demands. Possibilities are being explored globally to leverage these smart meter capabilities for

demand side management (DSM), particularly to advance rural electrification in emerging contexts.

Simulating different microgrid configurations and operating strategies before implementing

them is valuable for providing reliable service to customers and keeping expenses low. This

dissertation focuses on the design, development, and implementation of a simulation tool that

quantitatively compares microgrid operating strategies and sizing options. To this end, the toolâ€™s

pre-processing engine accepts arbitrary parameters for probability distributions to characterize

loads and nondispatchable supplies (e.g., wind and PV). This dissertation presents methods to

compute probability distributions for the aggregate system demand from individual load distributions

that characterize each consumer. Further computational methods are given to derive

probabilistic estimates of aggregate loads reduced by DSM. These probability underpinnings

create effective system-level models for simulation studies of energy management schemes. The

models of aggregate load behavior and probabilistic supply are used in a simulation model that

includes dispatchable generation and storage components to perform Monte Carlo simulation

studies.

This dissertation describes the rationale and modeling parameters for different components

in the simulation tool. The tool allows different rule-based energy management strategies to

be implemented and compared, with certain supplies and storage options being controllable

while others are driven by external factors. To account for the wide range of possible outcomes

that occur in a real-world system, the tool is fundamentally probabilistic and runs its

MATLAB/Simulink-based microgrid model with Monte Carlo methods. The loads can be designated

Markovian or independent-in-time. The Energy Manager makes dispatch decisions with

limited knowledge of the rest of the system, similar to controllers in real-world microgrids. The

Energy Manager also limits certain loads with DSM to meet system goals, e.g., to reduce the

incidence of power cuts or limit fuel-burning generation. The Energy Manager can prioritize

renewable generation, energy storage, etc. as desired by the microgrid operators. To demonstrate

the simulation tool, this dissertation concludes with case studies based on a microgrid in Rwanda. The case studies provide examples of how smart meters, which are able to control

residential demand, can benefit microgrid operations. The deployment of DSM strategies using

smart meters is shown to reduce the occurrence and duration of power cuts when system

demand exceeds the total available supply.