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Monte Carlo Simulation for Energy Risk Management

Monte Carlo Simulation for Energy Risk Management. Scotty Nelson. January 15, 2013. 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

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Monte Carlo Simulation for Energy Risk Management

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  1. Monte Carlo Simulation for Energy Risk Management Scotty Nelson January 15, 2013

  2. 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

  3. 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)

  4. Background on Deregulated Power Markets

  5. State of Deregulation Source: Department of Energy

  6. Regulated vs. Deregulated Power Markets Deregulated Setup Regulated Setup Power (MWh) Generator Load Load Generator Payment ($) Payment ($) Power (MWh) Power (MWh) Payment ($) ISO

  7. Risk Exposure to Power Price Movements Load Generator Payoff ($) Payoff ($) Power Price ($/MWh) Power Price ($/MWh)

  8. Hedge Optimization

  9. Decision Making Under Uncertainty

  10. Decision Making Under Uncertainty • Risk Drivers • Deterministic scenario planning models • Sensitivity analysis • Monte Carlo simulation • Optimizing the Decision Making Process • Unconstrained Optimization • Constrained Optimization

  11. Dispatch Optimization

  12. Deterministic Planning Models • Deterministic planning models • Pro: • Simple • Con: • How to come up with assumptions? • Are these assumptions realistic? • Doesn’t acknowledge uncertainty • Can lead to biased decisions

  13. Historical versus expected – Henry Hub

  14. Historical versus expected – West Hub

  15. Historical implied heat rate versus expected implied heat rate

  16. Sensitivity Analysis • Sensitivity analysis • Pro: • Simple • Con: • How to create sensitivity scenarios? • Are these scenarios realistic? • In general the following does not hold, especially for nonlinear functions

  17. Monte Carlo Simulation • Monte Carlo simulation • Pro: • Realistic representations of possible states of the world (this could actually happen) • Correlations are maintained • Can benchmark against actual price distributions • Cons: • Complex, slow

  18. Optimizing the Decision Process • Given the prices, we want to optimize a decision process • Example: • European Call Option • Value a call option, value=max(P-K,0)  simple decision rule, if P>K then exercise, otherwise don’t • Decisions today don’t impact decisions tomorrow • Power Plant • Operational constaints  can’t turn on and off instantly • How to optimize the decision process, given that decisions today impact possible decisions tomorrow? • Answer is provided through dynamic programming

  19. Dispatch Optimization

  20. Monte Carlo Simulation

  21. 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)

  22. Overview of PowerSimm Processes WX Sim Load Sim Spot Price Sim Calibrated Spot Price Data Dispatch Forward Price Sim Portfolio Summarization

  23. Marginal Price of Electricity Demand Supply P2 Marginal price of electricity $/MWh P1 Peakers (CTs) Baseload (Coal) Midmerit (CC) MW

  24. Weather – historical relationships

  25. Weather – modelling – vector autoregression

  26. Weather – simulated temperature – temporal correlations

  27. Weather – simulated temperature – benchmarking

  28. Load – historical relationships Summer Load Profile Winter Load Profile Load vs Temperature

  29. Load – modelling – model specification

  30. Load – benchmarking simulations

  31. Load – benchmarking simulations

  32. Spot Prices – historical relationships

  33. Spot Prices – modelling

  34. Spot Prices – simulation results

  35. Wrapup

  36. 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)

  37. What is My Portfolio Worth? Expected Value of Portfolio Gross Margin At Risk

  38. How Sensitive is My Portfolio To Prices? Sensitivity of gross margin = $19 million per $/MWh Optimal forward sale = ~1500 MW

  39. Questions? Scotty Nelson snelson@ascendanalytics.com

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