Estimating Reliability of a System of Electric Generators Using Stochastic Integration of Renewable Energy Technologies (SIRET) in the Renewable Energy Optimization (REO) Method

The Renewable Energy Optimization (REO) screening method identifies the optimal combination of renewable energy technologies for a site subject to constraints imposed by the organization or by the physical limits of the site. The REO method calculates the life-cycle cost of a combination of renewable energy generators and then uses a solver to iterate until it finds the size of each generator that minimizes life-cycle cost. In 2007, NREL developed a method called “Stochastic Integration of Renewable Energy Technologies” (SIRET) [1, 2, 3, 7] to account for the interactions between multiple generators serving a load. SIRET calculates the quantity of electricity bought from and sold to the utility within a time interval, which allows an accurate economic model when the retail rate and sell-back rate of electricity differ. These two energy quantities calculated by SIRET are used to calculate utility costs in the REO life-cycle cost calculation. When one or more emergency generators and/or the electric grid are included with renewables as generation resources in the analysis, the SIRET algorithm can also be used to estimate the reliability of the system, where reliability is defined as the fraction of time in which any combination of generation resources is sufficient to meet the load. The SIRET algorithm is capable of analyzing any number of generation resources, and currently includes photovoltaics (PV), wind, solar thermal electric, biomass gasification, anaerobic digestion, landfill gas, daylighting, and an arbitrary number of emergency generators as well as the electric grid. An example consisting of PV, wind, and an emergency generator is presented to illustrate the method. This method is based on very simple and heuristic methods, and thus should be regarded only as a relative indicator of reliability, rather than an accurate prediction, but agreement with hourly simulation is presented as one benchmark of the effectiveness of the method.