A Tool for Probabilistic Evaluation of Microgrid Operating Strategies with Demand Side Management

Smart meters in microgrids enable fine-grained monitoring and control of individual building
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 tool’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.