On Integrating Object Detection Capability into a Coastal Energy Conversion System

U.S. Department of Defense, Office of Naval Research

Project Summary
Near-shore wave energy converter arrays may be designed to provide uninterrupted power to a number of coastal sensing applications, including sensors monitoring meteorological conditions, sea-water chemical/physical properties, tsunamis and storm surges, fish and other marine life, coastal and sea-floor conditions, etc. Active control seeking near-optimum hydrodynamic operation has been shown to enable a dramatic reduction in device size for required amounts of power. Certain features of the control strategies developed make them particularly amenable to incorporation of additional sensing capability based on the wave patterns generated by intruding submerged objects (at distances on the order of 1000 m), in particular, the phase changes to the approaching wave field that occur in the presence of an object.

This project investigates schemes for actively controlled wave energy converter arrays in coastal waters which enable detection of intruding marine vessels by monitoring the spatial and temporal energy conversion rates over the arrays. The proposed approach mainly utilizes a linear-theory based understanding of wave propagation, body hydrodynamics, and controller design, but also incorporates nonlinear extensions based on Volterra series modeling. Of particular interest, is using small device sizes, for which response nonlinearities can be significant. Therefore, it is proposed to exploit the nonlinearities to enhance energy generation. Furthermore, also investigate ways to utilize features of the nonlinear response that enable preferential coupling to certain phase signatures, so that energy conversion by certain array elements would imply the presence of an object. Analysis and simulation results on arrays of moored devices will be extended to free-floating arrays.

The first objective of the overall effort is to evaluate the proposed techniques through analysis and simulation. For near-shore sea areas to be identified, two categories or types of array designs with their own particular control strategies will be investigated, using Hydrodynamics and Controls based analytical techniques and detailed simulations (linear and nonlinear). Necessary in this process is the characterization of the phase-change signatures of various submerged objects when stationary and when in translation. This knowledge will provide the test parameters for the designs to be investigated. The first two years of the overall, 4-year long, effort are expected to provide the groundwork for the development of a prototype system. Prior to ‘at-sea’ prototype testing, first test the prototype in a wave-basin environment. To provide reliable designs for the testing in the wave basin, wave tank testing under simplified conditions is also proposed. The overall testing sequence from wave tank tests through wave-basin tests to ‘at sea’ tests is expected to occur over years 3 and 4.

Investigators: Umesh Korde, Rush Robinett, Ossama Abdelkhalik

Increasing Ship Power System Capability throught Exergy Control

U.S. Dept. of Defense, Office of Naval Research

The main objective of this effort is to develop an exergy control strategy, applied to a ship medium voltage de (MVDC) grid that exploits exergy flow coupling between multiple subsystems. This work involves: 1) exergy control strategy development and 2) mapping exergy control system performance to ship-relevant metrics. A ship power grid Challenge Problem model will be developed to illustrate and resolve the fundamental gaps of exergy control. The model will also compare and contrast feedforward and feedback exergy control with conventional strategies.

Ship subsystems and mission modules perform energy conversion during their operation resulting in a combination of electricity consumption, heat generation and mechanical work. Mission module thermal management requirements further impact the ship’s electrical grid, for example, via chiller operation. Subsystems often have opportunities for performing an energy storage role during their operation cycle. A ship crane is one example where potential energy is stored in the raised load and can be converted into electrical energy during lowering. Whether subsystem requirements are dominated by electrical, thermal or mechanical functions, they are coupled through energy and information flows, often by the ship’s electrical power grid. Treating each subsystem as a disconnected entity reduces the potential for exploiting their inherent interconnection and likely results in over designed shipboard systems with higher than necessary weight and volume. Realizing the opportunity of coupled subsystem operation requires modeling and control schemes that are unavailable today, but that we believe should require few infrastructure changes. We propose that the design and control of coupled ship subsystems should be based on exergy- the amount of energy available for useful work. A recent study, applied to a room heating system, showed that exergy control increased the overall efficiency by 18%. Since the system was powered electrically, this translated directly to a decrease in the electrical load. The main objective of this effort is to develop an exergy control strategy, applied to a ship medium voltage de (MVDC) grid that exploits exergy flow coupling between multiple subsystems.

