Agent Based Control with Application to Microgrids with High Penetration Renewables

Sandia National Laboratory

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.

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.

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

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

Ossama Abdelkhalik

Abdelkhalik_0219Dr. Abdelkhalik conducts research in the area of dynamics, control, and  global optimization with applications to spacecraft trajectory planning, data assimilation in oil reservoirs, systems design, and traffic engineering. In some applications, the design space has numerous local minima, with mixed variables (integer and real), and the number of optimization variables can be varied among different solutions to explore new regions in the design space. Global optimization methods can handle problems with mixed variables and numerous local minima, but variable-size design space optimization is yet to be explored. The research focus is on the study of global optimization methods that can handle variable-size design space problems. Other research efforts include the recursive implementation of evolutionary optimization algorithms for the sake of improving the computational efficiency in data assimilation problems.

Areas of Expertise

  • Estimation of Dynamic Systems
  • Global Optimization
  • Data Assimilation
  • Controls and Control Systems

Madhi Shahbakhti

MahdiShahbakhtiDr. Shahbakhti joined MTU in August of 2012. Prior to this appointment, he was a post-doctoral scholar for two years in the Mechanical Engineering Department at the University of California, Berkeley. He worked in the automotive industry for 3.5 years on R&D of powertrain management systems for gasoline and natural gas vehicles. Some of his past academic and industrial research experience includes system identification, physical modeling and control of dynamic systems, analysis of combustion engines, utilization of alternative/renewable fuels, vehicular emissions, and hybrid electric vehicles. Shahbakhti is an active member of ASME Dynamic Systems & Control Division (DSCD), serving as the trust area leader and executive member of the Energy Systems (ES) committee and as a member of the Automotive Transportation Systems (ATS) technical committee, chairing and co-organizing sessions in the areas of modeling, fault diagnosis, and control of advanced fuel and combustion systems.

His research focuses on increasing efficiency of energy systems through utilization of advanced control techniques. His current research involves the transportation and building sectors which account for 68% of total consumed energy in the United States. Dr. Shahbakhti’s research to optimize efficiency of energy systems centers on developing and incorporating the following research areas: thermo-kinetic physical modeling, model order reduction, grey-box modeling, adaptive parameter estimation, model-based and nonlinear control.

Areas of Expertise

  • Dynamic Systems Modeling and Control
  • Powertrain/Vehicle Control
  • Internal Combustion Engines
  • Alternative/Renewable Fuels
  • Vehicular Emissions and Aftertreatment Systems

Research Interests

  • Modeling and Control of Energy Systems
  • Hybrid Electric Vehicles
  • Fuel Flex Powertrains
  • Energy Control of Buildings in a Smart Grid

Energy Storage Design Research


From a controls point of view, energy storage systems are the “actuators” in the electrical power grid that enable the mitigation of the transient inputs of power supplies as well as uncontrolled loads. A goal is to optimize the location and amount of energy storage capacity needed to meet microgrid performance and stability constraints. This energy storage capacity can take on many forms from batteries to fly wheels to pumped hydro. Research is focused on integrated energy storage systems that utilize unconventional resources as much as possible. For example, buildings and parking lots full of PHEV’s and EV’s are good targets of opportunity when combined with PV on covered parking structures or distribution-scale PV systems.

Active Research Projects

Energy Storage Design

Energy Storage Design