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

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