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United Nations’ 7th Sustainable Development Goal envisions the availability of modern energy for everyone by 2030. While the progress has been satisfactory in the last few years, further rural electrification is increasingly challenging. The current mainstream approach of electrifying villages individually is becoming cost-ineffective due to uncertainties in both resource availability and energy demand for small, difficult-to-reach, residences. A networked rural electrification model, i.e. a cost-optimized network connecting villages and generation facilities, could improve resources utilization, reliability and flexibility. However, determining optimal paths with common search algorithms is extremely inefficient due to complex topographic features of rural areas. This work develops and applies an artificial intelligence search method to efficiently route inter-village power connections in the common rural electrification situation where substantial topological variations exist. The method is evolved from the canonical A* algorithm. Results compare favorably with optimal A* results, at significantly reduced computational effort. Furthermore, users can adaptively trade-off between computation speed and optimality and hence quickly evaluate sites and configurations at reasonable accuracy, which is impossible with classical methods.

Networked rural electrification can potentially improve energy resources utilization, reduce cost and enhance supply reliability. Identifying optimal connection paths is critical for proper network design. To overcome the inefficiency of applying standard A* path-finding method to complex topography, multiplier-accelerated A* (MAA*) algorithm, which utilizes a modified heuristic, has been developed in previous research. While MAA* can generally reduce computation time by ~90% at the cost of ~10% optimality, the computation burden can still be remarkable for some areas with intricate topological variations. This paper proposes an adaptive version of MAA*. By introducing intermediate nodes in MAA*, the new algorithm significantly simplifies computations in complex regions. This greatly facilitates the analysis and design of optimal network for cost-effective electricity supply to users in remote, difficult-to-reach areas.

This paper introduces a potential approach for mitigating the effects of communication delays between multiple, closed-loop hardware-in-the-loop experiments which are virtually connected, yet physically separated. The approach consists of an analytical procedure for the compensation of communication delays, along with the supporting computational and communication infrastructure. The control design leverages tools for the design of observers for the compensation of measurement errors in systems with time-varying delays. The proposed methodology is validated through computer simulation and hardware experimentation connecting hardware-in-the-loop experiments conducted between laboratories separated by a distance of over 100 km.

An increase of non-linear loads, primarily from power electronics, has substantially increased current harmonics in commercial buildings, which contributes to decreased transformer efficiency / lifespan and poor power quality. This study uses recorded power consumption data from common miscellaneous electric loads (MELs) seen in offices, combined with detailed characterizations of example MELs, to simulate harmonic cancellation within building circuits. Typically, harmonic cancellation studies assume that AC converters operate across their rated power range. However, this study finds that common MELs operate below 40% of rated power the majority of the time when not quiescent; 89% of sampled devices never operated above 60% of rated power. Simulations using these more realistic power levels indicate current-harmonic cancellation ( 3 rd to 13 th harmonic) is significantly lower than that predicted when using full-range power assumptions, resulting in minor errors for low-order harmonics and larger errors for higher order harmonics. Increased MELs load diversity increases harmonic cancellation, but insufficiently to eliminate errors. In contrast, blending lighting loads with MELs on the secondaries of distribution transformers improves harmonic cancellation to near those predicted by traditional methods. These results indicate that realistic power levels, as well as better characterization of harmonics from typical MELs, should be used to estimate harmonic cancellation.

Motorized window shades are widely used with embedded lithium-ion batteries that are charged by solar energy via photovoltaic (PV) arrays. The complex models of sun irradiance and glass light absorption are developed to forecast the energy balance for long term operation of those off-grid shades. However, the batteries are often depleted due to partial shading and low illumination. The paper presents a new method of monitoring the energy balance of those motorized shades via IoT networks. The battery voltage variations over time are periodically measured to calculate the input solar energy and the motor power consumption. The result can inform users to either add more PV arrays or charge battery using a power adaptor before it is completely discharged. It guarantees the continuous operation of motorized shades.

