Meta-Stability of Pulsed Load Microgrids

Sandia National Labs

Statement of Work
NAVSEA/ Military microgrids
Using the HSSPFC (Hamiltonian Surface Shaping and Power Flow Control) derived MATLAB/Simulink
tools develop a Reduced Order Model (ROM) to support control designs for pulse load applications for i)
up to (3) key ship modes of a ship power system operation and ii) a stable and unstable modes of
switching operations as a part of a survivability scenario.
Deliverables Tasks:
1. Provide ROM of meta-stable ship system.
2. Analyses and control design (feedforward and feedback) of meta-stable system.
3. Analyses and control design for multi-pulse load systems.
4. Analyses of the effects and potential benefits of non-linear magnetics in meta-stable system.
5. Develop and perform hardware testing on metastable laboratory benchtop system.
6. Develop networked Microgrid model for KIER/LUXCO scenario

Investigator: Wayne Weaver

Unstable and Pulse Load Control Designs for Naval Electrical Systems

Sandia National Labs

NAVSEA
Using the HSSPFC (Hamiltonian Surface Shaping and Power Flow Control) derived Matlab/Simulink tools develop a Reduced Order Model (ROM) to support control designs for pulse load applications for i) up to (3) key ship modes of a ship power system operation and ii) a stable and unstable modes of switching operations as a part of a survivability scenario.

ElectroMagnetic (EM) Coupling-Penetration Measurement Standard
Testing and simulation facilities have various methods for test readiness activities and post-test instrumentation and sensors performance verification. Such a canonical standard has been developed but has not been used or re-verified in recent years. Using the mathematical model of the canonical measurement standard previously documented in an EM Sand report, verify both analytical and computational analyses and propose experimental validation with analytical model.

Investigator: Wayne Weaver

“CRISP Type 2: Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity.”

National Science Foundation

This CRISP project addresses the challenges associated with the rapid evolution of the electricity grid to a highly distributed infrastructure. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels for delivering electricity to customers, to distribution microgrids, entities that actively manage local production, storage and use of electricity, with participation from individual customers. Distribution microgrids combine the advantages of the traditional electricity grid with the advantages of emerging distributed technologies, including the ability to produce and use power locally in the event of grid outages. The project will result in a unified model that incorporates key aspects of power generation and delivery, information flow, market design and human behavior. The model predictions can be used by policymakers to guide a transition to clean energy via distribution microgrids. The expectation is to enable at least 50% of electric power to come from renewable resources. This cannot be done with either the traditional grid, due to its limited capacity to accommodate intermittent renewable power sources, or with fully decentralized approaches, which would not be affordable for most utility customers.

This project addresses many socio-technological gaps necessary to translate from research discovery to commercial applications. To date, there is no theoretical framework to ensure system stability as renewable energy routed through power electronics replaces traditional rotating machinery. To achieve an optimal mix of storage performance and information bandwidth and to design nonlinear controllers, we will use Hamiltonian Surface Shaping Power Flow Control theory. We will study methods to detect malicious tampering with information flows. The complex interaction of intermittent resources, human behavior and market structures will be modeled in an agent-based simulation. System inputs will be provided by utility and meteorological data, and by behavioral models that incorporate information obtained by surveys, interviews and metering data. Emergent system dynamics will be abstracted and studied using dynamical complex network theory, to explore stability limits as a function of human behavior and market design. Finally, the effect of enhanced controllability of distribution systems on the robustness of large energy-information-social networks will be analyzed using interdependent Markov-chain models. Graduate students involved in this program will be exposed to a unique combination of skills from engineering, data analysis and social sciences; such cross-disciplinary training will prepare them for leadership roles in the emerging energy economy of tomorrow.

Investigators: Laura Brown, Chee-Wooi Ten, Wayne Weaver

Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity

September 20, 2015

Laura Brown (PI) received a $699,796 NSF grant. The title of the project is “CRISP Type 2: Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity.” The co-principal investigators of this project are Chee-Wooi Ten (ECE) and Wayne Weaver (ECE). This is a three-year collaborative project with four other institutions with a total budget of $2,499,801. The project addresses the rapid evolution of the electricity grid, from one based on few centralized generators providing power to millions of users to one where many distributed energy resources. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels that deliver electricity from the transmission grid to customers, to distribution microgrids, highly intelligent entities that actively manage production, storage and use of electricity.

 

 

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.

Distributed Agent-Based Management of Agile Microgrids Research

Overview

A remote microgrid is a class of stand-alone power grids that services diverse loads, employs distributed generation with renewable resources, and requires on-line control and optimization to maintain stability and power flow. The grid control system is both agile and autonomous, accommodating rapid changes in generation and load resources with minimal training or intervention on the part of human operators.

Active Research Projects

Applications

  • Control based on a hybrid approach that marries novel model-predictive control strategies with multi-agent systems.
  • Utilizes artificial intelligence and machine learning techniques.
  • By imbuing software agents with component models and knowledge about grid operations the collective can cooperatively plan and execute coordinated operations that essentially re-organize grid structure in real-time while maintaining uninterrupted service.

Distributed Agent Based Management Layout

High Order Nonlinear Droop

High Order Nonlinear Droop

Distributed Flowchart

Control and Optimization of Microgrids Research

Overview

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

Applications

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