Full Course Description

KS102
Intermediate Applications of Energy Statistics

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. Building on the introductory statistical concepts, this course is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets by examining several applications of statistical analysis. Be armed with the tools and methods needed to properly analyze and measure data to reduce risk and increase earnings for your organization. This course is an intermediate level course covering several applications and prepares the participant for the application specific advanced courses offered in this series.

Learn These Keys to Success:

1.   Using real option analysis, the Black-Scholes option pricing model and binomial trees for valuing commodity
       dependent assets, and GARCH models to measure energy price risk.
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.   How Monte Carlo simulation is used to value Demand Response programs; benchmarking techniques used for
       estimating the incremental cost savings of expanding existing operations; and the real-option value of generation
       assets.

Seminar Agenda

• Statistical Reports that everyone can understand

- Using charts to create information from data
- Benchmarking to Industry Standards

1.   Application: Comparing O&M Expenditure to that of Peer Facilities
2.   Application: Estimating the "Economies of Scale" (marginal cost reduction) Associated with Multiple Unit
       Generation Facilities

• Measuring Forward Volatility in Commodity Markets

- Adapting Value-at-Risk (VaR) and Hedging Commodity Price Risk for the Energy Industry

1.   Application: Calculating Value-at-Risk (VaR)
2.   Application: Optimal Hedging using Statistical Triggers
3.   Application: Minimizing Price Risk through Operational Design Flexibility

• Introduction to Real Options Analysis

-   Details of Option Model Implementation
-   Estimating Volatility and Uncertainty in Historical Prices
-   Building pricing models

1.   Geometric Brownian Motion
2.   Mean Reversion Jump Diffusion
3.   Black-Scholes, Binomial Trees, and GARCH Models

- Building the Engineering Valuation (Option) model
- Applications in Real Option Valuation

1.   Application: Valuing The Option of Real-Time Forward Load Reduction
2.   Application: Valuing Combustion Turbines as a Real Options
3.   Application: Valuing the Injection and Withdraw Opportunities of Gas Storage

>>Registration