Adapting turbocharger performance maps to a form suitable for dynamic simulations is challenging for the following reasons: (1) the amount of available data is typically limited, (2) data are typically not provided for the entire operating range of the compressor and turbine and (3) the performance data are non-linear. To overcome these challenges, curve fits are typically generated using the performance data individually for each device. The process, however, can take un-economical amounts of effort to implement for a range of compressors and turbines. This article introduces a method to implement non-dimensional performance maps thereby allowing a range of turbochargers to be modeled from the same performance data, reducing the effort required to implement models of different sizes. The non-dimensional maps seek to model the performance of compressor and turbine families in which the geometry of the rotor and housing are similar and allow the turbocharger to be scaled for simulation in much the same way used to design customized sizes of turbochargers. A method to match the non-dimensional compressor map to engine performance targets by selecting the compressor diameter is presented, as well as a method to match the turbine to the selected compressor.

This work builds on the Volterra series formalism presented in [D. W. Dreisigmeyer and P. M. Young, J. Phys. A \textbf{36}, 8297, (2003)] to model nonconservative systems. Here we treat Lagrangians and actions as `time dependent' Volterra series. We present a new family of kernels to be used in these Volterra series that allow us to derive a single retarded equation of motion using a variational principle.

In this paper, we propose a robust control strategy for reducing system frequency deviation, caused by load fluctuation and renewable sources, in a smart microgrid system with attached storage. Frequency deviations are associated with renewable energy sources because of their inherent variability. In this work, we consider a microgrid where fossil fuel generators and renewable energy sources are combined with a reasonably sized, fast acting battery-based storage system. We develop robust control strategies for frequency deviation reduction, despite the presence of significant (model) uncertainties. The advantages of our approach are illustrated by comparing system frequency deviation between the proposed system (designed via μ synthesis) and the reference system which uses governors and conventional PID control to cope with load and renewable energy source transients. All the simulations are conducted in the Matlab™ and Simulink™ environment.

With the low price of natural gas, and its low emissions, significant market growth for natural gas engines is likely in various applications. There are multiple challenges in controlling a natural gas engine, especially a pre-mixed lean burn natural gas engine. In particular, the system dynamics includes long fuel and air transport delays. In terms of natural gas engine control, our main focus is on engine speed control, engine output torque control, air/fuel ratio and emission regulation. In order to facilitate control study and development, we develop a control-oriented turbocharged pre-mixed lean burn natural gas engine mean value model (MVM). This model is designated for natural gas engine controller design, control algorithm development, and first step validation. The model is implemented in the MATLAB® Simulink® environment. The model is validated with a 10L natural gas engine for power generation applications.

A feedback/feedforward controller architecture is developed that characterises the achievable reference tracking of real time inputs for both minimum phase and non-minimum phase systems with time delays, when there are no modelling errors or external disturbances. This characterisation is obtained by factoring the plant into its minimum phase, non-minimum phase, and time delay components, which are used to design two feedforward controllers that inject signals into two points of the feedback loop. Design constraints are provided that determine both the types of signals that may be achieved, and the feedforward controllers that will generate that output. Of course, in practice, both modelling errors and external disturbances will be present. In this case, we develop robust analysis tools that both guide the feedback controller design process, and provide rigorous robust tracking performance that guarantees for the overall resulting closed-loop system. Robust methods for designing the feedforward controller...

People and technology are at the centre of our climate crisis. Technological advances have contributed to the accumulation of GHG. On the other side of the coin, technological advances such as renewable energy hold promise for ameliorating our environmental woes. Similarly, rapid population growth has the potential to exacerbate our environmental problems, whilst behaviour change holds the key to conserving our natural resources. This is the sustainability paradox: the problem is also the solution.

This paper utilizes the Karush–Kuhn–Tucker (KKT) conditions to develop a scheme for economic dispatch of load to generation units in a microgrid. By using the (pure) KKT conditions, the true optimum can be found in the sense of minimizing the fuel consumption. This is the most economic way of dispatching the load to generators, but it may not be directly applicable in the real world. In practice extra constraints, in the form of swing generator(s), base load generator upper and lower operating limits, and spinning reserve are also required. These lead to a set of constrained KKT conditions, which are utilized to solve the problem. Note that although this solution obtained from constrained KKT conditions is not the true optimum, the extra constraints are there to enhance the system stability and reliability.This new approach is tested extensively in simulation against standard dispatch approaches, and its performance is seen to be superior in all cases.

