Status on the Development of a Modeling and Simulation Framework for the Economic Assessment of Nuclear Hybrid Energy

Continued effort to design and build a modeling and simulation framework to assess the economic viability of Nuclear Hybrid Energy Systems (NHES) was undertaken in fiscal year (FY) 2016. The purpose of this report is to document the various tasks associated with the development of such a framework and to provide a status of their progress. Several tasks have been accomplished. First, a synthetic time history generator has been developed in RAVEN, which consists of Fourier series and autoregressive moving average model. The former is used to capture the seasonal trend in historical data, while the latter is to characterize the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). As demonstration, both synthetic wind speed and grid demand are generated, showing matching statistics with database. In order to build a design and operations optimizer in RAVEN, a new type of sampler has been developed with highly object-oriented design. In particular, simultaneous perturbation stochastic approximation algorithm is implemented. The optimizer is capable to drive the model to optimize a scalar objective function without constraint in the input space, while the constraints handling is a work in progress and will be implemented to improve the optimization capability. Furthermore, a simplified cash flow model of the performance of an NHES in the electric market has been developed in Python and used as external model in RAVEN to confirm expectations on the analysis capability of RAVEN to provide insight into system economics and to test the capability of RAVEN to identify limit surfaces. Finally, an example calculation is performed that shows the integration and proper data passing in RAVEN of the synthetic time history generator, the cash flow model and the optimizer. It has been shown that the developed Python models external to RAVEN are able to communicate with RAVEN and each other through the newly developed RAVEN capability called "EnsembleModel".