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

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

Vehicle – to – Vehicle Resource Sharing

Mississippi State University / U.S. DoD TARDEC

The existing communication layer for Vehicle to Grid (V2G) operations has sufficient throughput and capabilities for basic connectivity, but may not have enough for tasks such as operating military vehicle systems remotely. They cyber security approach to V2G operations has had some development in industry; however military vehicles demand more scrutiny from a cyber security perspective.

Vehicle-to-Vehicle (V2V) resource sharing would enable a greatly expanded flexibility for utilization of assets for forward operating bases (FOB). Consider a FOB with a variety of vehicle assets, each with different levels of functionality. The ability to daisy-chain the vehicle assets together (including partially disabled vehicles), have the vehicles automatically determine their net capability and then share resources to accomplish a common goal (force protection for example), would enable a level of capability not currently available.

Specific Tasks: Vehicle-to-Grid Simulation, Connection Protocol Assessment, Connection Protocol Development, Throughput Assessment, and Simulation Studies.

Investigators: Gordon Parker, Wayne Weaver, Steven Y. Goldsmith

 

Gordon Parker

parkerDr. Parker specializes in control system design and correlation of nonlinear dynamic models to experimental data. A key area of his research is the optimal control of microgrids with particular attention given to networked topologies. Closed loop control and real-time optimization for harmonizing use of available energy generation and storage assets, while satisfying loads, is the main theme. Applications requiring temporary or remote power motivate much of his funded research along with disaster relief scenarios. Development of a scalable, optimal control solution is critical for allowing the interconnection, in both power and communication, of separately deployed microgrids. The main challenge stems from a microgrid’s ever-changing energy asset and load portfolio and their effect on the system models used for optimal planning and control system design. Rational segregation of distributed versus centralized optimization and control is another research area. In the past year Dr. Parker and his colleagues formed the Agile and Interconnected Micrgorid (AIM) Center to bring together faculty from Computer Science, Mathematics, Cognitive Sciences and Learning, Electrical and Computer Engineering and Mechanical Engineering to focus an interdisciplinary team on this technical area. More generally, nonlinear control, system simulation, nonlinear system parameter identification and optimization, are present in most of Dr. Parker’s ongoing projects. Examples include active control of diesel engine aftertreatment systems and at-sea control of naval equipment.

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

Rush Robinett

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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

Prepositioned Power Research

Overview

Prepositioned Power RobotsResearch is focused on developing technology to create systems that can autonomously create a microgrid, for situations that require the ability to preposition a basic level of energy infrastructure such as areas damaged by natural or man-made disasters, and autonomously deploying forward operating bases. Modeling and control of robotics and power conversion systems provides the ability to create such prepositioned electric power networks.

Active Projects

Applications

Autonomous Robots can carry a variety of power equipment:

  • Intelligent power electronics for energy conversion
  • Power connection hardware
  • Generation sources, both traditional and renewable
  • Energy storage

 

Prepositioned Power

Prepositioned Power

Four autonomous microgrid robots, each with different power network functionality. Two have renewable energy generation and storage capability, another has a conventional diesel genset, and the third contains intelligent power electronics for conversion and hard-line interconnection, and switchgear. After assessing the power requirements and available resources they would physically organize and electrically interconnect to form a micro-grid.

Energy Storage Design Research

Overview

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