Augmented cognition is a form of human-systems interaction in which a tight coupling between user and computer is achieved via physiological and neurophysiological sensing of a user's cognitive state. This interactive paradigm seeks to revolutionize the manner in which humans engage with computers by leveraging this knowledge of cognitive state to precisely adapt user-system interaction in real time. This review provides an overview of contemporary works in the field of augmented cognition and details regarding the three main components of an augmented cognition system: cognitive state sensors, adaptation strategies, and control systems. The review provides a perspective on the field as well as insights into the many challenges that lie ahead for those who endeavor to realize the full potential of augmented cognition.

While having the potential to significantly improve heating, ventilating and air conditioning (HVAC) system performance, advanced (e.g., optimal, robust and various forms of adaptive) controllers have yet to be incorporated into commercial systems. Controllers consisting of distributed proportional-integral (PI) control loops continue to dominate commercial HVAC systems. Investigation into advanced HVAC controllers has largely been limited to proposals and simulations, with few controllers being tested on physical systems. While simulation can be insightful, the only true means for verifying the performance provided by HVAC controllers is by actually using them to control an HVAC system. The construction and modeling of an experimental system for testing advanced HVAC controllers, is the focus of this article. A simple HVAC system, intended for controlling the temperature and flow rate of the discharge air, was built using standard components. While only a portion of an overall HVAC system, it is representative of a typical hot water to air heating system. In this article, a single integrated environment is created that is used for data acquisition, controller design, simulation, and closed loop controller implementation and testing. This environment provides the power and flexibility needed for rapid prototyping of various controllers and control design methodologies.

Recent years have seen a significant number of low-cost (sub-$100) Linux-based microcontroller systems introduced to the market. Initially targeted to the educational and hobbyist markets, the low cost, small physical package, low power consumption and high compute capability of these microcontrollers has made them interesting to research and technical communities. Many of the applications these platforms are being used for require precise timing to measure physical phenomena and coordinate with other computers.

This letter provides a brief explanation of echo state networks (ESNs) and provides a rigorous bound for guaranteeing asymptotic stability of these networks. The stability bounds presented here could aid in the design of echo state networks that would be applicable to control applications where stability is required.

We consider the problem of robust performance analysis when some of the exogenous inputs acting on the system are assumed to be fixed and known, while others are unknown but bounded. In particular, we consider the case where performance is measured by the l∞ norm of the output signals, and the uncertainty on the nominal model is described by LTV perturbations of bounded l∞-induced norm. We first address the special case when all the exogenous inputs are fixed and known. We propose upper and lower bounds for the measure of robust performance. Two upper bounds are derived, which trade off accuracy versus computational expense. Both conditions are much less conservative than what one would obtain from assuming a worst-case exogenous input. We then generalize the conditions to the more general case, where both fixed and worst-case inputs act on the system. All these conditions are readily computable, and yield much less conservative results than one would obtain from applying standard worst-case analysis methods. Copyright © 2005 John Wiley & Sons, Ltd.

Exploitation of the NP hard, mixed μ problem structure provides a polynomial time algorithm that approximates μ with usually reasonable answers. When the problem is extended to the skew μ problem, an extension of the existing method to the skew μ formulation is required. The focus of this paper is to extend the μ upper bound derivation to the skew μ upper bound and show its direct computation by way of an Osborne balancing algorithm and an linear matrix inequality (LMI) algorithm. Copyright © 2005 John Wiley & Sons, Ltd.

Exploitation of the NP hard, mixed µ problem structure provides a polynomial time algorithm that approximates µ with usually reasonable answers. When the problem is extended to the skew µ problem an extension of the existing method to the skew µ formulation is required. The focus of this paper is to extend the µ lower bound derivation to the skew µ lower bound and show its direct computation by way of a power algorithm. Copyright © 2005 John Wiley & Sons, Ltd.

