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The presence of condensate inside the dryer cylinders results in a significant increase in the power and torque required until the condensate transitions to the rimming stage. This paper evaluates the effect of the condensate during the starting of a dryer section using the experimental curves reported by several authors, as well as a dimensionless model of the input power as a function of the condensate quantity and roll velocity. Through the evaluation of a nine-cylinder dryer section for various condensate quantities and varying number of cylinders containing condensate, a comparison is made to the power requirements of several actual paper machine dryer sections undergoing various starting procedures. These comparisons indicate that if the percent condensate (\condensate volume/full cylinder volume \times 100) in the range of 0.5%–1%, or greater, is nominally assumed to be present, then the sizing of the section drive capacity must take into consideration the additional indrive load resulting from this condensate. Options are proposed to consider the effect of the condensate without increasing the Technical Association of the Pulp and Paper Industry recommended drive capacity of dryer sections. [ABSTRACT FROM PUBLISHER]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

This paper studies the subcarrier allocation and precoder design problem for downlink multiple-input-multiple-output-orthogonal frequency-division multiple access systems. The criterion is to maximize the energy efficiency (EE) of the system. We adopt the time-sharing concept to allocate each subcarrier to one user, which results in a criterion similar with that of rate maximization. However, this approach does not make it possible to solve the problem for any value of the transmit power. We found that there exist gaps along the direction of total transmit power when the user selection switches for each subcarrier, which leads to a discontinuous EE function, for which quasi-concavity has to be checked. We first show that the sufficient conditions obtained for the subcarrier allocation approach are the optimal ones among all possibilities with the same transmit power. Then, we show that the proposed subcarrier allocation results in a discontinuous and quasi-concave EE function. We also give an upper bound of the EE function when the proposed sufficient conditions are not met. Finally, we propose an algorithm to find the maximal EE and its performance is illustrated by means of numerical results. [ABSTRACT FROM PUBLISHER]

Recently, electric power consumption has increased rapidly owing to the development of electric devices, and thus environmental problems such as global warming have come to the fore. Therefore, developed and developing countries are preparing various measures to prevent global warming. Minimum energy performance standards (MEPS) are one of the representative measures. MEPS is an efficiency regulation for induction motors that limits the minimum efficiency with regard to the power of the induction motors, in order to save energy. Studies have been conducted on induction motors to improve the efficiency and to satisfy MEPS. However, It is very difficult to satisfy the efficiency of induction motor for MEPS. Therefore, synchronous reluctance motors (SynRMs) are actively being studied to replace induction motors. In this paper, a SynRM is designed using a Kw-map. Moreover, to improve the output characteristics, the rib thickness of the SynRM is optimized using the response surface method. Finally, the validity of the design method, proposed in this paper is verified with a real test using the prototype. [ABSTRACT FROM AUTHOR]

Modern electric grid usages extensive non-linear loads leading to the increased harmonic current injection in the system. This paper proposes a data-driven algorithm based on a sparse decomposition approach with the Overcomplete Hybrid Dictionary (OHD) and provides in-depth mathematical analysis to detect and mitigate harmonics in emerging electric power systems. The sparse decomposition method is widely used in image processing applications with significant advantages to big-data applications. However, its application is not properly addressed in electric grids’ applications despite massive data generation in a smart grid environment. Hence, in this paper, the performance evaluation of a sparse decomposition-based algorithm using a greedy approach is proposed and carried out in a real-time simulation environment for Distribution Static Compensator (DSTATCOM) application for the real-time detection, classification, and mitigation of PQ events to show its suitability and effectiveness. Finally, the experimental results on a small-scale laboratory setup are presented to validate the effectiveness of the proposed sparse-based control algorithm for DSTATCOM in real-time applications. The comparative result shows that the proposed sparse-based method is advantageous for off-line PQ signal processing and capable of real-time detection, classification, and control of the power apparatus for harmonics mitigation. [ABSTRACT FROM AUTHOR]

Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods for load forecasting relied on historical records from one single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multitask learning (MTL), has been developed and has the potential for load forecasting. By MTL, the electricity consumption data from multiple communities can be fused to improve forecasting accuracy. However, appropriate modeling of the relatedness to enable the between-community knowledge transfer remains a challenge. This paper proposes an improved MTL algorithm for a Bayesian spatiotemporal Gaussian process model (BSGP) to characterize the relatedness among the different residential communities. It hypothesizes on the similar impacts of environmental and traffic conditions on electricity consumption in improving short-term load forecasting. Furthermore, this paper proposes a low-ranked dirty model along with an iterative algorithm to improve the learning of model parameters under an MTL framework. This paper uses a real-world case study from two residential communities in Tallahassee, Fl, USA, to demonstrate the method effectiveness. The proposed method significantly outperforms state-of-the-art forecasting methods and effectively captures the relatedness to provide between-community knowledge transfer compared with other MTL methods. [ABSTRACT FROM AUTHOR]

