Kuilin Zhang

Zhang-pers

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

Mo Rastgaar

Mo_Main

Dr. Mohammad Rastgaar-Aagah is an assistant professor in the Mechanical Engineering-Engineering Mechanics Department at Michigan Technological University since 2011. Dr. Rastgaar received his Ph.D. degree in Mechanical Engineering from Virginia Tech in 2008. He was a post-doctoral associate in the Newman Laboratory for Biomechanics and Human Rehabilitation at MIT. Dr. Rastgaar is a recipient of 2014 NSF CAREER award.

Dr. Rastgaar is the founding director of the Human-Interactive Robotics Lab (HIRoLab) at Michigan Tech. The research at the HIRoLab is focused on the development of lower extremity assistive and rehabilitation devices for enhanced agility and improved mobility. The research goal at the HIRoLab is to further the critical understanding about the dynamics of gait, especially during different maneuvers, through experiments with human subjects and modeling.

Areas of Expertise

  • Dynamics and Controls
  • Robotics
  • System Identification

 

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

Blackout? Robots to the Rescue

September 25, 2014—

Big disasters almost always result in big power failures. Not only do they take down the TV and fridge, they also wreak havoc with key infrastructure like cell towers.  That can delay search and rescue operations at a time when minutes count.

Now, a team led by Nina Mahmoudian of Michigan Technological University has developed a tabletop model of a robot team that can bring power to places that need it the most.

“If we can regain power in communication towers, then we can find the people we need to rescue,” says Mahmoudian, an assistant professor of mechanical engineering–engineering mechanics. “And the human rescuers can communicate with each other.”

Unfortunately, cell towers are often located in hard-to-reach places, she says. “If we could deploy robots there, that would be the first step toward recovery.”

The team has programmed robots to restore power in small electrical networks, linking up power cords and batteries to light a little lamp or set a flag to waving with a small electrical motor. The robots operate independently, choosing the shortest path and avoiding obstacles, just as you would want them to if they were hooking up an emergency power source to a cell tower. To view the robots in action, see the video posted on Mahmoudian’s website.

“Our robots can carry batteries, or possibly a photovoltaic system or a generator,” Mahmoudian said. The team is also working with Wayne Weaver, the Dave House Associate Professor of Electrical Engineering, to incorporate a power converter, since different systems and countries have different electrical requirements (as anyone who has ever blown out a hair dryer in Spain can attest).

In addition to disaster recovery, their autonomous power distribution system could have military uses, particularly for special forces on covert missions. “We could set up power systems before the soldiers arrive on site, so they wouldn’t have to carry all this heavy stuff,” said Mahmoudian.

The team’s next project is in the works: a full-size, working model of their robot network. Their first robot is a tank-like vehicle donated by Michigan Tech’s Keweenaw Research Center. “This will let us develop path-planning algorithms that will work in the real world,” said Mahmoudian.

The robots could also recharge one another, an application that would be as attractive under the ocean as on land.

During search missions like the one conducted for Malaysia Airlines Flight 370, the underwater vehicles scanning for wreckage must come to the surface for refueling. Mahmoudian envisions a fleet of fuel mules that could dive underwater, charge up the searching robot and return to the mother ship. That way, these expensive search vehicles could spend more time looking for evidence and less time traveling back and forth from the surface.

The team presented a paper describing their work, “Autonomous Power Distribution System,” at the 19th World Congress of the International Federation of Automatic Control, held Aug. 24-29 in Cape Town, South Africa. Coauthors are Mahmoudian, Weaver, mechanical engineering graduate student Barzin Moridian, electrical engineering undergraduate Daryl Bennett and Rush Robinett, the Richard and Elizabeth Henes Professor in Mechanical Engineering.

Funding has been provided by Michigan Tech’s Center for Agile Interconnected Microgrids.