We examine the dynamic and geometric phases of the electron in quantum mechanics using Hestenes' spacetime algebra formalism. First the standard dynamic phase formula is translated into the spacetime algebra. We then define new formulas for the dynamic and geometric phases that can be used in Hestenes' formalism.

Robust control theory is used to design stable controllers in the presence of uncertainties. This provides powerful closed-loop robustness guarantees, but can result in controllers that are conservative with regard to performance. Here we present an approach to learning a better controller through observing actual controlled behaviour. A neural network is placed in parallel with the robust controller and is trained through reinforcement learning to optimize performance over time. By analysing nonlinear and time-varying aspects of a neural network via uncertainty models, a robust reinforcement learning procedure results that is guaranteed to remain stable even as the neural network is being trained. The behaviour of this procedure is demonstrated and analysed on two control tasks. Results show that at intermediate stages the system without robust constraints goes through a period of unstable behaviour that is avoided when the robust constraints are included. Copyright © 2001 John Wiley & Sons, Ltd.

This paper provides a tutorial introduction and overview of analysis and design techniques for systems with both parametric uncertainty and unmodelled dynamics, using the structured singular value, or μ, family of techniques. This includes the analysis/design framework, stability and performance problems, frequency sweeps and state-space tests, continuous-time and discrete-time systems, and computational issues and methods for both analysis and design. The paper also includes connections between the structured singular value and polynomial approaches to handling parametric uncertainty, as well as several examples which illustrate how to cast typical (including polynomial) problems into the μ framework. Copyright © 2001 John Wiley & Sons, Ltd.

The ability to stabilize a system and achieve performance objectives using active feedback control is highly dependent on the location, quality, type, and number of control actuators and sensors. One role of a control engineer is to interact with the system designer to locate, size, and determine the quality of actuators and sensors required for effective control. This paper addresses one of these issues: location of sensors based on closed-loop objectives. A systematic approach, based on H/sub 2/ optimal control design techniques, is developed for sensor selection which achieves desired performance objectives and includes system constraints. This approach is applied to the selection of sensors for active vibration attenuation on the NASA Langley Mini-Mast experimental structure.

In this paper we develop a robust controller design for the Active Mass Driver (AMD) benchmark problem. The design process is based around the D-K iteration procedure for (complex) μ synthesis, together with a balanced truncation procedure to reduce the controller order. The final design is a third-order linear controller, which utilizes only four accelerometer measurements, and has desirable rolloff properties (i.e. small required bandwidth, and a high degree of robustness). Despite the simplicity of the controller, it is able to yield quite good performance, while using only modest control authority.

Single-nucleotide polymorphisms (SNPs) are the most frequent type of variation in the human genome, and they provide powerful tools for a variety of medical genetic studies. In a large-scale survey for SNPs, 2.3 megabases of human genomic DNA was examined by a combination of gel-based sequencing and high-density variation-detection DNA chips. A total of 3241 candidate SNPs were identified. A genetic map was constructed showing the location of 2227 of these SNPs. Prototype genotyping chips were developed that allow simultaneous genotyping of 500 SNPs. The results provide a characterization of human diversity at the nucleotide level and demonstrate the feasibility of large-scale identification of human SNPs.

This paper deals with μ problems involving full-structured (rather than block-diagonal) uncertainty, i.e. uncertainty blocks where each sub-block (or element) may be an independent uncertainty. Rearranging this problem into standard (block-diagonal) form results in an explosion in the required computation, and so a number of researchers have proposed more efficient bounds, in particular those based on non-similarity scaling, for such problems. Here we show how to map such problems into reduced size standard μ problems. The standard bounds applied to the reduced size problem are then shown to be at least as accurate, and require the same computational effort, as earlier techniques, with the added bonus that the standard μ upper bound is convex. Moreover this new approach is applicable in a much more general setting, allowing one to efficiently compute both robust stability and robust performance for problems involving multiple real and complex uncertainty blocks, any number of which may be full-structured.