In this paper, an ultralow-power true single-phase clocking flip-flop (FF) design achieved using only 19 transistors is proposed. The design follows a master–slave-type logic structure and features a hybrid logic design comprising both static-CMOS logic and complementary pass-transistor logic. In the design, a logic structure reduction scheme is employed to reduce the number of transistors for achieving high power and delay performance. Despite its circuit simplicity, no internal nodes are left floating during the operation to avoid leakage power consumption. In this design, a virtual $V_{{\mathrm{DD}}}$ design technique, which facilitates a faster state transition in the slave latch, is devised to enhance time performance. In circuit implementation, transistor sizes are optimized with respect to the power-delay product (PDP). A TSMC 90-nm CMOS process was selected as the implementation technology. In this paper, the performance levels of seven FF designs were compared. The timing parameters of each FF were first characterized. Post-layout simulation results indicated that the proposed design excelled in various performance indices such as PDP, clock-to-Q delay, average power consumption, and leakage power consumption. Moreover, the design was determined to have the smallest layout area. Compared with the conventional transmission-gate-based FF design, the PDP improvement in the proposed design was up to 63.5% (at 12.5% switching activity) and the area saving was approximately 10%. Further simulations on process corners, supply voltage settings, and working frequencies were conducted to study the design reliability. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic pricing mechanism in the industrial park with demand response programs. A Lagrangian relaxation based dynamic pricing model for electricity and thermal coupled industrial park is formulated, taking into account energy balance, feeder exchange and other systems operating constraints. Considering two-markets clearing mechanism and two types of demand response programs, a dynamic prices prediction model is proposed by long short-term memory (LSTM) technique. Based on the prediction model, we proposed a real-time prices spike detection model for industrial park, which can detect prices spike hourly by history data and give rolling prices spike warning for next short-term operating horizon. Simulation experiments validate the theoretic results and show the effectiveness of the dynamic prices spike detection model. Note to Practitioners—This paper focuses on the dynamic pricing mechanism and prices spike detection for the customers in the industrial park. We improve the pricing model based on the Lagrangian relaxation method and develop a dynamic prices prediction model to handle the uncertainty in real-time. Furthermore, we develop a prices spike detection mechanism, which can achieve rolling detect whether the electricity and thermal prices may exceeded the threshold in the next short-term operating horizon. This technique can give the customers a prices spike early warning service and let them to reschedule their own strategy to minimize their operation cost with respect to the uncertainties in the energy price. Experimental results show that the proposed prices spike detection mechanism can issue spike warnings correctly in most supply-demand mismatching cases. [ABSTRACT FROM AUTHOR]

This paper presents analytic and experimental results for high-efficiency GaN HEMT class-F and inverse class-F (class-F−1) synchronous rectifiers. The rectifier designs are based on time-reversed duals of corresponding class-F amplifiers. A detailed analysis of the power efficiency of a class-F rectifier is presented. The analysis shows that, unlike the amplifier dual, the rectifier configuration does not have significant overlap loss because of device constraints in terms of operation in quadrant III. Consequently, the power efficiency of the class-F rectifier is slightly higher than the amplifier circuit under equal source power conditions. Experimental measurements are shown, which compare the rectification efficiency of a class-F rectifier with class-F−1 rectifiers. It is shown that operation of the rectifiers under back-off conditions mimics the power efficiency of an amplifier with envelope tracking. Based on this paper, it is concluded that the class-F−1 mode has better rectification efficiency compared with that of class-F, both at peak power and under back-off conditions. [ABSTRACT FROM PUBLISHER]

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach. [ABSTRACT FROM AUTHOR]

Integrated antennas have become the attractive solution as the electromagnetic (EM) interface for mm-Wave and terahertz ICs. However, on-chip antennas lying at the interface between two different dielectrics (such as air and substrate) can channel most of its power into multiple nonradiative surface-wave modes, reducing efficiency drastically. In this paper, we consider the following problem: given a dielectric substrate, what is the theoretical optimal 2-D surface-current configuration that collectively suppresses surface waves and maximizes radiation efficiency with the desirable radiation pattern? This paper also discusses demonstrative examples of a circuit-EM codesign approach to realize the approximation of such current configurations. Measurement results of radiating arrays in CMOS at mm-Wave frequencies (250–300 GHz) are presented and compared with theoretical predictions. [ABSTRACT FROM PUBLISHER]

This paper investigates how to precisely transform images displaying color on organic light-emitting diodes (OLEDs) in real time for the purpose of energy-saving that meets the personal viewing quality requirements based on the structural similarity (SSIM) assessment. These methods could be applied to real-time energy-saving transformation of any displaying media, including video playback on OLEDs, and it will save up to 20% of the displaying power consumption based on the predicted SSIM = 0.9, which has very good image quality after the transformation. [ABSTRACT FROM PUBLISHER]

