zonal electricity supply curve estimation with fuzzy fuel switching thresholds
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Mostafa Sahraei-ArdakaniSeth BlumsackAndrew Kleit
Department of Energy and Mineral EngineeringPenn State University
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)
04/22/2023
MOTIVATION
2
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?• What would happen to the emissions?
04/22/2023
DISPATCH CURVE MODEL
3
• 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
New
com
er e
t al.,
200
8
04/22/2023
DIFFERENT MODELS
4
• Engineering models– 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
04/22/2023
OTHER APPROACHES
5
• 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.
04/22/2023
OUR APPROACH
6
For each zone we want to identify:
1. Thresholds where the marginal fuel changes (Coal, Gas, Oil) CMA-ESFixed and variable thresholds
2. The slope of each portion of the overall dispatch curve. OLS
04/22/2023
FUZZY THRESHOLDS
7
GAS
COAL
Summer 2008DeterministicThresholds
Summer 2011
qi
qT
ΔC/G
100% Natural Gas100% Coal
50% Coal, 50% Natural Gas
Observations100%
Natural Gas100%
Oil
Fuzzy Gap
qT,C/G
qi,G/O
Fuzzy ThresholdsVariables to be estimated:1. Relative fuel price
threshold for having the fuzzy gap
2. Fuzzy gap width coefficient
04/22/2023
IMPLEMENTATION IN PJM
8
• 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
04/22/2023
Marginal Fuels in PSEG
Load in PSEG (GW)
Tota
l Loa
d in
PJM
(GW
)
Price ($/MWh)
Gas
Coal
Oil
Gas-Oil Fuzzy Region
Coal – Gas Fuzzy Region
RESULTS: THRESHOLDS
9
PSEG= Public Service Electric and Gas Company
$/MWhPSEG demand= 5.8 GWPJM demand= 118 GWAPS price=80 $/MWh
04/22/2023
RESULTS: SUPPLY CURVE PROJECTION
10
Central Pennsylvania and West Virginia Philadelphia
• Zonal price differences are captured.•50 $/ton carbon tax
04/22/2023
RESULTS: MARGINAL FUEL SHARES
11
• Another robustness check• Natural Gas often sets the prices.
•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.
04/22/2023
RESULTS: PRICES
12
•BGE is in eastern PJM (Baltimore).
•DUQ is in western PJM (Pittsburgh).
•Our model captures zonal price differences.
•50 $/ton carbon tax would increase prices by about 70%.
04/22/2023
APPLICATION: PENNSYLVANIA ACT 129
13
• Act 129 is a wide-reaching energy policy initiative in Pennsylvania. Among other things, Act 129 requires all Pennsylvania utilities to:
1. Reduce annual electricity demand by 1%
2. Reduce “peak” demand (highest 100 hours) by 4.5%
• We will estimate the impacts of Act 129 on total electricity costs, fuels utilization and greenhouse gas emissions in the PJM territory, using our model and the “dispatch curve” model that I discussed earlier. We use 2009 as our “base” year.
04/22/2023
APPLICATION: PENNSYLVANIA ACT 129
14
Electricity Cost Savings ($ million):
• Savings: 333 million dollars
•253 million dollars in PA
•Dispatch Curve: 150 million dollars
04/22/2023
APPLICATION: PENNSYLVANIA ACT 129
15
Shifts in Marginal Fuel (% Increase with Act 129):
Emission decreases by 4 million metric tons.
Dispatch Curve: 2.3 million metric tons.
04/22/2023
CONCLUSIONS
16
• 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
Mostafa Sahraei-Ardakani
Department of Energy and Mineral EngineeringPenn State University
Comprehensive Exam
Thanks!
04/22/2023
PRICE INCREASE IN DC
18
Rest of PJM
Virginia and Washington, DC
10 MW
MC1=P1
MC2=10+P2 50MW
1
2 3
35MW
25MW
50/325/325/3
50/3
25MW
25MW
λ1=MC1=35 ($/MWh)
λ2=MC2=35 ($/MWh)
Thermal Capacity =20 MW
20MW
40MW
20MW
30MW
10MW
λ1=MC1=20 ($/MWh)
λ2=MC1=50 ($/MWh)
45MW
25MW
30MW
25MW
5MW
25$/MWh
40$/MWh
MC1=MC2P1+P2=10+50 (MW)