14. Application: Pennsylvania Act 129
Electricity Cost Savings ($ million):
Savings: 333 million dollars
253 million dollars in PA
Dispatch Curve: 150 million dollars
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15. Application: Pennsylvania Act 129
Shifts in Marginal Fuel (% Increase with Act 129):
Emission decreases by 4 million metric tons.
Dispatch Curve: 2.3 million metric tons.
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16. Conclusions
We have developed an approach to estimating zonal supply curves in transmission constrained electricity markets:
Requires no proprietary data
Can be implemented by analysts without requiring complex engineering calculations
Our approach captures regional effects of policies that “transmission-less” dispatch models do not. Regional impact differences may be important in policy evaluation.
Zonal fuel utilization shift
Zonal price differences
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17. Thanks! Mostafa Sahraei-Ardakani
Department of Energy and Mineral Engineering
Penn State University
Comprehensive Exam

18. Price Increase in DC 1 2 3 MC1=MC2 P1+P2=10+50 (MW) 10 MW MC1=P1
Rest of PJM
Virginia and Washington, DC
10 MW
MC1=P1
MC2=10+P2
50MW 1 2 3
λ1=MC1=35 ($/MWh)
λ1=MC1=20 ($/MWh)
20MW
35MW
25$/MWh
25MW
25MW
50/3
25/3
25/3
50/3
20MW
30MW
10MW
Thermal Capacity =20 MW
5MW
40$/MWh
λ2=MC1=50 ($/MWh)
λ2=MC2=35 ($/MWh)
25MW
25MW
40MW
45MW
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30MW