Nina Mahmoudian

Advanced Control and Energy Storage Architectures for Microgrids

Sandia National Laboratory

Consult on advanced control and energy storage architectures for microgrids.
Tasks:
1) Multiple Spinning Machines on a Single AC Bus – Finish the development of the Hamiltonian Surface Shaping Power Flow Controller (HSSPFC), controller design for multiple spinning machines on a single AC Bus.
2) Unstable Pulse Power Controller – Perform simulation studies on the unstable pulse power controller relative to the optimal feedforward (stable) controller for a single DC bus in order to determine the effectiveness of the unstable controller design relative to performance and stability.

Help characterize path forward for nonlinear control design.
Tasks:
1) Review dynamic programming interior point method (DPIP) for feedforward/optimal reference trajectory,
2) HSSPFC (Hamiltonian Surface Shaping Power Flow Controller (nonlinear dynamic structure for feedback),
3) Preliminary assessment of nonlinear wave model and impact on power absorbed.

Investigators: Wayne Weaver, 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

Distributed Agent-Based Management of Agile Microgrids

US Department of Defense, Army Research Laboratory

This project plan (APP) describes the third year of the four year program for distributed agent-based management of agile microgrids. In year 1, the team has evaluated modeling and forecasting techniques for renewable energy sources as well as developed relevant case studies. In year 2 the further developed the models and forecasting techniques as well as begin implementation of simulations and hardware test cases.

The existing simulation models a user-definable, network ofmicrogrids and the Autonomous Agile Microgrid (AAM) control system. The AAM has three main components – (1) a low-level, asset control system (Decentralized Closed Loop Controller agent- DCLC), (2) a mid-level, optimal, grid state-change solver (Decentralized Model Based Control agent – DMBC) and the highest level reasoning layer, (Distributed Grid Management agent- DGM).
The entire system is “driven” by a user-configurable, time-history of prioritized loads and events based on field data.

The focus of the year three plan is to (1) increase the reasoning capability of the DGM, (2) develop an optimal power flow strategy at the DMBC level and (3) design a human-in-the-loop interface that permits real-time interaction with the simulation.

Deliverable 1. The AAM uses a command line approach to execute the simulation and observe the grid’s evolution based on a pre-defined time history scenario of events and loads. While the process for designing rich scenarios has a well-defined workflow, the system currently lacks the ability to respond to real-time inputs from a user. The deliverable is demonstration of a new human-in-the-loop capability for the AAM simulator. It will permit one user to “actuate” the power grid manually, or in an AAM-assist mode where the user can optionally decide to implement the AAM’s recommended actions. A second user will be able to trigger events and load changes in real-time, including policy and scenario changes in the DGM. The intent for year three is to create an environment for more complete testing of the AAM and demonstrate its capabilities. This feature would then be available for future studies to increase the reasoning functions of the DGM using human-in-the-loop training.

Deliverable 2. The DMBC currently computes an optimal solution to transition the power grid from its current state to a new state as requested by the DGM. These requests are based on load, generation and storage forecast agent calculations. The DMBC also triggers a new solution based on high-tempo changes to the bus voltage, independent of the DGM, due to unforecasted changes in loads or generation. All DMBC solutions are based on the assumption of a fully functioning, well-defined set of loads, generation, and storage assets. The DMBC does not compute optimal redirection of power flow based on catastrophic generation or load failures. The deliverable is the development and demonstration of a scheme to optimally redistribute power flow for contingency and catastrophic events including equipment faults and attack damage. This redistribution strategy control may be at the level of the DMBC, DCLC or both. The human-in-the-loop capability, described in Deliverable I, will be used to demonstrate this new feature by instantaneously removing generation assets and loads.

