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

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

Steven Y. Goldsmith

image94336-pers

Dr. Steven Y. Goldsmith holds dual appointments in the Mechanical Engineering and Engineering Mechanics Department, and the Electrical and Computer Engineering Department. He is also a Senior Fellow at the Technological Leadership Institute at the University of Minnesota. Dr. Goldsmith spent 32 years with Sandia National Laboratories and retired as Distinguished Member of the Technical Staff in 2011. While at Sandia he developed information and control systems for many different applications, including nuclear weapon testing, particle beam accelerators, seismic array monitoring, arms control and treaty verification, environmental life-cycle analysis, e-commerce and international trade, electric grid coordination, collective robotics, information warfare, and automated cyber defense. His current research efforts are focused on intelligent agent systems and technology, particularly the development of adaptive and multi-agent systems. His current projects involve the application of intelligent agents to “smart” electric grid controls and microgrids and cyber security of distributed control systems.

Areas of Expertise

  • Adaptive Software Systems and Intelligent Agents
  • Environmental Life Cycle Analysis of Microgrids
  • Multi-agent Systems for Microgrid Control
  • Cyber Security of Distributed Control Systems
  • Automated Cyber Warfare
  • Simulation of Large Scale Cyber Conflict

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

Lucia Gauchia

DSC_1949Dr. Lucia Gauchia received her General Engineering degree and her Ph.D. degree in Electrical Engineering from the University Carlos III of Madrid, Spain in 2005 and 2009, respectively.

Dr. Gauchia was appointed in 2013, the Richard and Elizabeth Henes Assistant Professor of Energy Storage Systems at the Electrical and Computer Engineering Department and Mechanical Engineering-Engineering Mechanics Department at Michigan Technological University (USA). She was a Postdoctoral Research Associate with McMaster University (Canada), working for the Canada Excellence Research Chair in Hybrid Powertrain and the Green Auto Powertrain Program. From 2008 to 2012 she worked at the Power Electric Engineering Department at the University Carlos III of Madrid (Spain).

Her research interests include the testing, modeling and energy management of energy storage systems for microgrid and electrical vehicle applications. She is particularly interested in the integration of energy storage for microgids, its selection and control depending on the energy storage technology and microgrid needs.

Areas of Interest

  • Energy Storage Systems
  • State estimation for batteries and supercapacitors

Nina Mahmoudian

Nina Mahmoudian_Fall2013-1Dr. Mahmoudian’s general research interests lie in the area of dynamics, stability, and control of nonlinear systems. Specifically, she is interested in dynamic modeling, motion planning, and developing cooperative control algorithms to autonomous vehicles. Design and control of autonomous vehicles based on the principles used by nature is another area of interest.  She works on developing analytical and computational tools for the cooperative control of a network of autonomous vehicles in complex environment using nonlinear control and stochastic analysis. The application will be for air, ground, and sea autonomous vehicles.

Areas of Expertise

  • Nonlinear Control and Dynamics
  • Cooperative Control of Multi Agent Systems
  • Autonomous Vehicles with Special Interest in Underwater Gliders

Wayne Weaver

image25785-persWayne W. Weaver received a BS in Electrical Engineering and a BS in Mechanical Engineering from GMI Engineering & Management Institute in 1997, and an MS and PhD in Electrical Engineering from the University of Illinois at Urbana–Champaign. Weaver was a research and design engineer at Caterpillar Inc., in Peoria, Illinois, from 1997 to 2003. From 2006 to 2008, he also worked as a researcher at the US Army Corp of Engineers, Engineering Research and Development Center (ERDC), Construction Engineering Research Lab (CERL), in Champaign, Illinois, on distributed and renewable-energy technology research. Weaver is a registered professional engineer in Illinois. His research interests include power electronics, electric machine drives, electric and hybrid-electric vehicles, and non-linear and optimal control.

Areas of Interest

  • Power electronics systems
  • Microgrids
  • Non-linear and game theoretic controls
  • Distributed energy resources
  • Electric drives and machinery

Interconnected and Agile Microgrids Research

Overview

Interconnected MicrogridA microgrid may consist of many interconnected energy assets to improve reliability efficiency. Two or more microgrids can also interconnect to share resources to further improve reliability and efficiency. The scalable microgrid project is aimed at creating a hardware test-bench capable of developing and testing technologies for control and optimization in large numbers of interconnected microgrids. It is also aimed at studying how these technologies can scale up to high and higher numbers of interconnected microgrids. Development of power conversion nodes that adapt and connect to an expanding interconnected microgrid structure to create a large, decentralized power distribution network that can adapt to changing resources and demands.

Active Research Projects

Applications

  • Communication protocols
  • High penetration renewable
  • Agile grid controls
  • Control of interconnected microgrids
  • Scale model explorations

Interconnected Flowchart

Agile DC Microgrid Testbed Architecture

Agile DC Microgrid Testbed Architecture

MTU - Scalable Interconnected Microgrid Testbed

MTU – Scalable Interconnected Microgrid Testbed
The light-weight agile DC microgrid testbed will be expanded to dozens of interconnected microgrids.

Interconnected Applications