HVDC Distribution Study of Intelligent Power System

University of Dayton Research Institute

SCOPE
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.

OBJECTIVES
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.

RESEARCH TASKS
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

Increasing Ship Power System Capability throught Exergy Control

U.S. Dept. of Defense, Office of Naval Research

The main objective of this effort is to develop an exergy control strategy, applied to a ship medium voltage de (MVDC) grid that exploits exergy flow coupling between multiple subsystems. This work involves: 1) exergy control strategy development and 2) mapping exergy control system performance to ship-relevant metrics. A ship power grid Challenge Problem model will be developed to illustrate and resolve the fundamental gaps of exergy control. The model will also compare and contrast feedforward and feedback exergy control with conventional strategies.

Introduction
Ship subsystems and mission modules perform energy conversion during their operation resulting in a combination of electricity consumption, heat generation and mechanical work. Mission module thermal management requirements further impact the ship’s electrical grid, for example, via chiller operation. Subsystems often have opportunities for performing an energy storage role during their operation cycle. A ship crane is one example where potential energy is stored in the raised load and can be converted into electrical energy during lowering. Whether subsystem requirements are dominated by electrical, thermal or mechanical functions, they are coupled through energy and information flows, often by the ship’s electrical power grid. Treating each subsystem as a disconnected entity reduces the potential for exploiting their inherent interconnection and likely results in over designed shipboard systems with higher than necessary weight and volume. Realizing the opportunity of coupled subsystem operation requires modeling and control schemes that are unavailable today, but that we believe should require few infrastructure changes. We propose that the design and control of coupled ship subsystems should be based on exergy- the amount of energy available for useful work. A recent study, applied to a room heating system, showed that exergy control increased the overall efficiency by 18%. Since the system was powered electrically, this translated directly to a decrease in the electrical load. The main objective of this effort is to develop an exergy control strategy, applied to a ship medium voltage de (MVDC) grid that exploits exergy flow coupling between multiple subsystems.

An exergy approach to control permits consideration of both mission modules and the platform infrastructure as mixed physics power systems that may act as loads, storage or sources depending on the situation. Instead of separately designed and managed subsystems that satisfy electrical and thermal requirements via static design margins a, multi-physics, unified system-of-systems approach is needed to enable affordable mid-life upgrades as requirements and mission systems evolve over the platform’s lifespan. Being able to translate the benefits of exergy control into savings in mass, volume, energy storage requirements and fuel usage is necessary for making rational design decisions for new ship platforms and for increasing the efficiency of legacy ship systems. Currently, there does not exist an analysis technique to map control system performance into ship-relevant performance metrics. This restricts ship designers from understanding the tradeoffs of adopting advanced control schemes that may exploit subsystem coupling. One of the objectives of this work is to develop a method for extrapolating control system performance into ship-relevant metrics that impact mass, volume, energy storage, and fuel usage.

As described above, there are two main thrusts to this work: (1) exergy control strategy development and (2) mapping exergy control system performance to ship-relevant metrics. We will develop a ship power grid Challenge Problem model that will illustrate the fundamental gaps of exergy control that will be addressed. The model will also be used to compare and contrast feedforward and feedback exergy control with conventional strategies. Techniques for mapping the results of the exergy control to weight, volume, and energy storage requirements will be developed and applied to the Challenge Problem throughout the project.

Investigators: Gordon Parker and Rush Robinett, and Ed Trinklein.

Distributed and Decentralized Control of Aircraft Energy Systems

U.S. Dept of Defense, Air Force Research Lab -InfoSciTex Corp (AFRL)

Aircraft energy system components, including sources, loads and distribution, have multiple commitments and responsibilities. Often much of the system is comprised of power electronic converters for sources, loads, and energy storage (chemical, mechanical and thermal). For example, a point of load power converter has the commitment to serve the energy needs of the end load. However, if the power system collapses, the needs of the load cannot be met. Therefore, it is also in the interest of the conve1ter to contribute to the global stability of the power system by reducing nonlinear dynamics and incremental negative impedance. One method to mitigate the destabilizing effects of constant power loads is the power buffer concept. A power buffer is a device that mitigates a destabilizing event by presenting controlled impedance to the supply during the transient while local energy is used to maintain constant power to the load until the system can recover. A power buffer may include additional hardware, or may merely be a modification of the controls of an existing active front end power converter. However to date the use of a load as an energy asset in a power buffer has been limited to traditional chemical (capacitor and battery) storage devices in the electrical network. Next generation aircraft may have a broad range of potential assets in the form of loads, including inertial spinning devices and thermal systems, which could be utilized in the overall energy strategy.