Domestic electric water heaters (DEWHs) can provide operational flexibility for load control due to their energy storage capacity. Load forecasting for aggregated DEWHs is important for providing information of baseline load and controlling electricity demand profile without negative impact to the normal end use. Advanced metering infrastructures nowadays provide more possibilities to further enhance forecasting with bottom-up method. This article proposes a bottom-up forecasting with Markov-based error reduction method to predict power consumption of aggregated DEWHs for multiple forecast horizons. DEWHs are randomly divided into small aggregations, whose power consumption is forecasted by independent forecast engines. In this paper, the engines are K-means and wavelet decomposition-based neural networks. After summing all forecasting of small aggregations up, a new Markov-based error reduction method is proposed to extract features in residuals and mitigate forecasting error accumulation introduced by the summation, providing opportunities to further improve forecasting accuracy for the total DEWH load. Differing from traditional Markov-based error reduction, two new compensation parameters (compensation coefficient, and compensation threshold) are proposed. They are determined by using particle swarm optimization algorithm. Experiments on real and simulated DEWH loads verified the effectiveness of the proposed forecasting method. The proposed method improved the forecast accuracy over selected benchmark algorithms by about 20% to 80%, according to four performance metrics: mean absolute error, mean absolute percentage error, root-mean-square error, normalized form RMSE. The aggregation effects on performance were also analyzed in theory and tested with simulated DEWHs, providing a good indication of the forecast dependence on the aggregation size. [ABSTRACT FROM AUTHOR]

In this paper, we identify the level shifter (LS) for voltage up-conversion from the ultralow-voltage regime as a key application domain of tunnel FETs (TFETs). We propose a mixed TFET–MOSFET LS design methodology, which exploits the complementary characteristics of TFET and MOSFET devices. Simulation results show that the hybrid LS exhibits superior dynamic performance at the same static power consumption compared with the conventional MOSFET and pure TFET solutions. The advantage of the mixed design with respect to the conventional MOSFET approach is emphasized when lower voltage signals have to be up-converted, reaching an improvement of the energy-delay product up to three decades. When compared with the full MOSFET design, the mixed TFET–MOSFET solution appears to be less sensitive toward threshold voltage variations in terms of dynamic figures of merit, at the expense of higher leakage variability. Similar results are obtained for four different LS topologies, thus indicating that the hybrid TFET–MOSFET approach offers intrinsic advantages in the design of LS for voltage up-conversion from the ultralow-voltage regime compared with the conventional MOSFET and pure TFET solutions. [ABSTRACT FROM PUBLISHER]

At the gigahertz range of frequencies, contribution of combinational logic upsets has increased significantly to the overall single-event (SE) upset rate (SER) of sequential circuits. Most approaches for modeling and/or predicting logic SER are either pure simulation based or pure experiment based. Simulation-based approaches need a lot of computing power. Experiment-based approaches require fabrication of actual circuits. This paper presents an empirical method that uses experimental data from simple test structures for estimating SE vulnerability of any combinational logic circuit. Estimated logic SEU cross section matches well with the measured logic SEU cross section. Estimated logic SEU cross section results obtained with the proposed method are within $2\times $ average error when compared to the experimentally measured logic SEU cross section. This method only needs to be calibrated once for use at a given technology node. [ABSTRACT FROM PUBLISHER]

This paper presents a balanced frequency doubler with 6.5-dBm peak output power at 204 GHz in 130-nm SiGe BiCMOS technology ( fT/f\max =210 /250 GHz). To convert the single-ended input signal to a differential signal for balanced operation, an on-chip transformer-based balun is employed. Detailed design procedure and compensation techniques to lower the imbalance at the output ports, based on mixed mode S parameters are proposed and verified analytically and through electromagnetic simulations. The use of optimized harmonic reflectors at the input port results in a 2-dBm increase in output power without sacrificing the bandwidth of interest. The measured conversion loss of the frequency doubler is 9 dB with 6-dBm input power at 204-GHz output. The measured peak output power is 6.5 dBm with an on-chip power amplifier stage. The 3-dB output power bandwidth is measured to be wider than 50 GHz (170–220 GHz). The total chip area of the doubler is 0.09 mm2 and the dc power consumption is 90 mW from a 1.8-V supply, which corresponds to a 5% collector efficiency. [ABSTRACT FROM PUBLISHER]

