Techniques and potential capabilities of multi-resolutional information (knowledge) processing

A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions

Semi-Structured Decision Processes: A Conceptual Framework for Understanding Human-Automation Decision Systems

The purpose of this work is to improve understanding of existing and proposed decision systems, ideally to improve the design of future systems. A "decision system" is defined as a collection of information-processing components -- often involving humans and automation (e.g., computers) -- that interact towards a common set of objectives. Since a key issue in the design of decision systems is the division of work between humans and machines (a task known as "function allocation"), this report is primarily intended to help designers incorporate automation more appropriately within these systems. This report does not provide a design methodology, but introduces a way to qualitatively analyze potential designs early in the system design process. A novel analytical framework is presented, based on the concept of "semi-Structured" decision processes. It is believed that many decisions involve both well-defined "Structured" parts (e.g., formal procedures, traditional algorithms) and ill-defined "Unstructured" parts (e.g., intuition, judgement, neural networks) that interact in a known manner. While Structured processes are often desired because they fully prescribe how a future decision (during "operation") will be made, they are limited by what is explicitly understood prior to operation. A system designer who incorporates Unstructured processes into a decision system understands which parts are not understood sufficiently, and relinquishes control by deferring decision-making from design to operation. Among other things, this design choice tends to add flexibility and robustness. The value of the semi-Structured framework is that it forces people to consider system design concepts as operational decision processes in which both well-defined and ill-defined components are made explicit. This may provide more insight into decision systems, and improve understanding of the implications of design choices. The first part of this report defines the semi-Structured process and introduces a diagrammatic notation for decision process models. In the second part, the semi-Structured framework is used to understand and explain highly evolved decision system designs (these are assumed to be representative of "good" designs) whose components include feedback controllers, alerts, decision aids, and displays. Lastly, the semi-Structured framework is applied to a decision system design for a mobile robot.Charles Stark Draper Laboratory, Inc., under IR&D effort 101

Control Architecture Modeling for Future Power Systems

Uncontrollable power generation, distributed energy resources, controllable demand, etc. are fundamental aspects of energy systems largely based on renewable energy supply. These technologies have in common that they contradict the conventional categories of electric power system operation. As their introduction has proceeded incrementally in the past, operation strategies of the power system could be adapted. For example much more wind power could be integrated than originally anticipated, largely due to the flexibility reserves already present in the power system, and the possibility of interregional electricity exchange. However, at the same time, it seems that the overall system design cannot keep up by simply adapting in response to changes, but that also new strategies have to be designed in anticipation. Changes to the electricity markets have been suggested to adapt to the limited predictability of wind power, and several new control strategies have been proposed, in particular to enable the control of distributed energy resources, including for example, distributed generation or electric vehicles. Market designs addressing the procurement of balancing resources are highly dependent on the operation strategies specifying the resource requirements. How should one decide which control strategy and market configuration is best for a future power system? Most research up to this point has addressed single isolated aspects of this design problem. Those of the ideas that fit with current markets and operation concepts are lucky; they can be evaluated on the present design. But how could they be evaluated on a potential future power system? Approaches are required that support the design and evaluation of power system operation and control in context of future energy scenarios. This work addresses this challenge, not by providing a universal solution, but by providing basic modeling methodology that enables better problem formulation and by suggesting an approach to addressing the general chicken/egg problem of planning and re-design of system operation and control. The dissertation first focuses on the development of models, diagrams, that support the conceptual design of control and operation strategies, where a central theme is the focus on modeling system goals and functions rather than system structure. The perspective is then shifted toward long-term energy scenarios and adaptation of power system operation, considering the integration of energy scenario models with the re-design of operation strategies. The main contributions in the first part are, firstly, by adaptation of an existing functional modeling approach called Multilevel Flow Modeling (MFM) to the power systems domain, identifying the means-ends composition of control levels and development of principles for the consistent modeling of control structures, a formalization of control-as-a-service; secondly, the formal mapping of fluctuating and controllable resources to a multi-scale and multi-stage representation of control and operation structures; and finally the application to some concrete study cases, including a present system balancing, and proposed control structures such as Microgrids and Cells. In the second part, the main contributions are the outline of a formation strategy, integrating the design and model-based evaluation of future power system operation concepts with iterative energy scenario development. Finally, a new modeling framework for development and evaluation of power system operation in context of energy-storage based power system balancing is introduced.<br/