Research with AFRL researchers to investigate distributed and decentralized control of aircraft energy systems. This effort will include using models and simulations to formulate decentralized control and study the effects. Specifically,
• Develop and document a mathematical model of the aircraft energy systems including thermal and inertial loads.
• Formulate a decentralized power buffer control including inertial and thermal loads as energy storage assets.
• Develop and document nume1ic simulation models in MATLAB/Simulink and/or
• Modelica. The models will include aircraft system and controls.
• Validate theoretic results through simulation under stressing scenarios.

Investigator: Wayne Weaver

Wave Energy Conversion (WECs)

WECS are devices with moving elements that are directly activated by the cyclic oscillation of the waves for Ocean wave energy utilization and energy harvesting. Power is extracted by converting the kinetic energy of these displacing parts into electric current; dynamics, control, and hydrodynamics of oscillating bodies and pressure distributions performing as the primary working element of a wave energy converter. Specific recent research has been on small devices capable of integration into measurement and sensing systems in the ocean, as well as shore and ocean based microgrids serving a variety of applications. A focal area of this current research has been new techniques for modeling and control, including novel ways to utilize existing approaches.

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
  • Advanced Control of Wave Energy Converters

    Sandia National Laboratory

    Background
    A new multi-year effort has been launched by the Department of Energy to validate the extent to which control strategies can increase the power produced by resonant Wave Energy Converters (WEC) devices. A large number of theoretical studies have shown promising results in the additional energy that can be captured through control of the power conversion chains of resonant WEC devices.
    However, most of the previous work has been completed on highly idealized systems and there is little to no validation work. This program will specifically target controls development for nonlinear, multi-degree of freedom WEC devices. Multiple control strategies will be developed and the efficacy of the strategies will be compared within the “metric matrix.”
    Objective: The purpose of this contract is to provide the labor to develop and implement custom control strategies for a specified WEC device.

    Scope of Work
    Michigan Technological University (MTU) will provide optimization expertise (Dynamic Programing, pseudo-spectral, shape optimization, others) to support MTPA-FF (mid-targeting phase and amplitude-feedforward) designs and analysis specific to the performance model WEC. This will include numerical simulations specific to the metric matrix requirements. In addition, MTU will provide expertise and support for feedforward real-time implementation and investigations.

    Investigator: Ossama Abdelkhalik

    Modeling and Control Technologies for Near-Term and Long-Term Networked Microgrids

    Argonne National Laboratory

    Introduction
    Microgrids offer attractive options for enhancing energy surety and increasing renewable energy penetration. Within a single microgrid energy generation, storage and utilization is localized. Greater enhancements to energy surety can be accomplished by networking multiple microgrids into a collective which can lead to almost unlimited use of renewable sources, reduction of fossil fuels and self-healing and adaptive systems. However, one pitfall to avoid is losing the surety within the individual microgrids. This produces design and control challenges that are currently unsolved in networked microgrids. To help solve this dilemma, development of analysis methods for design and control of networked microgrids is the general focus of this activity.

    Specific tasks include:
    1. Collaborate and form a coalition with national labs and other microgrid stakeholders to identify key R&D topics in networked microgrids.
    2. Look at near term solutions that can quickly and easily be integrated into existing microgrids,
    3. Determine best practices and optimized control strategies for the ground-up design of future networked microgrids.
    4. Work within the DOE and national lab partnerships to produce the FOA whitepaper on single microgrid systems.
    Tasks 1 through 3 will include microgrid modeling, control and optimizations of single and networked microgrids with focus on achieving DOE 2020 microgrid targets. Specifically, targets include developing commercial scale microgrid systems that reduce outage time, improve reliability and reduce emissions.

    TASK 1: Collaborate and form a coalition with national labs and other microgrid stakeholders to identify key R&D topics in networked microgrids.