An exergy approach to control permits consideration of both mission modules and the platform infrastructure as mixed physics power systems that may act as loads, storage or sources depending on the situation. Instead of separately designed and managed subsystems that satisfy electrical and thermal requirements via static design margins a, multi-physics, unified system-of-systems approach is needed to enable affordable mid-life upgrades as requirements and mission systems evolve over the platform’s lifespan. Being able to translate the benefits of exergy control into savings in mass, volume, energy storage requirements and fuel usage is necessary for making rational design decisions for new ship platforms and for increasing the efficiency of legacy ship systems. Currently, there does not exist an analysis technique to map control system performance into ship-relevant performance metrics. This restricts ship designers from understanding the tradeoffs of adopting advanced control schemes that may exploit subsystem coupling. One of the objectives of this work is to develop a method for extrapolating control system performance into ship-relevant metrics that impact mass, volume, energy storage, and fuel usage.

As described above, there are two main thrusts to this work: (1) exergy control strategy development and (2) mapping exergy control system performance to ship-relevant metrics. We will develop a ship power grid Challenge Problem model that will illustrate the fundamental gaps of exergy control that will be addressed. The model will also be used to compare and contrast feedforward and feedback exergy control with conventional strategies. Techniques for mapping the results of the exergy control to weight, volume, and energy storage requirements will be developed and applied to the Challenge Problem throughout the project.

Investigators: Gordon Parker and Rush Robinett, and Ed Trinklein.

Making Small Wave Energy Converters Cost-Effective for Underwater Microgrids through a 10-Fold Improvement in Year-Round Productivity

South Dakota School of Mines and Technology


Proposed Work
Drivetrain and Actuator
1- Conceptual Design of actuators with large stroke and large rated force.
2- Conceptual Design of a high efficient drivetrain and energy storage for low frequency oscillatory systems (WECs)
3- Evaluate several technologies (electrical, mechanical, and hydraulic) for the design of the actuator and powertrain, with the requirement of limiting the overall cost.

Investigators: Ossama Abdelkhalik and Mark Vaughn

Unstable and Pulse Load Control Designs for Naval Electrical Systems

Sandia National Labs

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

Modeling and Control Technologies for Near-Term and Long-Term Networked Microgrids

Argonne National Laboratory

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

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

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

Low-Cost Underwater Glider Fleet for Littoral Marine Research

Office of Naval Research

This research is focused on development of innovative practical solutions for control of individual and multiple unmanned underwater vehicles (UUVs) and address challenges such as underwater communication and localization that currently limit UUV use. More specifically, the Nonlinear and Autonomous Systems Laboratory (NAS Lab) team are developing a rigorous framework for analyzing and controlling underwater gliders (UGs) in harsh dynamic environments for the purpose of advancing efficient, collaborative behavior of UUVs.

Underwater gliders are now utilized for much more than long-term, basin-scale oceanographic sampling. In addition to environmental monitoring, UGs are increasingly depended on for littoral surveillance and other military applications. This research will facilitate the transition between academic modeling/simulation problem solving approach to real-world Navy applications. The importance of this research is evident in the Littoral BattleSpace Sensing (LBS) Program contract at the Naval Space and Naval Warfare Systems Command for 150 underwater gliders, designated the LBS-G. These gliders will be operated by the Navy in forward areas to rapidly assess and exploit environmental characteristics to improve the maneuvering of ships and submarines and advance the performance of fleet sensors.

Research results will provide the coordination tools necessary to enable the integration of these efficient and quiet vehicles as part of a heterogeneous network of autonomous vehicles capable of performing complex, tactical missions. The objective is to develop practical, energy-efficient motion control strategies for both individual and multiple UGs while performing in inhospitable, uncertain, and dynamic underwater environments.

The specific goals of this project are twofold. The first goal is to design and fabricate a fleet of low-cost highly maneuverable lightweight underwater gliders. The second goal is to evaluate the capability of the single and multiple developed UGs in littoral zones. The proposed work will develop UGs that would share the buoyancy-driven concept with the first generation of gliders called “legacy gliders.” However, the NAS Lab UGs will be smaller in size, lighter in weight, and lower in price than legacy gliders. This will result in more affordable and novel UG applications. Moreover, the NAS Lab design to development approach allows for technological innovation that overcomes known challenges and responds to unexpected needs that arise during testing. Therefore, the significance of this research is that it will enable implementation of recently developed efficient motion planning algorithms, multi-vehicle coordination algorithms, and extension of these algorithms in realistic conditions where absolute location and orientation of each vehicle is not known and the time-varying flow field is not locally determined.


Investigators: Nina Mahmoudian