The Internet of Things (IoT) refers to a pervasive presence of interconnected and uniquely identifiable physical devices. These devices’ goal is to gather data and drive actions in order to improve productivity, and ultimately reduce or eliminate reliance on human intervention for data acquisition, interpretation, and use. The proliferation of these connected low-power devices will result in a data explosion that will significantly increase data transmission costs with respect to energy consumption and latency. Edge computing reduces these costs by performing computations at the edge nodes, prior to data transmission, to interpret and/or utilize the data. While much research has focused on the IoT’s connected nature and communication challenges, the challenges of IoT embedded computing with respect to device microprocessors has received much less attention. This paper explores IoT applications’ execution characteristics from a microarchitectural perspective and the microarchitectural characteristics that will enable efficient and effective edge computing. To tractably represent a wide variety of next-generation IoT applications, we present a broad IoT application classification methodology based on application functions, to enable quicker workload characterizations for IoT microprocessors. We then survey and discuss potential microarchitectural optimizations and computing paradigms that will enable the design of right-provisioned microprocessors that are efficient, configurable, extensible, and scalable. This paper provides a foundation for the analysis and design of a diverse set of microprocessor architectures for next-generation IoT devices. [ABSTRACT FROM PUBLISHER]

Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational effort of today’s CNNs requires power-hungry parallel processors or GP-GPUs. Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption significantly. Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage. This prevents the adoption of CNNs in future ultralow power Internet of Things end-nodes for near-sensor analytics. Recent algorithmic and theoretical advancements enable competitive classification accuracy even when limiting CNNs to binary (+1/−1) weights during training. These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as reducing I/O bandwidth and storage. In this paper, we present an accelerator optimized for binary-weight CNNs that achieves 1.5 TOp/s at 1.2 V on a core area of only 1.33 million gate equivalent (MGE) or 1.9 mm 2 and with a power dissipation of 895 $\mu$ W in UMC 65-nm technology at 0.6 V. Our accelerator significantly outperforms the state-of-the-art in terms of energy and area efficiency achieving 61.2 TOp/s/W@0.6 V and 1.1 TOp/s/MGE@1.2 V, respectively. [ABSTRACT FROM PUBLISHER]

Electric motors and the drive systems approximately consume half of the global electricity consumption. In the European Union (EU), electric motors and the drive systems are estimated to account for about 70% of all industrial electricity consumption. Therefore, mandatory applications accrue from the legislation of governments or unions like the EU for customs and market surveillance or eco-design requirements. The mandatory applications and the global market size find out the necessity of efficiency measurement. There are several compliance and precompliance efficiency test methods in the literature. The compliance tests should execute reliable, accurate, and reproducible results. The purpose of this paper is to define a simple field method that will be used for measuring and determining in-service efficiency at industrial conditions. The proposed method in this paper is an inexpensive and easy method that would help to energy managers and engineers to make correct decision in replacing inefficient motors with efficient and new ones. In this paper, a novel induction motor-efficiency model function has been proposed based on the International Electrotechnical Commission 60034-2-1 standard equations, and a new field test procedure has been used in efficiency estimation. The new efficiency model function variables and constants are determined from the regression analysis, which depends on 86 tests of 437 different load points’ efficiency data. Furthermore, the proposed field test procedure, equations, and compliance test methods are tested on a sample motor in laboratory, and the results are compared to each other. [ABSTRACT FROM PUBLISHER]

The 12 papers in this special issue focus on energy efficient computing applications and technologies. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of a Qualcomm Hexagon digital signal processor (DSP) in a 28 nm high-κ metal gate technology. The DSP is a multi-threaded very-long- instruction-word (VLIW) machine optimized for low leakage and energy efficiency. It uses a clock distribution network, clock gating cells, and pulsed latches that are optimized for low switching energy. The processor can be powered using a low-dropout (LDO) voltage regulator or a head switch. It operates from 255 MHz at 0.60 V to 1.24 GHz at 1.05 V. When operating from the LDO, the power consumption of the core can be as low as 58 µW/MHz, which is two to three times lower than comparable cores optimized for ultra-low voltage operation. [ABSTRACT FROM PUBLISHER]

