3 Day Practical Statistical Analysis for the Energy & Power Markets Course (Houston, United States - October 12-14, 2022)

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Sep 09, 2022, 15:15 ET

DUBLIN, Sept. 9, 2022 /PRNewswire/ -- The "In-Depth: Practical Statistical Analysis for the Energy & Power Markets" training has been added to ResearchAndMarkets.com's offering.

This course adds a third day to the popular Energy Statistical Analysis seminar to allow the time needed for a more in-depth discussion and explanation of many important topics. Additionally, this three-day course is designed as a hand-on workshop. Not only will you learn about practical energy statistical techniques and tools, but you will practice building statistical models in a workshop format.

Learn why companies continue to be exposed to significant energy and electricity related price risk, and how risk and value are 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."

This comprehensive three-day program is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets. Through a combination of lecture and hands-on exercises that you will complete using your own laptop, participants will learn and practice key energy applications of statistical modeling. Be armed with the tools and methods needed to properly analyze and measure data to reduce risk and increase earnings for your organization.

Who Should Attend:

Among those who will benefit from this seminar include energy and electric power executives; attorneys; government regulators; traders & trading support staff; marketing, sales, purchasing & risk management personnel; accountants & auditors; plant operators; engineers; and corporate planners. Types of companies that typically attend this program include energy producers and marketers; utilities; banks & financial houses; industrial companies; accounting, consulting & law firms; municipal utilities; government regulators, and electric generators.

What You Will Learn

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.
Workshop exercises will have you building forecast models including time series and financial engineering price models including Geometric Brownian Motion and Mean Reversion Jump Diffusion.
How to minimize price risk through operational design flexibility; measure forward price volatility and adapt Value-at-Risk concepts (VaR) for the Energy Industry.
Workshop exercises will have you building VaR models, calculating volatility, and simulating complex energy projects.
Use actual case studies to examine 1) how Monte Carlo simulation is used to value renewable energy, demand response programs, and energy storage projects; 2) bench-marking techniques used for estimating the incremental cost savings of expanding existing operations, and 3) real-option value of generation assets and power purchase agreements.
Actual workshop problems and case studies will look at statistical applications and tools most frequently used in the energy industry.
Learn the four manage statistical metrics.

Key Topics Covered:

DAY ONE:

The Basics of Deterministic vs. Probabilistic Thinking for Energy Applications

Basics of data science - Information from Data
Descriptive Statistics, Means, Standard Deviations, Distribution Shapes
Frequency Distributions and Confidence Intervals
Implications of the Empirical Rule, Transformations and Probability

Fundamental Modeling Tools and Simulation

Exercise: Setting up a Monte Carlo Simulation to Evaluate Project Value and Risk

Application: Calculating Value at Risk (VaR)

The Linear Method and
The Quadratic Method
Historic Simulation Method
Monte Carlo Method
Exercise: Calculating VaR Using Three Different Methods

Application: Hedging Energy Exposure

Understanding the "Greeks"
How and when to Hedge
Delta Hedging
Dynamic Hedging
Gamma Hedging

Application: Component Risk Analysis

Payoff Diagrams
Portfolio VaR Diagram
CAPM, RAROC and the Sharp Ratio
Calculating Load Following Supply Risk
Layered Hedging using Statistical Triggers
Exercise: Customer Migration Model Estimating Migration out of Standard Offer Service
Exercise: Measuring Load Following Supply Risk
Exercise: Measuring Intermittent Renewable Supply Risk

Correlation and Regression Analysis for Maintaining the Competitive Edge

Univariate and Multivariate Analysis
Hypotheses Testing
Testing for Equal Means and Variances
Control Charts

DAY TWO:

The Energy Forecasting Toolbox

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
Case Study: Statistical Reports that Everyone Can Understand
Case Study: Benchmarking to Industry Standards- GTS Steel vs. KCPL
Exercise: Building Regressions and Forecasting, PDF's, CDF's and Payoff Diagrams
Exercise: Calculating Hedge Ratios, Constructing an Energy Hedge and a Weather Hedge
Exercise: Using Forecasts in Monte Carlo Simulation to Calculate Risk Premium

DAY Three:

Introduction to Real Options Analysis

Details of Option Model Implementation
Real Options and Net Present Value (NPV) Analysis
Estimating Volatility and Uncertainty In Historical Prices
Black-Scholes, Binomial Trees, and GARCH Models
Geometric Brownian Motion and Mean Reversion
Application: Minimizing Price Risk through Operational Design Flexibility
Application: Real Option Value of Demand Response and the Smart Grid
Exercise: Calculating Volatility
Exercise: Simulating Prices using GBM and Mean Reversion Monte Carlo Models
Exercise: Valuing Combustion Turbines using Real Options
Exercise: Valuing Gas Storage using Real Options

Key Topics Covered:

Kenneth Skinner
VP and Chief Operating Officer
Integral Analytics

Kenneth Skinner, Ph.D. is Vice President of Risk & Evaluation Products for Integral Analytics, an analytical software and management consulting firm focused on operational, planning, and market research solutions. Dr. Skinner has over 20 years' experience in evaluation and risk measurement, having worked as an energy consultant with PHB Hagler Bailly and Financial Times (FT) Energy, and as the Derivative Structuring Manager for the retail energy supplier Sempra Energy Solutions. He has his Ph.D. from Colorado School of Mines, in Mineral Economics, with an emphasis in Operations Research, an MBA from Regis University and his BS in Engineering from Letourneau University.

Dr. Skinner is a nationally recognized expert in economic evaluation and modeling of energy assets including energy storage, distribution and generation, efficiency and demand response, renewable energy alternatives, financial derivatives and structured contracts using net present value, econometric and statistical methods, optimization principles, and real option valuation techniques. Dr. Skinner is currently the technology columnist for Wiley Natural Gas and Electricity Journal and is a noted speaker on energy related topics for organizations such as AESP, IAEE, ACEEE, PLMA, IEPEC, INFORMS, Infocast, EUCI, SNL Energy and PGS Energy Training.

For more information about this training visit https://www.researchandmarkets.com/r/tf1ec7