Many engineering problems can be shown to be equivalent to solving semidefinite programs (SPs), i.e., convex optimization problems involving linear matrix inequalities (LMIs). Powerful computation tools are available for such problems in the finite-dimensional case. However, the problems arising in optimal and robust control theory are often infinite dimensional, and so adequate computation tools are not available. The key to tackling such problems with finite computation tools is to have a primal-dual formulation of the problem without duality gap. In this paper we study infinite-dimensional SPs and present a lifting technique to recast SPs as parameterized linear programs (LPs). This enables the wealth of theoretical tools available for infinite-dimensional LPs to be extended to infinite-dimensional SPs. In particular, we develop some new sufficient conditions for the lack of a duality gap for infinite-dimensional SPs and give an exact characterization of the primal and dual problems for these cases. Both primal and dual problems are formed as infinite-dimensional SP problems, with finite truncations to each giving upper and lower bounds, respectively, on the exact solution to the infinite-dimensional problem. Thus, these results can form the basis of practical computation schemes for infinite-dimensional problems, which require only finite-dimensional computation tools. To illustrate the power of these tools we apply the results to two previously unsolved optimization problems, namely minimizing the l/sub 1/ norm of a closed-loop system subject to bounds on the frequency response magnitude at a finite number of points and/or bounds on the H/sub 2/ norm.

In this paper, we propose a robust control strategy for reducing system frequency deviation, caused by load fluctuation and renewable sources, in a smart microgrid system with attached storage. Frequency and voltage deviations are associated with renewable energy sources because of their inherent variability. In this work, we consider a microgrid where fossil fuel generators and renewable energy sources are combined with a reasonably sized, fast acting battery-based storage system. We develop robust control strategies for frequency deviation reduction, despite the presence of significant (model) uncertainties. The advantages of our approach are illustrated by comparing system frequency deviation between the proposed system (designed via μ synthesis) and the reference system which uses governors and conventional PID control to cope with load and renewable energy source transients. All the simulations are conducted in the Matlab™ and Simulink™ environment.

A number of techniques have been developed in recent years for the analysis and design of controllers which are robust with respect to structured complex uncertainty. In particular the complex μ synthesis procedure has been successfully applied to a number of engineering problems. However the presence of real parametric uncertainty in the problem description substantially complicates matters, so that standard complex μ synthesis techniques are no longer adequate. In this paper we develop a procedure to tackle the mixed (real and complex) μ synthesis problem. This procedure involves a "D,G-K iteration" between computing the mixed μ upper bound and solving an H∞ optimal control problem, and has guaranteed convergence to a local minimum of the (nonconvex) problem. The procedure has been implemented in software, and several controller designs are compared with the corresponding complex μ synthesis designs.

The eight-band superlattice crystal K\ifmmode\cdot\else\textperiodcentered\fi{}p formalism is extended to include the higher-lying antibonding $p$ states perturbatively. The initial 14\ifmmode\times\else\texttimes\fi{}14 Hamiltonian matrix is block diagonalized (or folded down) so that corrections due to these states appear in the modified 8\ifmmode\times\else\texttimes\fi{}8 matrix. Their principal effect is to introduce a finite heavy-hole mass. The use of the eight-band formalism for band-structure and interband optical calculations is validated because other corrections are very small. The approximate wave functions associated with the original 14\ifmmode\times\else\texttimes\fi{}14 Hamiltonian are used to calculate intersubband optical matrix elements. These include new terms, present in crystals lacking inversion symmetry, which increase the magnitude of conduction intersubband absorption by about three orders of magnitude for in-plane polarized (TE) photons, while leaving absorption due to growth-axis polarized (TM) photons relatively unaffected. The TE absorption in narrow $\mathrm{GaAs}/{\mathrm{Al}}_{x}{\mathrm{Ga}}_{1\ensuremath{-}x}\mathrm{As}$ quantum wells (QW's) is shown to be observable. However, use of the $f$-sum rule and the cyclotron-resonance-determined conduction-band effective-mass anisotropy indicate an upper limit of about 20% relative to TM absorption. Comparison with the measured TM absolute absorption coefficient for a system used for quantum-well infrared photoconductors (QWIP's) yields excellent agreement. The conductivity sum rule involving these intersubband transitions is used to confirm the observed broadening associated with very small QW width fluctuations for this system.