Deliverable 3. The DGM relies on data-driven load and generation forecasts to compute grid state change requests for the DMBC. The forecasts will be improved with the inclusion of additional knowledge of inventory, asset models, and situational information. While load prioritization is accommodated, there is not functionality for addressing situations where there is not enough power to accommodate all of the highest priority loads. Policies and negotiation protocols for the DGM multi-agent system that enable power sharing among microgrids will be explored. Additional policies for fine control of load shedding will be examined and simulated. The ERDC-CERL VFOB project can potentially provide a rich source of data and models to the DGM design that support more elaborate forecasts and reasoning under conflict. In addition, the data can drive methods for scenario classification (prediction of the current and future events of the base, e.g., patrols, heightened alerts, etc.). The deliverable is a report and demonstration of improved forecasting agents and conflict resolution handling, through the power scheduling agent, based on knowledge-based reasoning mechanisms and statistical risk analysis metrics. The report will document the agent models and reasoning strategies along with a description of opportunities and gaps for implementing a fully autonomous, resilient power grid.

Investigators: Gordon Parker, Laura Brown, Wayne Weaver, Steven Goldsmith

SGAS Drive Train Model Calibration

IMECO

Introduction
Calibration is an important step in creating a physical model that can be used for predictive control system design. IMECO has a MATLAB/Simulink model of their Steering Gear Actuation System (SGAS). It contains parameters that can be classified as known (e.g. control system gains), known with uncertainty (e.g. mass properties) and unknown (e.g. damping coefficients). IMECO has also obtained experimental data that can be used to run the model and compare model outputs to sensor measurements. An optimization-based method for identifying the model parameters is needed to help automate the calibration process.

Statement of Work
Using the model and experimental data supplied by IMECO, calibrate the model using advanced numerical optimization strategies. Separate calibration parameters for several data sets will be developed in addition to a single calibration across multiple data sets. While the calibration is of primary importance, development of a methodology for automating the process will also be developed.

Investigators: Gordon Parker, Ed Trinklein

Agent Based Control with Application to Microgrids with High Penetration Renewables

Sandia National Laboratory

Abstract
Prior Work is leveraged; MTU has developed and demonstrated through simulation a prototype multiagent system that coordinates the life cycle operations of a microgrid collective composed of independent electric power sources, loads, and storage. MTU has performed simulations of DC micro grids of varying compositions and characteristics. MTU has analyzed simulation results, and developed candidate architectures and protocols for agent-based microgrid controls.

Objective
Execution of this project will further technical innovations associated with multi-agent software controlling microgrid collectives. The microgrid control algorithms for microgrid collectives will be developed and refined using Michigan Tech microgrid models and simulations validated against the MTU test bench. The algorithms will then be applied to SNL hardware models in simulation and finally against the SNL hardware test bed.

Scope
Agent-based control systems will be further developed by MTU in Matlab/Simulink blocks, tested, and refined through simulations. Once control performance objectives have been achieved, the systems will be ported to the MTU situated multi-agent system (MAS) and supporting servo loop controllers on the MTU test bench for evaluation. New Matlab simulations will be tailored and tuned to control the SNL test bed models and verified in simulation. SNL will re-apply the MTU MAS to the physical SNL test bed. SNL will collaborate with MTU on implementation and validation. Collaborative efforts will ensure that SNL attains the technology necessary to achieve the final project objectives for the SNL test bed

Required Research Innovations:
1. Identify control system performance issues between agent informatics and DC nonlinear controls. Since global computations require input from various points, processor speed and network bandwidth may dominate the performance of collaborative protocols that rely on nonlinear control approaches. Research must identify the computational and communication limits for porting nonlinear controls to agent control layers.
2. Investigate scaling properties for controls applied to increasing the number of interconnected DC microgrids. Trading power between microgrids may not be feasible due to geographical distances or communication time latencies. There may also be thresholds identified for collaboration considerations, such as partnering with 10 microgrids or less, due to the global computation requirements. Control scaling results should describe the appropriate considerations at various time scales (seconds, minutes, hours, and days). Additional considerations for scalability may include increasing the number of components within a single microgrid and increasing the variety of components within the microgrid.

Investigators: Gordon Parker, Wayne Weaver, Steven Goldsmith

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