3-Day In-Depth Practical Statistical Analysis for Energy and Power Markets Course (Houston, USA – December 7-9, 2022) – ResearchAndMarkets.com


DUBLIN–(BUSINESS WIRE)–The “In Depth: Practical Statistical Analysis for Energy and Power Markets” training has been added to from ResearchAndMarkets.com offer.

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

Find out why businesses continue to face significant energy and electricity price risk, and how risk and value are properly quantified. Energy and power companies around the world depend on accurate information about the risks and opportunities facing day-to-day decisions. Statistical analysis is often misapplied and many companies find that “a little knowledge is a dangerous thing”.

This comprehensive three-day program is designed to provide a solid understanding of the main statistical and analytical tools used in the energy and electricity markets. Through a combination of lectures and hands-on exercises that you will perform using your own laptop, attendees will learn and practice key energy applications of statistical modeling. Be armed with the tools and methods to properly analyze and measure data to reduce risk and increase revenue for your organization.

Who should attend:

Among those who will benefit from this seminar are energy and electricity executives; Lawyers; government regulators; merchants and business support staff; marketing, sales, purchasing and risk management personnel; accountants and auditors; plant operators; engineers; and business planners. The types of businesses that typically participate in this program include energy producers and distributors; public services ; banks and financial institutions; industrial enterprises; accounting, consulting and legal firms; municipal utilities; government regulators and electric generators.

What you will learn

  • Correlation and regression analysis; real options analysis; the Black-Scholes option pricing model; binomial trees; GARCH models; measurement of energy price risk; and how to use correlation and regression analysis to maintain a competitive advantage.

  • The lab exercises will allow you to create forecasting 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; measuring futures price volatility and adapting Value-at-Risk (VaR) concepts for the energy industry.

  • The workshop exercises will allow you to create VaR models, calculate volatility and simulate complex energy projects.

  • Use real-world case studies to examine 1) how Monte Carlo simulation is used to evaluate renewable energy, demand response programs, and energy storage projects; 2) benchmarking techniques used to estimate incremental cost savings from expanding existing operations, and 3) the actual option value of generation assets and power purchase agreements.

  • The workshop’s real world problems and case studies will examine the most frequently used applications and statistical tools in the energy industry.

  • Learn the four manage statistical measures.

Main topics covered:


The basics of deterministic thinking versus probabilistic thinking for energy applications

  • Data Science Basics – Information from Data

  • Descriptive statistics, means, standard deviations, forms of distribution

  • Frequency distributions and confidence intervals

  • Implications of empirical rule, transformations and probabilities

Fundamental modeling tools and simulation

  • Exercise: Setting up a Monte Carlo simulation to assess the value and risks of a project

Application: Calculation of Value at Risk (VaR)

  • The linear method and

  • The quadratic method

  • Historical simulation method

  • Monte Carlo method

  • Exercise: Calculating VaR using three different methods

Application: Energy Exposure Coverage

  • Understanding the “Greeks”

  • How and when to cover

  • delta coverage

  • Dynamic coverage

  • Gamma coverage

Application: Component Risk Analysis

  • Gain charts

  • VaR diagram of the portfolio

  • CAPM, RAROC and the Sharp ratio

  • Calculation of the charge according to the supply risk

  • Layered coverage using statistical triggers

  • Exercise: Customer Migration Model Estimating Migration Out of Standard Offering Service

  • Exercise: Measure load against supply risk

  • Exercise: Measuring the risk of intermittent renewable energy supply

Correlation and regression analysis to maintain competitive advantage

  • Univariate and multivariate analysis

  • Hypothesis testing

  • Means and variances equality test

  • Control boards


The Energy Forecasting Toolkit

  • Analysis of historical trends

  • Univariate time series

  • Multivariate time series

  • Econometric models

  • Bayesian estimation

  • End-use models

  • Engineering or process models

  • Optimization

  • Network models

  • Simulation

  • game theory

  • Scenarios

  • Investigations

  • Case Study: Statistical Reports Anyone Can Understand

  • Case Study: Benchmarking Against Industry Standards – GTS Steel vs. KCPL

  • Exercise: Building Regressions and Predictions, PDFs, CDFs, and Gain Plots

  • Exercise: Calculation of coverage ratios, construction of an energy coverage and a climate coverage

  • Exercise: Using forecasts in the Monte Carlo simulation to calculate the risk premium

Day three:

Introduction to Real Options Analysis

  • Option Model Implementation Details

  • Real Options and Net Present Value (NPV) Analysis

  • Estimation of historical price volatility and uncertainty

  • 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 Smart Grid

  • Exercise: Calculating volatility

  • Exercise: Simulating Prices Using GBM and Mean Reversion Monte Carlo Models

  • Exercise: Evaluate combustion turbines using real options

  • Exercise: Evaluate gas storage using real options


Kenneth Skinner

VP and COO

Integral analysis

Kenneth Skinner, Ph.D. is Vice President of Risk and Valuation Products for Integral Analytics, an analytical and management software consulting firm focused on operational, planning and market research solutions.

Dr Skinner has over 20 years of risk assessment and measurement experience, having worked as an energy consultant at PHB Hagler Bailly and Financial Times (FT) Energy, and as a derivative products for retail energy supplier Sempra Energy Solutions. He has his doctorate. from the Colorado School of Mines, in mineral economics, with a specialization in operations research, an MBA from Regis University and his BS in engineering from Létourneau University.

Dr. Skinner is a nationally recognized expert in the economic valuation and modeling of energy assets, including energy storage, distribution and generation, efficiency and demand response, alternatives renewable energy sources, financial derivatives and structured contracts using net present value, econometric and statistical methods, optimization principles. , and real options valuation techniques.

Dr. Skinner is currently a technology columnist for the Wiley Natural Gas and Electricity Journal and is a renowned 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 on this training, visit https://www.researchandmarkets.com/r/kf5i9i


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