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The global increase in energy consumption and demand has forced manycountries to transition into including more diverse energy sources in theirelectricity market. To efficiently utilize the available fuel resources, allenergy sources must be optimized simultaneously. However, the inherentvariability in variable renewable energy generators makes deterministic modelsineffective. On the other hand, comprehensive stochastic models, including allsources of generation across a nation, can become computationally intractable.This work proposes a comprehensive national energy planning framework from apolicymaker's perspective, which is generalizable to any country, region, orany group of countries in energy trade agreements. Given its relative land areaand energy consumption globally, the United States is selected as a case study.A two-stage stochastic programming approach is adopted, and a scenario-basedBenders decomposition modeling approach is employed to achieve computationalefficiency for the large-scale model. Data is obtained from the U.S. EnergyInformation Administration's online data collection. Scenarios for theuncertain parameters are developed using a k-means clustering algorithm.Various cases are compared from a financial perspective to inform policymakingby identifying implementable cost-reduction strategies. The findings underscorethe importance of promoting resource coordination and demonstrate the impact ofincreasing interchange and renewable energy capacity on overall gains.Moreover, the study highlights the value of stochastic optimization modelingcompared to deterministic modeling to make energy planning decisions.

A Nationwide Multi-Location Multi-Resource Stochastic Programming Based Energy Planning Framework