This paper introduces reconditioning: a novel systematic technique for reducing unnecessary power consumption of asynchronous gate-level netlists, which involves the optimal reordering of conditional communication and logic primitives. Our technique is applicable to asynchronous circuits with handshaking protocols that encode data and control together, in particular, quasi delay insensitive and 1-of- N handshaking circuits. Both an optimal integer linear program (ILP) and a fast heuristic algorithm are presented. We show that our ILP is feasible for moderate size circuits and our heuristic algorithm scales to much larger circuits, completing in seconds on circuits with tens of thousands of gates. Our experimental results show power improvement highly depends on the structure of the circuit but can often be above 26% with typically less than 5% area overhead. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new capacity-approaching code, namely, parallel-concatenated (PC)-Low Density Parity Check (LDPC) convolutional code that is based on the parallel concatenation of trellis-based quasi-cyclic LDPC (TQC-LDPC) convolutional codes. The proposed PC-LDPC convolutional code can be derived from any QC-LDPC block code by introducing the trellis-based convolutional dependency to the code. The capacity-approaching PC-LDPC convolutional codes are encoded through parallel concatenated trellis-based QC recursive systematic convolutional (RSC) encoder (namely, QC-RSC encoder) that is also proposed in this paper. The proposed PC-LDPC convolutional code and the associated encoder retain a fine input granularity on the order of the lifting factor of the underlying block code. We also describe the corresponding trellis-based QC maximum a posteriori probability (namely, QC-MAP) decoder that efficiently decodes the PC-LDPC convolutional code. Performance and hardware implementation results show that the PC-LDPC convolutional codes with the QC-MAP decoder have two times lower complexity for a given bit-error-rate (BER), signal-to-noise ratio, and data rate, than conventional QC-LDPC block codes and LDPC convolutional codes. Moreover, the PC-LDPC convolutional code with the QC-MAP decoder outperforms the conventional QC-LDPC block codes by more than 0.5 dB for a given BER, complexity, and data rate and approaches Shannon capacity limit with a gap smaller than 1.25 dB. This low decoding complexity and the fine granularity make it feasible to efficiently implement the proposed capacity-approaching PC-LDPC convolutional code and the associated trellis-based QC-MAP decoder in next generation ultra-high data rate mobile systems. [ABSTRACT FROM PUBLISHER]

The electrostatic actuation method has not been used widely in bimorph cantilevers despite its advantage of large displacement and low power consumption, mostly owing to the fact that it is limited to just two stable positions: 1) initial curled; and 2) final flat positions. In this paper, we first demonstrate a bimorph cantilever actuator having multiple steady-state positions in between those two boundary positions, accomplished by simply dividing the bottom electrode. We found that the number of steady-state positions and the steady-state tip heights can be controlled by the design of the bottom electrode. In our test experiment, the steady-state bimorph cantilever tip heights were 48 \mu \textm at 0 V, 28 \mu \textm at 21 V, 7 \mu \textm at 25 V, and 0 \mu \textm (flat) at 29 V, respectively. In order to relieve low reliability of the electrostatic bimorph cantilever, we devised non-contact actuation method and demonstrated that the lifetime of the electrostatic bimorph actuator greatly increased. This paper will pave the way to utilize bimorph cantilever actuators with greater versatility. [2016-0081] [ABSTRACT FROM AUTHOR]

A prior knowledge of residential load demand is critical for power system operations at the distribution level, such as economic dispatch, demand response and energy storage schedule. However, as residential customers perform more casual and active consumption behaviors, prediction of such highly volatile loads can be much harder. Owing to the development of sensor technology, micrometeorological data can be sampled with a high geographic resolution. Those data that represent the weather condition on the land surface show a strong relationship to the residential load evidently, whereas it remains unsolved on how to fully utilize those great number of datasets. This paper proposes a day-ahead probabilistic residential load forecasting method based on a novel deep learning model, named convolutional neural network with squeeze-and-excitation modules (CNN-SE), and micrometeorological data. The model can employ multi-channel input data with dissimilar weights, suitable for analyzing massive relevant input factors. A feature extraction method is adopted for customer consumption pattern based on sparse auto-encoder (SAE), which can help correct probabilistic forecasting results. A case study that covers 8 residential communities and 18 micrometeorological sites is conducted to validate the feasibility and accuracy of the proposed hybrid method. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear economic model predictive control based energy management of a multi-energy microgrid is presented. The microgrid is composed of the district heating system with a cogeneration power plant supplying both the heat and the electricity demand of residential buildings with photovoltaic sources. Furthermore, access to liberalized heat and electricity market is assumed. This kind of environment requires consideration of the economical aspects, the technical aspects, the safety/quality of supply, and consumer preferences. The major contributions introduced in the presented approach are the following. A nonlinear formulation is adopted to capture the nonlinear thermal models and the nonlinear algebraic model of the heat supply network. The second major contribution is the economic character of the objective following the assumption of a liberalized market environment. Such formulation accounts for the specifics of the energy market organization. Finally, the stochastic nature of the planning problem is introduced through a practical robust formulation. The results obtained on a representative case study clearly show the benefits of the proposed approach compared to the traditional energy management approach. [ABSTRACT FROM AUTHOR]

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades. [ABSTRACT FROM AUTHOR]