    TASK 2: Look at near term solutions that can quickly and easily be integrated into existing microgrids Model development is one of the first steps in the microgrid control design process and incurs trade-offs between fidelity and computational expense. Models used for modelbased control implementation must be real-time while having sufficient accuracy so that feed-forward information can be maximized to achieve specified requirements. The expected outcomes of this study are (1) determination of appropriate time scales for networked microgrid modeling (2) a MATLAB/ Simulink reduced order model library of networked microgrid components and (3) lab scale hardware validation of networked microgrid models. These model libraries will then be used to construct models and develop control and optimization algorithms of current microgrid systems and equipment.

    Task 3: Determine best practices and optimized control strategies for the ground-up design of future networked microgrids. Demonstrating robust networked microgrids will require system-level optimization. This includes both its physical and control system designs. This task will build upon the models and optimizations achieved in task 2 applied to the design of future networked microgrids. The expected outcomes of this study are (1) energy-optimal design methods suitable for networked microgrid design and control of future long-term application architectures and (2) integration of these strategies with the microgrid model environment and bench scale hardware described in task 2.

    Investigators: Wayne Weaver, Gordon Parker

    Microgrid Modeling and Optimization for High Penetration Renewables Integration

    Sandia National Laboratory

    Abstract
    Future microgrids are envisioned having a large renewable energy penetration. While this feature is attractive it also produces design and control challenges that are currently unsolved. To help solve this dilemma, development of analysis methods for design and control of microgrids with high renewable penetration is the general focus of this activity. The specific foci are (1) reduced order microgrid modeling and (2) optimization strategies to facilitate improved design and control. This will be investigated over a multi-year process that will include simplified microgrid modeling and control, single microgrid modeling and control, collective microgrid modeling and control, and microgrid (single and collective) testing and validation.

    Microgrid Reduced Order Modeling (ROM)
    Model development is one of the first steps in the microgrid control design process and incurs trade-offs between fidelity and computational expense. Models used for model-based control implementation must be real-time while having sufficient accuracy so that feedforward information can be maximized to achieve specified requirements. The expected outcomes of this study are (1) quantification of model uncertainty as a function of the assumptions with particular interest given to reduced order models (2) determination of appropriate time scales for reduced order modeling and (3) a MATLAB / Simulink reduced order model library of microgrid components. Contrasting different microgrid reduced order modeling approaches and simulation results that demonstrate the reduced order microgrid simulation.

    Microgrid Optimization
    Demonstrating microgrids with robust and high renewable penetration requires system-level extremization. This includes both its physical and control system designs. The expected outcomes of this study are (1) energy-optimal design methods suitable for microgrid design and control and (2) integration of these strategies with the microgrid reduced order model environment described above. How energy-optimal design can be exploited for microgrid design and control.

    Investigators: Gordon Parker, Wayne Weaver

    Mark Vaughn

    xdJJr3eLPtUPZrpPVx4z3KTToTZm8-h2bIOB7P4spj0Dr. Vaughn has joined Michigan Tech as a research professor after retiring from Sandia National Laboratories. His research expertise is in the area of mechanical and electromechanical design, stress analysis, dynamics, and innovative applications. He has over 10 patents, and has been the lead on a broad array of projects for the military.

    Areas of Expertise

    • Electro-Mechanical Analysis and Design
    • Energy Storage
    • Hydrogen Peroxide Systems
    • Advanced Payloads
    • Robotic Vehicles
    • Biomedical Devices

    Vehicle to Grid Research

    Vehicle to Grid

    Overview

    By treating a hybrid vehicle as a microgrid, it has the ability to exploit interconnection strategies for plug-and-play integration with deployed microgrids while being a mobile, energy exchange system between disconnected power grids. Research is focused on optimization and control of microgrids that have a significant penetration of vehicles that can be loads, sources, or energy storage devices.

    Active Projects

    Applications

    • Exploiting tradeoffs between high power plug-in vehicles, storage and renewable penetration
    • Optimal storage state of charge for mobile/vehicular microgrids
    • Vehicle design impact on grid connectivity
    • Use of military hybrids for FOB microgrid deployment
    • Distributed control strategies for plug-in hybrid charging for more manageable grid load
    • Information transfer between vehicles and grid (smartgrids)

    Vehicle To Grid Chain

    Vehicle Chart