|
Full Course Description
ON SITE!
Intermediate Applications of Energy Statistics
New York, NY - September 24 & 25, NYC Torch Club (NYU Campus)
Houston, TX - October 10 & 11, Homewood Suites by Hilton Houston Near the Galleria
Houston, TX - December 11 & 12, DoubleTree Suites by Hilton Hotel Houston by the Galleria
>> Register
Companies continue to be exposed to significant energy and electricity related price risk, and this risk needs to be properly quantified. Energy and electricity companies worldwide depend on accurate information about the risks and opportunities facing day to day decisions. Statistical analysis is frequently misapplied and many companies find that "a little bit of knowledge is a dangerous thing."
Operational decisions, capital investment, risk management, strategic positioning, litigation, and marketing are some of the many areas that require accurate information and analysis founded on sound statistical principles. This comprehensive two-day program is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets. 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 Keys to Success:
- 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.
- How to minimize price risk through operational design Flexibility; measure forward price volatility and adapt Value-at-Risk concepts (VaR) for the Energy Industry.
- 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.
Seminar Agenda
DAY ONE:
The Basics of Deterministic vs. Probabilistic Thinking in Deregulated Markets (2.0 hours)
- Means vs. Standard Deviations
- Distribution Shapes
- Confidence Intervals
- Probability
- Simulation
Application: Setting up a Monte Carlo Simulation
Example 1-Confidence Intervals for Calculating Value at Risk - VaR
Example 2-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 (2.0 hours)
- 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
The Energy Forecasting Toolbox (2 hours)
- Historical Trend Analysis
- Univariate Time Series
- Multivariate Time Series
- Econometric Models
- Bayesian Estimation
- End-Use Models
- Engineering or Process Models
- Optimization
- Network Models
- Simulation
- Game Theory
- Scenarios
- Surveys
DAY TWO:
Time Series Step-by-Step (2hours)
- Time Plots
- Adjusting for Stationarity
- Logarithmic Transformation
- Differencing
- Correlation and Partial Correlation Functions
- Model Specification and Selection
- ARMA Models
- Estimated Parameters and Standard Errors
- Testing for White Noise
- Heteroskedasticity
- Autocorrelation
- Forecasting Future Values
- Additional Seasonality Considerations
Example 1-Statistical Reports that everyone can understand
Introduction to Real Options Analysis (2 hours)
Details of Option Model Implementation
Black-Scholes, Binomial Trees, and GARCH Models
Application: Real Option Valuation
Example of Valuing The Option of Real-Time Forward Load Reduction
Estimating Volatility and Uncertainty In Historical Prices
Measuring Forward Volatility
Adapting Value-at-Risk (VaR) for the Energy Industry
Application: Optimal Hedging using Statistical Triggers
Application: Minimizing Price Risk through Operational Design Flexibility
Example 1- Valuing Combustion Turbines using Real Options
Example 2- Valuing Gas Storage using Real Options
>>Register | 