In this paper, a reverse carry propagate adder (RCPA) is presented. In the RCPA structure, the carry signal propagates in a counter-flow manner from the most significant bit to the least significant bit; hence, the carry input signal has higher significance than the output carry. This method of carry propagation leads to higher stability in the presence of delay variations. Three implementations of the reverse carry propagate full-adder (RCPFA) cell with different delay, power, energy, and accuracy levels are introduced. The proposed structure may be combined with an exact (forward) carry adder to form hybrid adders with tunable levels of accuracy. The design parameters of the proposed RCPA implementations and some hybrid adders realized utilizing these structures are studied and compared with those of the state-of-the-art approximate adders using HSPICE simulations in a 45-nm CMOS technology. The results indicate that employing the proposed RCPAs in the hybrid adders may provide, on average, 27%, 6%, and 31% improvements in delay, energy, and energy-delay-product while providing higher levels of accuracy. In addition, the structure is more resilient to delay variation compared to the conventional approximate adder. Finally, the efficacy of the proposed RCPAs is investigated in the discrete cosine transform (DCT) block of the JPEG compression and finite-impulse response (FIR) filter applications. The investigation reveals 60% and 39% energy saving in the DCT of JPEG and FIR filter, respectively, for the proposed RCPAs. [ABSTRACT FROM AUTHOR]

The regional energy management and optimized operating strategies of electric vehicles (EVs) and battery swapping station (BSS) are proposed in this paper based on smart microgrid according to the effects of the utility grid caused by uncoordinated charging of EVs and BSS. A price-incentive model is utilized to generate the management strategy to coordinate the charging of EVs and BSS to minimize the total cost of EVs and maximize the profit of BSS in grid-connected mode. In islanded mode, based on the power balance between renewable electric sources and loads, the fuzzy control method is applied to produce the service price of EV according to its state of charge. Combined with the interruptible-load scheduling, the energy management and dispatch of EVs and BSS are optimized to minimize the operational cost and maximize the benefit of islanded microgrid. The main optimization problems are formulated as a cost minimizing problem and a profit maximizing one, which are implemented in A Modeling Language for Mathematical Programming. The effectiveness of the proposed strategies for the optimized operation of EVs and BSS is validated by case studies. The approach given in this paper also provides ideas about the optimal operation of a future smart grid with the booming of EVs. [ABSTRACT FROM AUTHOR]

This paper presents a low-power coordinate rotation digital computer (CORDIC)-based reconfigurable discrete cosine transform (DCT) architecture. The main idea of this paper is based on the interesting fact that all the computations in DCT are not equally important in generating the frequency domain outputs. Considering the importance difference in the DCT coefficients, the number of CORDIC iterations can be dynamically changed to efficiently tradeoff image quality for power consumption. Thus, the computational energy can be significantly reduced without seriously compromising the image quality. The proposed CORDIC-based 2-D DCT architecture is implemented using 0.13 \mum CMOS process, and the experimental results show that our reconfigurable DCT achieves power savings ranging from 22.9% to 52.2% over the CORDIC-based Loeffler DCT at the cost of minor image quality degradations. [ABSTRACT FROM AUTHOR]

This paper deals with the setting-parameter optimization procedure for a multiphase induction heating system considering transverse flux heating. This system is able to achieve uniform static heating of different thin/size metal pieces without movable inductor parts, yokes, or magnetic screens. The goal is reached by the predetermination of the induced power-density distribution using an optimization procedure that leads to the required inductor supplying currents. The purpose of the paper is to describe the optimization program with the different solution obtained and to show that some compromise must be done between the accuracy of the temperature profile and the energy consumption, with the calculation of the losses. [ABSTRACT FROM PUBLISHER]

A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users’ benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The utilization of demand response flexibility has become a significant method to cope with the intermittence of renewable energy sources in distributed systems. This paper proposed a new pricing method for demand response resources managed by a distribution system aggregator, which is deduced from analyzing the operating revenue within the timescale from hours to years. In the proposed model, the hourly decision-making of an aggregator is formulated as a newsvendor model and uncertainties in the long-term decisions are modelled by a backward valuation process. It maximizes the benefit of an aggregator by considering the price and quantity uncertainties of distributed load/generation in day-ahead and real-time wholesale electricity markets. Meanwhile, the coexistence of controllable and uncontrollable loads is also considered, where the former refers to electricity consumption from end-users who are equipped with smart devices for energy management, and the latter load demand of passive end-users who have no willingness or capability to participate in the demand response schemes. Finally, numerical studies are carried out to demonstrate the feasibility and effectiveness of the developed model and methods, and the impacts of active end-user percentage on the aggregator operation under the proposed pricing method are also compared and illustrated. [ABSTRACT FROM AUTHOR]

This paper studies the feasibility of integrating a community energy storage (CES) system with rooftop photovoltaic (PV) power generation for demand-side management of a neighbourhood while maintaining the distribution network voltages within allowed limits. To this end, we develop a decentralized energy trading system between a CES provider and users with rooftop PV systems. By leveraging a linearized branch flow model for radial distribution networks, a voltage-constrained leader-follower Stackelberg game is developed wherein the CES provider maximizes revenue and the users minimize their personal energy costs by trading energy with the CES system and the grid. The Stackelberg game has a unique equilibrium at which the CES provider maximizes revenue and the users minimize energy costs at a unique Nash equilibrium. A case study, with realistic PV power generation and demand data, confirms that the energy trading system can reduce peak energy demand and prevent network voltage excursions, while delivering financial benefits to the users and the CES provider. Further, simulations highlight that, in comparison with a centralized system, the decentralized energy trading system provides greater economic benefits to the users with less energy storage capacity. [ABSTRACT FROM AUTHOR]

