| Summary: | <p>This dissertation studies two important models in the field of the distributed generation
technologies to provide resiliency to the electric power distribution system. In the first
part of the dissertation, we study the impact of assessing a Combined Cooling Heating
Power system (CCHP) on the optimization and management of an on-site energy system
under stochastic settings. These mathematical models propose a scalable stochastic decision
model for large-scale microgrid operation formulated as a two-stage stochastic linear
programming model. The model is solved enhanced algorithm strategies for Benders
decomposition are introduced to find an optimal solution for larger instances efficiently.
Some observations are made with different capacities of the power grid, dynamic pricing
mechanisms with various levels of uncertainty, and sizes of power generation units. In the
second part of the dissertation, we study a mathematical model that designs a Microgrid
(MG) that integrates conventional fuel based generating (FBG) units, renewable sources
of energy, distributed energy storage (DES) units, and electricity demand response. Curtailment
of renewable resources generation during the MG operation affects the long-term
revenues expected and increases the greenhouses emission. Considering the variability of
renewable resources, researchers should pay more attention to scalable stochastic models
for MG for multiple nodes. This study bridges the research gap by developing a scalable
chance-constrained two-stage stochastic program to ensure that a significant portion of the
renewable resource power output at each operating hour will be utilized. Finally, some
managerial insights are drawn into the operation performance of the Combined Cooling
Heating Power and a Microgrid.</p>
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