In this paper we propose an approach for optimally selecting generators, interconnected to work together as a microgrid, based on solving the associated optimal dispatch problems by utilizing the Karush-Kuhn-Tucker (KKT) conditions. The approach ensures that the power demand is met at all times, whilst also minimizing the fuel consumption. As a result, the microgrid efficiency (in economic terms) is significantly improved. Microgrid stability, in terms of handling rapid load variations, is improved as well, since spinning reserve and swing capacity constraints are built into the algorithm. Furthermore, generator life is also addressed via rating constraints on each generation unit. Finally, startup and cool down costs are also considered, making the generator selection algorithm more realistic and closer to real-world practice.

The United States Marine Corps (USMC) utilizes Forward Operating Bases (FOBs) which employ multiple generation units, typically ranging from 2-200 kW in size, and primarily powered by JP-8 (similar to diesel fuel). It is often true that logistical support to deliver fuel is both expensive and dangerous. In typical deployment scenarios today, generation units are attached to a small group of loads, and are frequently operated far from their optimum efficiency. Future plans include combining multiple generators into a single microgrid. Since the generators have different fuel consumption curves, the dispatch of load to generation must account for unit capabilities, fuel consumption, and additional constraints, such as upper and lower operating limits. In addition, provision must also be made to handle fast load transients, typically with the use of “swing” generators. Furthermore, spinning reserve must also be available to cope with unanticipated load variations. This paper utilizes the Karush-Kuhn-Tucker (KKT) conditions to develop a scheme for economic dispatch of load to generators. The swing generator(s), base load generator upper and lower operating limits, and spinning reserve form constraints on the dispatch algorithm. The goal is to dispatch generators to minimize fuel consumption, while maintaining operating constraints for reliability.

The performance characteristics of type‐II InAs/InxGa1−xSb superlattices for long and very long‐wave infrared detection are discussed. This system promises benefits in this wavelength range over conventional technology based on Hg1−xCdxTe, in part because of suppressed band‐to‐band Auger recombination rates which lead to improved values of detectivity. The formalism for calculating Auger rates in superlattices is developed and the physical origin of Auger suppression in these systems is discussed. Accurate K⋅p band structures are used to obtain radiative, electron–electron, hole–hole, and band‐to‐band Auger rules, as well as shallow trap level assisted Auger recombination rates for photodiodes. Theoretical limits for high temperature operation of ideal photovoltaic detectors are presented and compared with HgCdTe.

In this paper we study robust stability, in the lp sense, against structured perturbations of bounded lp norm. The perturbations may be either linear time-varying, nonlinear time-varying, or nonlinear time-invariant. We develop exact tests for robust stability for each case in terms of convex optimization problems. These results are extended to solve the corresponding robust performance problems, with performance measured via the induced lp norm.

The lightly damped nature of flexible structures can lead to controllers that are highly sensitive to modeling errors in structural natural frequencies and damping levels. An approach to directly incorporating these modeling errors into the control design process is presented that leads to less sensitive and more robust controllers with improved performance/This approach is used to synthesize controllers for the NASA Langley Mini-Mast experimental structure. The resulting designs obtain good performance in the presence of significant modeling errors in the structural natural frequencies.

Robustness problems involving real parametric uncertainty can be reformulated as mixed μ problems, where the block-structured uncertainty description is allowed to contain both real and complex blocks. Upper and lower bounds for the mixed μ problem have recently been developed, and this paper examines the computational aspects of these bounds. In particular a practical algorithm is developed to compute the bounds. This has been implemented as a Matlab function (m-file), which is currently available in conjunction with the μ-Tools toolbox. Some results from our extensive numerical experience with the algorithm are presented. The algorithm performance is very encouraging, in terms both of accuracy of the resulting bounds, and of growth rate in required computation with problem size. In particular it appears that one can handle medium-size problems (fewer than 100 perturbations) with reasonable computational requirements.