This paper reports the integration of vanadium dioxide (VO2) thin films in a microelectromechanical systems (MEMS) mirror device, where the actuation is mainly due to the solid-solid phase transition of VO2. The fabrication process described in this paper provides the details that will enable the integration of VO2 thin films at any step during the fabrication of rather complex MEMS devices. The present VO2-based MEMS mirror device is operated electro-thermally through integrated resistive heaters, and its behavior is characterized across the phase transition of VO2, which occurs at a temperature of ~68 °C and spans about 10 °C. The maximum vertical displacement of the mirror platform is 75 \mu \mathrm m and it occurs for an input voltage of 1.1 V. This translates to an average power consumption of 6.5 mW per mirror actuator and a total power consumption of 26.1 mW for the entire device. The studies included in this paper are key for future device improvements and further development of MEMS mirror actuation technology, which could include the use of the hysteresis of VO2 for programming tilting angles in MEMS mirrors. [2016-0016] [ABSTRACT FROM PUBLISHER]

Electric vehicles will become a popular mode of travel when petroleum prices increase and global warming intensifies. However, the short driving range of electric vehicles remains a challenge. To avoid battery depletion, navigation systems route electric vehicles to surrounding charging stations for battery exchange or recharge. Nonetheless, zero inventories and occupied sockets may result in variable charging time and longer travel time. Conventional navigation approaches have been unable to respond to variable charging time because these approaches do not consider the charging times and energy consumption of electric vehicles. Navigation systems can manage shared information, traffic, and battery life, and can improve the mileage per kilowatthour of electric vehicles. In this paper, an electric-vehicle navigation system (EVNS) based on an autonomic computing architecture and a hierarchical architecture over vehicular ad-hoc networks (VANETs) is proposed. In addition to moving energy, the proposed EVNS considers air conditioner energy to predict the state of charge (SOC) because air conditioning is the most demanding auxiliary load in electric vehicles. Moreover, this paper proposes a time-dependent routing algorithm that considers shared information. The results show that the proposed EVNS method results in 31.15% and 9.52% improvements in mileage compared with the shortest path first (SPF) method and the distributed method in Manhattan, New York City, NY, USA, respectively. Furthermore, the proposed EVNS method results in 32.39% and 20.67% improvements in mileage compared with the SPF method and the distributed method in Taipei, Taiwan, respectively. [ABSTRACT FROM AUTHOR]

Because of its outstanding performance in extending coverage and reducing transmit power, wireless relay cooperation is deemed as one of the promising techniques to realize green-broadband communication in the future. In such relay cooperative systems, optimal power allocation not only can prolong the lifetime of users, but also is an effective way to reduce radiation in the environment. In this paper, for amplify-and-forward-relay cooperative networks, where multiple communication pairs share a common relay, we propose a novel power allocation scheme, which has considerably lower complexity compared with the existing solution schemes. The objective of this paper is to minimize sum-source-power consumption under the conditions that the transmit power of all nodes in the network is constrained and their predetermined target signal-to-noise ratios are satisfied. By providing sufficient analytical evidence, we study the optimality, convergence, and computational complexity of our proposed scheme. Through numerical simulation, we further justify the effectiveness and efficacy of our scheme compared with the existing works. [ABSTRACT FROM AUTHOR]

Energy consumption is a major concern in today's wireless communications due to the consensus for a greener world. LTE-Advanced (LTE-A) has been standardized for the fourth-generation mobile communications to meet the growing demands for high-speed wireless communications. However, high-speed signal processing on LTE/LTE-A user equipment (UE) causes excessive power consumption. The discontinuous reception (DRX) mechanism is a critical technique for tackling this issue. Delay constraint and power savings are two contradictory performance metrics associated with the DRX mechanism. Using recursive deduction and Markov model, this paper provides an in-depth analysis on the average delay and average power consumption of the DRX mechanism. Two performance metrics, namely, power-saving factor and relative power saving, are devised to assess the power-saving performance of the DRX mechanism. The accuracy of theoretical analysis is validated by computer simulations using the parameters in compliance with LTE specifications. The performance of the DRX mechanism is governed by a set of parameters that interact with one another in an intricate manner. Therefore, the values of key parameters are tested to assess their impacts on the performance of the DRX mechanism. The results shown in this paper give an insight into the operation and further improvement of the DRX mechanism. [ABSTRACT FROM AUTHOR]

