Control is and will continue to be a key discipline for realizing the objectives of smart grid initiatives. Research in control science and engineering is not limited to one or a few application concepts but is pervasive across the smart grid ecosystem. The principal contribution of this paper is to review, from a system-architectural perspective, how control enables smart grid applications. Application “templates” are presented for direct load control, automated demand response, microgrid optimization, control for distribution grids, wide-area control, and market-centric control. Technological developments, including in power electronics, that are enabling smart grid control research and applications are also itemized and two cross-cutting needs/opportunities for future research discussed. We conclude with a summary of a recent status report on the progress that has been made in the United States, noting also the challenges to further progress, in renewable generation, energy efficiency, and carbon reduction.

Keeping up with growing electricity demand and ensuring reliable grid operation, as renewable sources reach a large proportion of generation, require end-use facilities-commercial, residential, and industrial-to be sensitive and responsive to grid connections in new ways. Automated demand response (ADR) is widely acknowledged as a key approach. The technology has progressed substantially since early implementations, with worldwide projects and a new standard. Recent applications with grid-integrated buildings and microgrids are extending the functionality, with increasing sophistication of how demand-side load profiles are managed and with integration of distributed storage and generation. This paper reviews the motivation for demand response (DR) and outlines the architectural models, technology infrastructure, and communication and control protocols that are currently in use. Four projects for commercial buildings and microgrids, in the United States, United Kingdom, and China, are described. We also point out limitations of the state of the practice that represent opportunities for research and development. Several research topics are noted, focusing on needs for modeling, optimization, and control, and some preliminary related work is discussed.

Military systems are the motivational driver for much of the technology development conducted at applied research laboratories around the world. As the needs of the world's militaries change, so does the focus of this research and development. In this paper, we discuss how the fundamental characteristics of military operations in urban terrain (MOUT) impose requirements and constraints on sensing and reconnaissance. We highlight the importance of a new class of small unmanned aerial vehicles (UAVs) for network-centric military urban operations. We review some of the UAVs that have been developed in recent years, and that are under development, with particular attention to their endurance, portability, performance, payload, and communication capabilities. Selected university testbeds are also briefly noted. Over the last few years there has been considerable research focused on how these small UAVs, both individually and collectively, can operate autonomously in urban environments and help capture and communicate needed information. We discuss some of this research; specific topics covered include guidance and control for autonomous operation, multi-UAV coordination and route optimization, and ad-hoc networking with UAV nodes. A new concept of operations is described that relies on coordination and control of a heterogeneous suite of small UAVs for surveillance and reconnaissance operations in urban terrain

We present a versatile statistical verification methodology and we illustrate different uses of this methodology on two examples of nonlinear real-time UAV controllers. The first example applies our statistical methodology to the verification of a computation time property for a software implementation of a high-performance controller as a function of controller state variable values. The second example illustrates our statistical verification methodology applied to finding verifiably safe flight envelopes for a class of maneuvers, again as a function of controller state variable values. We compare our approach to verification with other statistical techniques used for estimating execution times and controller performance. We close with candidate topics for future work.

Technology transfer is an especially difficult proposition for real-time control. To facilitate it, we need to complement the “high-performance” orientation of control research with an emphasis on establishing “high confidence” in real-time implementation. Two particular problems are discussed and recent research directed at their solutions is presented. First, the use of anytime algorithms requires dynamic resource management technology that generally is not available today in real-time systems. Second, complex algorithms have unpredictable computational characteristics that, nevertheless, need to be modeled; statistical verification is suggested as a possible approach. In both cases, a synthesis of control engineering and computer science is required if effective solutions are to be devised. Simulation-based demonstrations with uninhabited aerial vehicles (UAVs) serve to illustrate the research efforts. © 2004 Wiley Periodicals, Inc.

We present a wavelet-based multi-resolution dynamic maneuver optimization method for UAV route planning. We use a combination of evolutionary computing and interior point based dynamic optimization algorithm to satisfy constraints imposed by vehicle dynamics as well as obstacle avoidance.

The paper describes a novel application of neural networks to model the behavior of students in the context of an intelligent tutoring system. Self-organizing feature maps are used to capture the possible states of student knowledge from an existing test database. The trained network implements a universal student knowledge model that is compatible with knowledge space theory approaches to student assessment and computer aided instruction. The student model can be applied to rapidly assess the knowledge of any given student, and chart a path from lower to higher states of expertise. The authors illustrate the concept on an aircraft fuel management domain, demonstrating its noise-tolerance and insensitivity to feature map parameter values. An approach to determining the correct feature map size is also described.>

A browser concept based on a connectionist architecture is presented. The concept utilizes both distributed and local representations. A proof-of-concept system is implemented for an integrally developed, Honeywell-proprietary knowledge acquisition tool. In the browser, concepts and relations in a knowledge base are represented using microfeatures. The microfeatures can encode semantic attributes, structural features, contextual information, etc. Desired portions of the knowledge base can then be associatively retrieved based on a structured cue. An ordered list of partial matches is presented to the user for selection. Microfeatures can also be used as bookmarks-they can be placed dynamically at appropriate points in the knowledge base and subsequently used as retrieval cues. The browser concept can be applied wherever there is a need for conveniently inspecting and manipulating structured information.>

The effects is studied on the convergence properties of the back-propagation learning rule of the range from which the initial weight values are randomly selected. In addition to the standard back-propagation rule, two variations are also considered, namely symmetric back-propagation and expected-value back-propagation. In most applications of back-propagation, the range of initial weights is small. It is shown that significantly higher initial weights can substantially improve learning rates. If the initial weight range is increased beyond a problem-dependent limit, however, performance degrades. Symmetric back-propagation is most sensitive to the initial weight range, while expected value back-propagation is least sensitive. The authors describe an improvement on the symmetric variation that produces faster learning rates with low initial weights.>