Federal and Not-for-Profit Grant Projects
The Center for Integrated Electric Energy Systems is engaged in a variety of energy projects with our federal and not-for-profit partners, including the US Navy Office of Naval Research, US Department of Energy, and National Science Foundation.
View these projects below.
Principle Investigator: Dr. Vyacheslav Solovyov
Funding Agency: US Navy Office of Naval Research
The project addresses the need for effective management of high-voltage supercapacitive energy storage that would significantly improve resilience in a high-voltage microgrid subjected to pulsed loads and disruptions. The project will advance the high-voltage supercapacitor storage as a part of Fully Integrated Power and Energy Systems (IPES) of the near-future NAVY grids. The SBU team will work with the leading manufacturer of the state-of-the-art high voltage supercapacitor storage, Ioxus Inc. and the energy storage integrator, Unique Technical Services LLC (UTS), to evaluate the feasibility of achieving the NAVY target of 1 MW/m3 power density in the Energy Storage Cabinet geometry using Ioxus iMOD-series product.
Application of high-volume supercapacitor in microgridPrinciple Investigator: Dr. Benjamin Hsiao
Funding Agency: US Navy Office of Naval ResearchRemote military bases or communities can have a wide range of systems, spanning from the kW to GW, with different complexity and maturity. There is a lack of integrated "plug-and-play" mobile energy storage solutions that can be seamlessly integrated into a microgrid without compromising the grid power quality.
The popular microgrid management approach is droop control-based; the strategy inherited from the traditional electric grid, where stability is ensured by multiple synchronous generators operating at a 4-5% frequency droop. Absent inertia of large generators in an islanded microgrid requires intermediate energy storage systems to absorb or inject power through pre-determined or adaptably adjusted droop curves. However, under large load variations, such as shipboard electromagnetic weapon platforms and high-power electric vehicle chargers on forward bases, droop controllers are known to demonstrate poor damping performance.
Layout of the microgrid prototype. The arrows show dispatch communications. The left panel shows the BRENERGY 4850 Li-ion battery used in this study.The project uses a communication-based multi-agent approach using secure wired communication, CAN and RS-485. By inter-connecting the loads and generators within the microgrid, optimal energy dispatch can be realized through a look-ahead load prediction strategy. Under this scenario, a load controller will communicate the anticipated energy dispatch to the distributed energy resource (DER) and the energy storage unit. Here we use the most advanced in the internet of things (IoT) communication to demonstrate predictive energy dispatch of a Mil-spec energy storage (Bren-Tronics BRENERGY TM 4850 Li-ion storage), commercial out-of-shelf (COTS) inverter, under a variety of loads. The proposal team fully utilizes synergy between SBU's expertise in wireless communication and Bren-Tronics experience in supporting US warfighters with state-of-the-art energy solutions.
Principle Investigator: Dr. Peng Zhang
Funding Agency: US Department of Energy
This work will help communities maintain power during man-made or natural disasters and restore power after them, improve cybersecurity for PV inverters and power systems, and develop advanced hybrid plants that operate collaboratively with other resources for improved reliability and resilience. It will advance grid operations technologies and enable solar to provide more grid services—or enable grid operators to maintain system‐wide balance and manage electricity transmission. In addition, it will advance the cybersecurity of solar technologies to better detect disturbances and develop strategies to survive a cyberattack.
Principle Investigator: Dr. Peng Zhang
Funding Agency: US Navy Office of Naval ResearchThis project develops a deployable Three Lines of Defense model that integrates three of SBU's unique techniques – programmable active security scanning, encrypted control, software-defined microgrid controls (including push-sum-enabled distributed algorithms to enable unprecedentedly self-protecting, ultra-cyber-physical-resilient, and cognitive microgrids.
Principle Investigator: Dr. Fang Luo
Funding Agency: Board of Trustees of the University of Ilinois
Cryogenic testing of GaN modulesThe project will develop a cryogenic hydrogen fuel cell system for powering all-electric aircraft. The team will investigate the technology needed to produce a practical all-electric design to replace conventional fossil fuel propulsion systems.