Oversizing electrical distribution equipment is a concern in industrial facilities. It adds cost and can increase fault levels. IEEE Std 141-1993 provides guidelines on how to size electrical equipment, but at the early stage of the project, when the equipment is being purchased, much of the process information necessary to properly size the equipment is preliminary at best. In addition, much is left to the discretion of the individual design engineer, who is often not an employee of the facility's eventual owner and, therefore, may have no incentive to reduce equipment size. In fact, depending upon the contractual relationship and due to the preliminary data, compressed schedule, and long delivery, design engineers tend to oversize the equipment. This paper describes an approach for establishing a load-based upper size limit to control oversizing of electrical equipment. This paper will demonstrate that establishing this upper limit can effectively control oversizing of the electrical equipment with information available at the early stage of the project. The resulting safety and economic benefits of the properly sized electrical equipment cascade throughout the power system. [ABSTRACT FROM AUTHOR]

Speed scaling has been widely adopted in computer and communication systems, in particular, to reduce energy consumption. An important question is how speed scaling interacts with other resource allocation mechanisms such as scheduling and routing. In this paper, we study the interaction of speed scaling with load balancing. We characterize the equilibrium resulting from the load balancing and speed scaling interaction, and introduce two optimal load-balancing designs, in terms of traditional performance metric and cost-aware (in particular, energy-aware) performance metric, respectively. Especially, we characterize the load-balancing–speed-scaling equilibrium with respect to the optimal load-balancing schemes in processor-sharing systems. Our results show that the degree of inefficiency at the equilibrium is mostly bounded by the heterogeneity of the system, but independent of the number of servers. These results provide insights in understanding the interaction of load balancing with speed scaling and guiding new designs. [ABSTRACT FROM PUBLISHER]

Variation in process, voltage and temperature is a major obstacle in achieving energy-efficient operation of LSI. This paper proposes an all-digital on-chip circuit to monitor leakage current variations of both of the nMOSFET and pMOSFET independently. As leakage current is highly sensitive to threshold voltage and temperature, the circuit is suitable for tracking process and temperature variation. The circuit uses reconfigurable inhomogeneity to obtain statistical properties from a single monitor instance. A compact reconfigurable inverter topology is proposed to implement the monitor circuit. The compact and digital nature of the inverter enables cell-based design, which will reduce design costs. Measurement results from a 65 nm test chip show the validity of the proposed circuit. For a 124 sample size for both of the nMOSFET and pMOSFET, the monitor area is 4500 µm 2 and active power consumption is 76 nW at 0.8 V operation. The proposed technique enables area-efficient and low-cost implementation thus can be used in product chips for applications such as dynamic energy and thermal management, testing and post-silicon tuning. [ABSTRACT FROM PUBLISHER]

This paper presents a Viterbi-specific 2T gain cell- based embedded DRAM (eDRAM) design for IEEE 802.11n WLAN application. In the proposed Viterbi decoder, refresh operations are completely removed in the eDRAM, by ensuring that the read-after-write period of survivor memory is shorter than the retention time of the gain cell. In order to facilitate the write operation with single-supply voltage, a beneficial read word-line (RWL) coupling technique is proposed. In this work, we also present a reference voltage generation scheme to support single-ended read operation. Thanks to the decoupled read and write structure of the gain cell, the proposed eDRAM can support dual-port operations without large area overhead, thus doubling the bandwidth of memories in the Viterbi decoder. To further reduce the area of the customized Viterbi memory, common redundant hardware between the memory peripheral and computational logics is identified and eliminated without latency overhead. The 4 bit soft-decision 64-state Viterbi decoder with 24 kb eDRAM (1-bank) is implemented in 65 nm CMOS process technology. The chip measurement results show 44% area and 39% power savings over the conventional SRAM-based Viterbi decoder implementation. [ABSTRACT FROM PUBLISHER]

This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive model, based on the Generalized Rule Induction (GRI) model. This predictive analysis discovers association rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company. [ABSTRACT FROM PUBLISHER]

This paper calculates and compares the efficiency and loss distribution in the three most popular state-of-the-art types of large motor drive systems: 1) current-source drive with active front-end rectifier; 2) current-source drive with 18-pulse thyristor rectifier; and 3) three-level voltage-source drive with 12-pulse/24-pulse diode rectifier. For converter systems with efficiencies better than 90%, it is suggested to calculate or measure the losses of all the components separately and add them up to calculate the total efficiency. A simple and accurate method of calculating switching losses for a current-source and voltage-source converter is proposed. Through the calculation and simulation of each loss factor such as switching losses, conduction losses, and snubber and filter losses, the three-level voltage-source drive system has been found to have the highest efficiency of 98.77% under the rated load of 1.6 MW. [ABSTRACT FROM AUTHOR]