A renewable or “clean” energy system pays back the user in three ways. First, it typically avoids the use of hydrocarbon fuel, so for every kilowatt-hour or BTU that it produces it displaces or avoids a quantity of CO2 emission due to combustion of hydrocarbon. Second, the system requires an energy investment during its manufacture, so the embodied energy is paid back over its life cycle, and this aspect of renewable energy systems is often analyzed in standardized life cycle analyses. Third, the system represents a financial investment that should be preferably paid back before the end of the system life in order for the investment to be profitable. Deterministic assessments may inaccurately assess variables that can affect the ROI in any of these three categories, such as resource availability, equipment reliability or failure, and efficiency factors. Probabilistic modeling, on the other hand, can account for some of this uncertainty and reflect the uncertainty in the output. Use of this modeling technique will be demonstrated via examples to show how feasibility or ROI projections can be augmented with the use of probabilistic models.

This content is only available via PDF.
You do not currently have access to this content.