  The global increase in energy consumption and demand has forced many
countries to transition into including more diverse energy sources in their
electricity market. To efficiently utilize the available fuel resources, all
energy sources must be optimized simultaneously. However, the inherent
variability in variable renewable energy generators makes deterministic models
ineffective. On the other hand, comprehensive stochastic models, including all
sources of generation across a nation, can become computationally intractable.
This work proposes a comprehensive national energy planning framework from a
policymaker's perspective, which is generalizable to any country, region, or
any group of countries in energy trade agreements. Given its relative land area
and energy consumption globally, the United States is selected as a case study.
A two-stage stochastic programming approach is adopted, and a scenario-based
Benders decomposition modeling approach is employed to achieve computational
efficiency for the large-scale model. Data is obtained from the U.S. Energy
Information Administration's online data collection. Scenarios for the
uncertain parameters are developed using a k-means clustering algorithm.
Various cases are compared from a financial perspective to inform policymaking
by identifying implementable cost-reduction strategies. The findings underscore
the importance of promoting resource coordination and demonstrate the impact of
increasing interchange and renewable energy capacity on overall gains.
Moreover, the study highlights the value of stochastic optimization modeling
compared to deterministic modeling to make energy planning decisions.
