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Outline of Talk
• Background on Deregulated Power Markets § Regulated vs. Deregulated Power markets§ Market Structure and Participants§ Risk Exposures
• Decision Making Under Uncertainty§ Deterministic Analysis§ Sensitivity Analysis§ Monte Carlo Simulation§ Optimizing the Decision Making Process
•Monte Carlo Simulation§ Model Specification§ Model Estimation§ Model Simulation§ Calibration§ Benchmarking
Analytics for Deregulated Power Markets
•Business questions:§What is my portfolio worth? (valuation)§ How much of my expected dispatch output should I sell
into the forward market? (hedging)§ How much money can I lose? (risk management)§What trades should I enter into so I can maximize my
profits and minimize my risk? (portfolio optimization)
Regulated vs. Deregulated Power Markets
6
Power (MWh)
Load
Generator
Payment ($)
Regulated Setup
ISO
Generator Load
Deregulated Setup
Paym
ent (
$)
Power (M
Wh)
Payment ($)
Pow
er (
MW
h)
Risk Exposure to Power Price MovementsPa
yoff
($)
Power Price ($/MWh)
Generator Load
Payo
ff (
$)
Power Price ($/MWh)
Decision Making Under Uncertainty
• Risk Drivers
§ Deterministic scenario planning models
§ Sensitivity analysis
§ Monte Carlo simulation
• Optimizing the Decision Making Process§ Unconstrained Optimization
§ Constrained Optimization
10
Deterministic Planning Models
• Deterministic planning models§ Pro:
o Simple
§ Con:o How to come up with assumptions?o Are these assumptions realistic?o Doesn’t acknowledge uncertaintyo Can lead to biased decisions
12
Sensitivity Analysis
• Sensitivity analysis
§ Pro:
o Simple
§ Con: o How to create sensitivity scenarios?o Are these scenarios realistic?
• In general the following does not hold, especially for nonlinear functions
16
Monte Carlo Simulation
• Monte Carlo simulation§ Pro:
o Realistic representations of possible states of the world (this could actually happen)o Correlations are maintainedo Can benchmark against actual price distributions
§ Cons:o Complex, slow
17
Optimizing the Decision Process
• Given the prices, we want to optimize a decision process
• Example:§ European Call Option
o Value a call option, value=max(P-K,0) à simple decision rule, if P>K then exercise, otherwise don’t
o Decisions today don’t impact decisions tomorrow
§ Power Plant
o Operational constaints à can’t turn on and off instantlyo How to optimize the decision process, given that decisions today impact possible
decisions tomorrow?o Answer is provided through dynamic programming
18
Monte Carlo Framework
- Model Specification
- Specify a model of the fundamental risk drivers
- Model Estimation
- Estimate the unknown parameters of the model
- Simulation
- Simulate the risk drivers
- Calibration
- Use any known information to calibrate the simulations, to match observed real world quantities
- Decision Making
- Optimize the decision process
- Summarize
- Summarize the outcomes (e.g. using probability distributions)
Overview of PowerSimm Processes
22
WX Sim Load Sim
Spot Price Sim
Forward Price Sim
Calibrated Spot Price Data
Dispatch
Portfolio Summarization
Marginal Price of Electricity
$/M
Wh
MW
Supply
Demand
Baseload (Coal) Peakers (CTs)
Marginal price of electricity
Midmerit (CC)
P1
P2
Analytics for Deregulated Power Markets
•Business questions:§What is my portfolio worth? (valuation)§ How much of my expected output should I sell into the
forward market? (hedging)§ How much money can I lose? (risk management)§What trades should I enter into so I can maximize my
profits and minimize my risk? (portfolio optimization)
How Sensitive is My Portfolio To Prices?
38
Sensitivity of gross margin = $19 million
per $/MWh
Optimal forward sale = ~1500 MW