Grid

Arizona State University (ASU) will develop a stochastic optimal power flow (SOPF) framework, which would integrate uncertainty from renewable resources, load, distributed storage, and demand response technologies into bulk power system management in a holistic manner. The team will develop SOPF algorithms for the security-constrained economic dispatch (SCED) problem used to manage variability in the electric grid.

General Electric (GE) Global Research along with its partners will develop a novel distributed flexibility resource (DFR) technology that aggregates responsive flexible loads and DERs to provide synthetic reserve services to the grid while maintaining customer quality-of-service. A key innovation of the project is to develop a forecast tool that will use short-term and real-time weather forecasts along with other data to estimate the reserve potential of aggregate loads and DERs.

Eaton will develop and validate a disruptive cloud-computing-based technology aimed at providing agile and robust synthetic regulating reserve services to the power grid. This approach separates the decision-making of synthetic regulating reserve services into two-levels to significantly reduce the computational complexity, thereby enabling large-scale coordinated control of a vast number of DERs and flexible load.

DNV GL together with its partners, Geli and Group NIRE, will develop an Internet of Energy (IoEn) platform for the automated scheduling, aggregation, dispatch, and performance validation of network optimized DERs and controllable loads. The IoEn platform will simultaneously manage both system-level regulation and distribution-level support functions to facilitate large-scale integration of distributed generation onto the grid.

Sandia National Laboratories will develop a new type of switch, a 100kV optically controlled switch (often called photoconductive semiconductor switch or PCSS), based on the WBG semiconductors GaN and AlGaN. The capabilities of the PCSS will be demonstrated in high-voltage circuits for medium and high voltage direct current (MVDC/HVDC) power conversion for grid applications. Photoconductivity is the measure of a material's response to the energy inherent in light radiation. The electrical conductivity of a photoconductive material increases when it absorbs light.

New York University (NYU) will develop an observational platform to remotely reveal energy usage patterns of New York City using synoptic imaging of the urban skyline. The electrical grid of the future will be a complex collection of traditional centralized power generation, distributed energy resources, and emerging renewable energy technologies. Advanced energy consumption data is required to design and optimize our future grid. At present, the costly and time-consuming installation of smart meters is the only way to obtain this level of building energy information.

Harvard University in partnership with Sandia National Laboratories will develop a transistor-less 16kW DC to DC converter boosting a 0.5kV DC input to 8kV that is scalable to 100kW. If successful, the transistor-less DC to DC converter could improve the performance of power electronics for electric vehicles, commercial power supplies, renewable energy systems, grid operations, and other applications. Converting DC to DC is a two-step process that traditionally uses fast-switching transistors to convert a DC input to an AC signal before the signal is rectified to a DC output.

Bigwood Systems is developing a comprehensive Optimal Power Flow (OPF) modelling engine that will enhance the energy efficiency, stability, and cost effectiveness of the national electric grid. Like water flowing down a hill, electricity takes the path of least resistance which depends on the grid network topology and on grid controls. However, in a complicated networked environment, this can easily lead to costly congestion or shortages in certain areas of the electric grid.

The University of Wisconsin-Madison (UW-Madison) and its partners will develop realistic transmission system models and scenarios that will serve as test cases to reduce barriers to the development and adoption of new technologies in grid optimization and control. The EPIGRIDS project aims to construct realistic grid models by using software to emulate the transmission and generation expansion decision processes used by utility planners.

The University of Illinois, Urbana-Champaign (UIUC), with partners from Cornell University, Virginia Commonwealth University, and Arizona State University will develop a set of entirely synthetic electric transmission system models. Their 10 open-source system models and associated scenarios will match the complexity of the actual power grid. By utilizing statistics derived from real data, the team’s models will have coordinates based on North American geography with network structure, characteristics, and consumer demand that mimics real grid profiles.