Two first order nonlinear controllers are introduced: partial state setting and error modulated state setting. Both controllers smoothly combine feedforward steady state predictions with integral control. In both controllers the mixing is controlled though the use of a single gain that determines how aggressively the steady state predictions are used. Steady state error bounds are presented for each controller. For error modulated state setting control steady state errors can be eliminated if the maximum expected error in the steady state predictions is known. Both controllers are demonstrated to improve the transient response of a simulated nonlinear second order variable gain plant when compared to a well tuned proportional plus integral controller.Copyright © 2012 by ASME

As smart grids introduce profound changes in the operation of the electric power industry, the need for efficient and robust resource allocation (RA) algorithms arises, especially due to the increasingly stochastic nature of availability of highly dispersed resources. A framework for solving the smart grid RA (SGRA) problem using a heuristic approach such as a genetic algorithm is presented in this paper. Similar challenges exist in resource allocation in the realm of computing. A comparison is drawn between SGRA and computing RA. Its application to a multi-agent-based distribution management system, used as an environment model, is also proposed. A path forward concludes the paper.

Energy Management Systems monitor and control the behavior of an underlying system, allowing it to fulfill its primary purpose at the same time balancing across multiple constraints including cost optimization, system reliability, and environmental considerations. Examples of such systems include building energy management systems, where electrical and HVAC resources are optimized to deliver human comfort; microgrid management, where generators, energy storage, and renewable energy resources are balanced with loads to provide electrical service; and datacenter energy management, where electrical and cooling resources are balanced to provide a reliable computing resource. This paper analyzes these disparate energy management systems and defines a model for resource allocation that can be used for these and other energy management systems. The paper then shows how this model can be used across these domains.

The United States Marine Corps (USMC) utilizes Forward Operating Bases (FOBs) which employ multiple generation units, primarily powered by JP-8 (similar to diesel fuel). It is often true that logistical support to deliver fuel is both expensive and dangerous. On the other hand, the generation units deployed by USMC range from 2 KW to over 200 KW, with very different input-output characteristics. In order to minimize fuel usage, a more sophisticated dispatch approach is needed. This paper applies the Karush-Kuhn-Tucker (KKT) conditions for optimality (in the sense of minimizing the fuel consumption) to develop an approach to economically dispatch generators. Simulation results based on the KKT method are compared with several existing dispatch methods, showing that our approach reduces the fuel usage compared to current standard methods.

We have measured the photoluminescence (PL) of HgTe/CdTe superlattices under high hydrostatic pressures up to 30 kbar at liquid-nitrogen temperature. We observed several photoluminescence peaks with energies ranging from \ensuremath{\sim}130 to \ensuremath{\sim}700 meV. The most prominent peak at \ensuremath{\sim}130 meV, which has been attributed to the recombination across the superlattice band gap, moves higher in energy with a pressure coefficient of \ensuremath{\lesssim}1 meV/kbar. Other peaks, whose origins are not well established, have higher energies and their pressure coefficients are in the range of 0--2 meV/kbar. A calculation based on the envelope-function approximation gives a pressure coefficient of at least \ensuremath{\sim}6.5 meV/kbar for the superlattice band gap. This is far outside the error bars of the measured pressure dependence of the main peak, \ensuremath{\lesssim}1 meV/kbar. Varying the input parameters for the calculation, including the valence-band offset, changes the result of the calculation by less than 10%. Possible explanations for this disagreement, including a modification of the current model of HgTe/CdTe superlattice bands and a reinterpretation of the PL peaks, are examined.

Quantitative calculations are reported of both band-to band Auger and radiative recombination lifetimes in thin-layered type II InxGa1−x Sb/InAs superlattices with energy gaps in the 5–17 μm range, using accurate band structure and numerical techniques. Results for an 11 μm superlattice are compared with similar calculations for bulk HgCdTe and a HgTe/CdTe superlattice having the same energy gap. The results show the n-type Auger rates to be comparable and the p-type rates to be suppressed by three orders of magnitude in some experimentally realizable structures. Thus, well fabricated III–V superlattices appear to be excellent candidates as a new class of infrarer detectors.