In this dissertation we develop a practical theory of micro-solar power systems that is materialized in a simulation suite that models component and system behavior over a long time-scale and in an external environment that depends on time, location, weather and local conditions. This simulation provides sufficient accuracy to guide specific design choices in a large design space. This design tool is very different from the many "macro-solar" calculators, which model typical behavior of kilowatt systems in the best conditions, rather than detailed behavior of milliwatt systems in the worst conditions. We provide a general architecture of micro-solar power systems, comprising key components and interconnections among the components, and formalize each component in an analytical or empirical model of its behavior. We incorporate these component models and their interconnections in the simulation suite.
Our discrete time-event simulation models the daily behavior and the long-term behavior by iteratively evaluating the state of the system in the context of its solar environment and internal loads. To model the variability of solar energy, it provides three solar radiation models depending on the degree of information available: an astronomical model for ideal conditions, an obstructed astronomical model for estimating solar radiation under the presence of shadows and obstructions, and a long-term measurement model for estimating solar radiation under weather variation. Our simulation suite is validated with a concrete design: the HydroWatch node which is a well-engineered climate monitoring node and network with a flexible power subsystem. A HydroWatch node is able to support various specific design points and provides visibility into solar performance in a real application setting.
With our simulation suite, micro-solar power systems can be designed in a systematic fashion. Putting the model and empirical vehicle together, the design choices in each component of a micro-solar power system are studied to reach a deployable candidate. The deployment is evaluated by analyzing the effects of different solar profiles across the network. The analysis from the deployment can be used to refine the next system-design iteration.