Energy Risk Modeling

Energy Risk Modeling

by Roy Nersesian

Growing out of Roy Nersesian’s energy courses he delivers at the School of International and Public Affairs at Columbia University, Energy Risk Modeling is a new, ground-breaking reference for those looking for simulation, decision trees, and optimization techniques for energy applications. The book is loaded with real-life examples that demonstrate how @RISK, PrecisionTree, and Evolver can be used to make better financial decisions within the oil and gas, electricity, and renewable energy industries. Roy Nersesian’s easy-to-read, step-by-step approach makes his techniques accessible to anyone who uses Microsoft Excel. All examples covered in the book are provided in Excel spreadsheets.

Topics include:

• Modeling payoffs of oil drilling using both PrecisionTree and @RISK
• Economic analysis of a Liquefied Natural Gas (LNG) export project where uncertain variables (cost of natural gas extraction, cost of liquefaction, cost of transportation, and the price of LNG in the intended foreign market) are modeled and simulated in @RISK.
• Optimizing an oil refinery to maximize profits and valuing a real option of purchasing a coal-fired plant using Evolver, shown to have a greater predictive efficacy than Excel’s built-in Solver.
• Modeling solar panel and wind turbine power outputs by factoring cloud cover, temperature, time of day, and wind speed, respectively, while optimizing said uncontrollable energy sources with uncontrollable demand to closely match daily energy demand with power generated.
• Projecting hydropower output in terms of percent capacity using rainfall, evaporation, and damn leakage as probabilistic variables.
• Selecting the preferable biofuel project given that one project has both a higher return and higher risk profile than the other.
• Modeling daily electricity demand amidst uncertainty in the deregulation, time of year and day.
• Selecting which kind of energy plant to meet incremental demand based on modeling of capacity, costs, energy output
  of each source, and taxes and capital recovery factor.
• Projecting oil consumption with trend/regression analysis and oil prices under conditions of uncertainty.
• Managing the risk of energy portfolios with swaps, trailing stops, and puts and calls.
• Optimizing the structure of a loan used to finance the development of an oil field where the loan has no recourse to the other parties.
• Designing a royalty, ownership, and tax regime between two oil companies jointly developing an oil field.
  

Companion volume: Utility Risk Modeling

Utility Risk Modeling is an update to Energy Risk Modeling, focusing only on renewables and electricity storage. Energy Risk Modeling and Utility Risk Modeling should be viewed as a single endeavor and as reference for simulation, decision trees, and optimization techniques for energy applications. This book has many real-life examples that demonstrate how to make better financial decisions within the renewable energy industry using @RISK, PrecisionTree, and Evolver. Roy Nersesian’s easy-to-read, step-by-step approach makes his techniques accessible to anyone who uses Microsoft Excel.

See the companion volume Utility Risk Modeling

Author Roy Nersesian

Roy Nersesian is a professor in the School of Business at Monmouth University and holds a BS in physics from Rensselaer Polytechnic Institute, followed by 8 years in the U.S. Navy with his last position as Chief Engineer on a Polaris submarine.

He graduated from Harvard Business School with an MBA and worked in various facets of the shipping industry. He began his academic career in 1985 at Monmouth focusing on operations management with an elective in renewable energy. He also taught as an adjunct at SIPA, Columbia University in marine transportation and energy modeling using @RISK as a software platform.

Professor Nersesian has written several books, with three in energy. The latest version is Energy Economics published by Routledge, 2016.

Palisade Books