Fenix - Framework for Network Co-Simulation
Any co-simulation framework must manage the exchange of information as well as the synchronization of the clocks between simulations. The Fenix (Trademark) framework for network co-simulation addresses the drawbacks of other known co-simulation frameworks. Specifically, Fenix (Trademark) provides a capability allowing all simulations to speculate whether they can forego time synchronization for a certain period without loss of simulation accuracy. Further, Fenix (Trademark) allows speculative multithreading at runtime, without compiler assistance. This technique has not yet been applied as a means of accelerating co-simulations. Besides speculation, Fenix (Trademark) also provides novel time synchronization algorithms which take into account network traffic delays if a network simulator is part of the co-simulation.
GridOPTICS Software System (GOSS) Architecture for the Power Grid
GridOPTICS Software Framework is a flexible software system for ingesting data from sensors in the field, storing configuration and model data; and providing the data to analytics applications in a flexible and easy to use manner. The output of the analytics applications can be stored together with the data that was used to create the outputs.
Frequency Response Tool
Frequency response tool automates the power system frequency response analysis process. The tool can help to perform the estimation of the frequency response characteristics based on the phasor measurement unit (PMU) measurements, collect and process statistical information on the frequency response characteristics. The tool can also represent this information using advanced visualization techniques.
Development of cost performance model for redox flow battery
The software uses electrochemical performance modeling, auxiliary losses such as pumpoing and shunt current losses, a bottom up cost model. Optimization is done with respec to flow rate to get the highest efficiency. Calculations are done at constant power as opposed to constant current. Various stack areas are investigated, with each area corresponding to diffent power density required to meet the power requirement. At a power density corresponding to a fixed stack area, differnt combinations of current density and stack voltage are possible. The current density during discharge at low SOC fixes the upper limit of current density for a fixed stack area. Varying the flow rate and the dimensions of the manifold to each stack and dimensions of the flow channels within each cell determine losses. An effective voltage for each SOC is determined after losses are accounted for. The stack costs are determined by the stack area, while energy costs by the effective voltage. Determining the costs for various stack areas guide the optimization of stack and energy costs. Balance of Plant optimization of pump sizing is done taking into pump costs as function of pumping power and the flow rate effect on electrochemical efficiency and pumping losses. The model optimizes the system for vaious power to energy ratio and various chemistries. The tool developed is also able to do spline interpolation to get polarization curves from input data. The one of a kind tool allows user interaction.