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 covered 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 
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