HVDC Distribution Study of Intelligent Power System

University of Dayton Research Institute

High Voltage Direct Current (HVDC) aviation electrical power systems (EPS) provide many advantages, particularly in the area of weight savings. Despite the advantages, there are technical challenges for these systems as the power and dynamic response demanded by high power and more-electric loads increases. High power HVDC systems require low source impedance which makes larger fault energy available to the system. In addition, flight and mission critical loads demand constant power and fast response by a tightly regulated EPS. These loads on a HVDC distribution can cause dynamically negative resistance resulting in poor power quality and/or loss of system stability.

AFRL’ s objective is to develop an intelligent power system to advance the state of the art in system efficiency and safety. This is a far-reaching and broad area of research that is best served by the participation of multiple research institutions that have developed expertise in specific areas. To that end, this Statement of Objectives outlines work where Michigan Technological University (MTU) has demonstrated outstanding research.
Specific areas of research that AFRL is interested in having MTU participate in this program are outlined below. The results of this research and development effort shall be available to all other parties collaborating on the AFRL Intelligent Power System Program as well as industry concerns involved with United States aviation power systems so that best practices and recommendations can be incorporated in future power system design concepts.

1.1 Analysis, Design, and Control of components (ns – ms level)
1.2 Distributed management/optimization of source and loads (ms – s level):
1.3 Mission level load planning(> 1 s level)
1.4 Energy Storage (ES) for pulsed power loads

Investigators: Wayne Weaver, Gordon Parker

Sumit Paudyal

Assistant Professor, Electrical and Computer Engineering
Areas of Interest

  • Distribution Grid Modeling and Optimization
  • Building-to-grid (B2G) and Vehicle-to-grid (V2G) Integration
  • Wide-area Control and Protection, Synchrophasor applications in Smart Grid
    Energy Hub, Distributed Energy Resources, Demand Response, and Demand Dispatch
    Distribution Level Energy Market
  • Kuilin Zhang


    Dr. Zhang received his Ph.D. degree in Transportation Systems Analysis and Planning from the Department of Civil and Environmental Engineering at Northwestern University in December 2009.  After working as a Postdoctoral Fellow in the Transportation Center at Northwestern, he joined the Energy Systems Division at Argonne National Laboratory as a Postdoctoral Appointee in November 2010. He is a member of Transportation Research Board (TRB) standing committees of Transportation Network Modeling (ADB30) and Freight Transportation Planning and Logistics (AT015). He is also a member of INFORMS.

    Research Interests

    • Vehicle Network Microgrids
    • Dynamic network equilibrium and optimization
    • Modeling and simulation of large-scale complex systems
    • Multimodal transportation systems analysis
    • Freight transportation and logistics systems
    • Data-driven travel behavior analysis
    • Impact of plug-in electric vehicles to smart grid and transportation network systems
    • Vehicle systems dynamics and fuel-efficient driving behavior

    Rush Robinett


    Dr. Robinett specializes in nonlinear control and optimal system design of energy, robotics, and aerospace systems.
    Of particular interest in the energy arena is the distributed, decentralized nonlinear control and optimization of networked microgrids with up to 100% penetration of transient renewable energy sources (i.e., photovoltaics and wind turbines).  At 100% penetration, the optimal design of energy storage systems is critical to the stability and performance of networked microgrids because all of the spinning inertia and fossil fuel of the generators have been removed from the system.  In the robotics area, collective control of teams of simple, dumb robots that solve complicated problems is of continuing research interest.  The application areas span the space from chemical plume tracing of buried land mines to underwater detection of targets of interest to airborne surveillance systems to spacecraft formations.  In the aerospace area, system identification, trajectory optimization, guidance algorithm development, and autopilot design form the fundamentals of all of these research topics.  These fundamentals are presently being applied to stall flutter suppression and meta-stable controller design research.

    Areas of Expertise

    • Renewable Energy Grid Integration
    • Collective Systems Control
    • Nonlinear Controls
    • Optimization
    • Dynamics
    • Aeroelasticity

    Research Interests

    • Energy storage system design for renewable energy grid integration
    • High penetration renewable energy microgrids
    • Collective control of networked microgrids and teams of robots
    • Exergy control for buildings
    • Flutter suppression for wind turbines
    • Nonlinear control system design

    Control and Optimization of Microgrids Research


    Optimal Control Surface

    Optimal Control Surface

    Researchers are focused on the control of individual energy load, source, and storage energy points as building blocks in a microgrid. This technology enables operation of a stable and optimized system through an agent based approach of the power electronics energy conversion points, enabling a robust and re-configurable system that does not rely on central control or communication.

    Active Research Projects


    Research is ongoing to develop new modeling, simulation, control and optimization tools for rational decisions for the best use of microgids with high penetrations renewable and dispatchable loads:

    • Rapid deployment of survivable, flexible, reconfigurable, stable, smart microgrids for military forward operating bases and humanitarian missions.
    • Transformation of U.S. military installations to be net neutral with safe, reliable power generation.
    • Training engineers who can adapt to new interdisciplinary challenges associated with delivering secure energy for both civilian and military applications.
    AIM Microgrid Strategy

    AIM Microgrid Strategy

    Control and Optimization

    Control and Optimization