Enerji Programları
K02.Fundamentals of Energy Statistical Analysis and the Real Option Valuation of Energy Asset.
Statistical analysis and real option valuationis frequently misapplied, and many companies find that "a little bit of knowledge is a dangerous thing."
This comprehensive one-day program is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets. The course introduces the fundamental statistical concepts that are most used in the energy industry and builds upon these ideas by examining real industry applications including the various approaches used to value energy and electricity assets as real options.
Be armed with the tools and methods needed to properly analyze and measure data to reduce risk and increase earnings for your organization.
Learn These 3 Keys to Success:
1. Correlation & regression analysis; real option analysis; the Black-Scholes option pricing model; binomial trees; GARCH Models; the measurement of energy price risk; and how to use correlation and regression analysis for maintaining a competitive edge.
2. How to minimize price risk through operational design Flexibility; measure forward price volatility and adapt Value-at-Risk concepts (VaR) for the Energy Industry.
3. Use actual case studies to examine 1) how Monte Carlo simulation is used to value Demand Response programs; 2) benchmarking techniques used for estimating the incremental cost savings of expanding existing operations; and 3) real-option value of generation assets.
Understand These 7 Important Issues:
1. The pros and cons of net present value (NPV), decision analysis (DA), and real options (RO) valuation techniques.
2. Understand the deficiencies of marginal cost valuation, and lean how to develop a real-options approach based on forward price simulation of fuel and energy markets.
3. The most common financial pricing models including start-to-finish development of the GBM and Mean Reversion (Jump Diffusion) models which will be defined, regressed, and simulated.
4. The relationship between financial options and real options, and the three methods of valuing options – Black Scholes, binomial trees, and Monte Carlo simulation.
5. How Black Scholes, binomial trees, and Monte Carlo simulation method are applied to value natural gas storage, electric generation, and energy portfolio assets.
6. The role of volatility, portfolio considerations, and risk management implications to asset pricing and valuation.
7. How to value generation assets using real option competitive price analysis.
Seminar Agenda
- The Basics of Deterministic vs. Probabilistic Thinking in Deregulated Markets
- Means vs. Standard Deviations
- Distribution Shapes
- Confidence Intervals
- Probability
- Simulation
Application:
Monte Carlo Simulation
Example—Customer Migration Model Estimating Migration out of Standard Offer Service in a Deregulated Retail Electricity Market
- Correlation and Regression Analysis for Maintaining the Competitive Edge
- Univariate and Multivariate Analysis
- Hypotheses Testing
- Testing for Equal Means and Variances
- Control Charts
Application:
Benchmarking to Industry Standards
Example 1—Comparing O&M Expenditure to that of Peer Facilities
Example 2— Estimating the "Economies of Scale" (marginal cost reduction) Associated with Multiple Unit Generation Facilities
- Introduction to Real Options Analysis
- Details of Option Model Implementation
- Net Present Value (NPV) vs. Real Options Analysis (RO)
- Volatility Modeling – What is it and Why we use it?
- Black-Scholes, Binomial Trees, and GARCH Models
- Estimating Volatility and Uncertainty In Historical Prices
- Measuring Forward Volatility
- Adapting Value-at-Risk (VaR) for the Energy Industry
Application:
Real Option Valuation
Example of Valuing Demand Response Programs Using Real Options
Application:
Principles of Risk Management
Example – Calculation of VaR for a Multi Asset Portfolio and Extensions
Application:
Comparing the results of Black-Scholes, Binomial Trees and Simulation in Valuing Distributed Generation - Detailed Example Using Three Separate Real Option Approaches to Value Energy Assets
- Details of Option Model Implementation
- A Generating Unit as a Strip of Options on a Btu Spread
- Measuring Hidden Value in Uncertainty and Optionality
Application:
Comparing Power Plant Value from ProSym Marginal Cost Analysis vs. Real Option Competitive Price Analysis
Example – Comparing Base-load, Mid-Merit and Peaking Units
- The Challenge of Forward Price Simulation
- Monte Carlo Simulation of Stochastic Prices
- Quantitative Models –
- Geometric Brownian Motion
- Mean Reversion
- Markov Regime Switching
- Jump Diffusion
- AR(3) GARCH(1,1)
- Model Selection Criteria
- Implied v. Historic Volatility
- Mark-to Market via Forward Price Hammers
- Correlating Random Numbers using Cholesky Decomposition
- Hourly Unit Commitment and Dispatch Under Price Uncertainty
- Incorporating Engineering Constraints
- Developing Optimal Spark Spread "Turn On" and "Turn Off" Boundaries
- Incorporating Rational Dispatch Behavior
- Using the Unit Commitment Model to Determine an Optimal Operation Schedule
Application:
Valuing Generation Assets Using Real Option Competitive Price Analysis
Step-by-Step Valuation Example for a Portfolio of Generation Assets
Your Instructor
Kenneth Skinner, Ph.D. – Dr. Skinner is a Senior Consultant with Summit Blue Consulting and has nearly 15 years of energy industry experience. As the recent Derivative Structuring Manager for Sempra Energy Solutions, a national energy supplier, Dr. Skinner focused on developing retail commodity supply strategies including portfolio risk management, hedging strategy, and least-cost supply opportunities. In addition to commodity supply, Dr. Skinner was responsible for valuing the benefit of demand reduction programs including the option value of various terms and conditions.
Dr. Skinner has significant experience in economic analysis and modeling of energy demand, forward energy prices, financial derivatives, transmission and pipeline capacity, natural gas storage, and generation assets using econometric time-series and cross-sectional analysis, statistical methods, optimization principles, real option valuation techniques; Structured valuation of distributed generation and electricity and natural gas commodity transactions, including demand response; Financial risk assessment and valuation of energy hedging strategies and market potential of new business ventures. He is fully trained in econometric methods, engineering principles, organizational development, optimization, and valuation techniques and has extensive experience with various software packages including Visual Basic, C++, SAS, SPSS, Crystal Ball, Matlab, GAMS, IREMM and ProSym.
Who Should Attend this Seminar?
Electric utilities, generators, marketers and industrials; corporate planners, economists, rate making staff, energy and electric power executives; government regulators; traders & trading support staff; marketing, sales, purchasing & risk management personnel; accountants & auditors; plant operators, and engineers