Principle Investigator: Dr. Fang Luo
Funding Agency: National Science Foundation
The project provides a disruptive semiconductor-based active filtering method which could essentially replace passive filtering solutions that have been used for decades. The success of this project will not only significantly improve the power density and efficiency of future power electronic systems, but also change the design philosophy for electromagnetic emission mitigation in such systems. The proposed program integrates cutting-edge research with education, and thus, provides a platform to integrate STEM interdisciplinary knowledge together with hands-on activities with the focus on establishing a pipeline of STEM students in electrical engineering from pre-college to graduate level.
Principle Investigator: Dr. Yue Zhao
Funding Agency: National Science FoundationThis project will develop new machine learning algorithms, both leveraging and integrating with existing computational tools, to greatly improve the computational efficiency of solving challenging power system operation problems. We accomplish this by designing algorithms that use data to replace some of the existing heuristics based on human experience. We use a bottom-up approach by carefully formulating the problems to determine the best interface between the physical system and machine learning. This allows us to design algorithms that are aware of the physics of the problems and complement existing tools in the field.
Principle Investigator: Dr. Yue Zhao
Funding Agency: NYS Energy Research and Development Authority
The projects will develop an integrated suite of grid modernization metrics that leverage current industry practice and emerging industry additions (e.g. extreme event metrics from NERC) to develop new metrics that reflect emerging grid attributes and architectures, conduct baseline modernization assessments and provide an ongoing dashboard for policy makers, regulators and industry stakeholders.
Dr. Yue Zhao and his teamPrinciple Investigator: Dr. Yue Zhao
Funding Agency: National Science FoundationThe innovation of the project lies in integrating Internet of Things technologies, software-defined networking and real-time computing to establish a scalable SD2N architecture.
Principle Investigator: Dr. Peng Zhang
Funding Agency: National Science FoundationThe research project creates and implements networked microgrids solutions on a novel cyberinfrastructure to ensure distribution grid resiliency. This cyber infrastructure is based on Software-Defined Networking. Specifically, the project has three main objectives: (1) To establish a formal analysis method to tractably assess networked microgrid stability; (2) To devise a new concept of microgrid active fault management (AFM) enabled through online distributed optimization; and (3) To build a Software-Defined Networking (SDN) based architecture to enable highly resilient networked microgrids.
Principle Investigator: Dr. Peng Zhang
Funding Agency: National Science FoundationThe main objective of this project is to create smart programmable microgrids (SPMs). Our key innovation is to virtualize microgrid functions, making them software-defined and hardware-independent, so that converting distributed energy resources (DERs)to community microgrids becomes affordable, autonomic, and secure. To achieve our main objective, our team will: 1) Architect a programmable microgrid platform for virtualizing traditionally hardware-dependent microgrid functions as flexible software services, fully resolving hardware dependence issues and enabling unprecedented low costs; 2) Pioneer a concept of software-defined operation optimization for microgrids, where operation objectives, grid connection, and DER participations will be defined by software and plug-and-play, and can be quickly reconfigured, based on the development of modularized and tightened models and a novel asynchronous price-based decomposition-and-coordination method; 3) Devise a software-defined distributed formal analysis for online stability assessment under heterogeneous uncertainties and plug-and-play of microgrid components or microgrids; 4) Develop a real-time-learning-based cybersecurity function to protect SPMs against power bot attacks; and 5) Enable anaerobic-biomass-digesters (ADs) as environmentally friendly and dispatchable DERs by virtualizing the dispatch and control of ADs in SPM. The proposed SPM will be demonstrated on a Connecticut community microgrid through a recently built cyber-physical testbed.
Principle Investigator: Dr. Yue Zhao
Funding Agency: National Science FoundationThis project will develop new machine learning algorithms, both leveraging and integrating with existing computational tools, to greatly improve the computational efficiency of solving challenging power system operation problems. We accomplish this by designing algorithms that use data to replace some of the existing heuristics based on the human experience. We use a bottom-up approach by carefully formulating the problems to determine the best interface between the physical system and machine learning. This allows us to design algorithms that are aware of the physics of the problems and complement existing tools in the field
Principle Investigator: Dr. Peng Zhang
Funding Agency: National Science FoundationThe innovation of the project lies in integrating Internet of Things technologies, software-defined networking and real-time computing to establish a scalable SD2N architecture.