Power Optimization Scheme Research Articles

Bacterial Nano‐Cellulose Triboelectric Nanogenerator

Motivated by a desire to resolve the needs of sustainable energy resources, remote sensing electronics, wireless autonomous devices, mobile internet of things (IoT) and portable self-power generators, triboelectric nanogenerators have recently been suggested. However, more specialized target applications to biomedical and wearable devices will require biocompatible and eco-friendly triboelectric materials in power generators. Herein, we report for the first time a bio-triboelectric nanogenerator based on an eco-friendly and naturally abundant biomaterial, bacterial nanocellulose. Initially, bacterial cellulose pellicles were produced in a gel state by Acetobacter xylinum KJ1 in the Glu-Fruc medium and then a bacterial nanocellulose film having transparent and flexible functionalities was regenerated on a current collector via a solubilization process. The bacterial nanocellulose triboelectric nanogenerator was investigated with various input conditions and structural aspects. The working mechanism was also considered by measuring the contact angle and the surface potential of the friction materials. We believe that this study provides new insights to advancing the biocompatible and eco-friendly triboelectric power generator and optimization strategies to achieve high performance of triboelectric nanogenerators.

Robust power optimization scheme for cooperative wireless relay system in smart city

SummaryUltra dense deployment of base stations is one of most significant features in smart city communication networks. Aiming at the large‐scale wireless communication issue in smart city, we propose a distributed robust power allocation scheme with proportional fairness for cooperative orthogonal frequency‐division‐multiple‐access relay network. With the amplify‐and‐forward relay mode, all of the relays assist the information transmission simultaneously on orthogonal subcarriers. Considering the uncertainty of channel gains, first we aim at achieving the maximum utility subject to the constraints of outage probability threshold and power bound. Subsequently, the problem is transformed to a solvable convex optimization problem with determination constraints. The dual‐decomposition method is applied to solve the formulated optimization problem. To reduce the information exchange of the whole system, we propose a computationally efficient distributed iteration algorithm. Numerical results reveal the effectiveness of the proposed robust optimization algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances (HA) for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system (EMS) and then propose a power optimization strategy based on household load model and electric vehicle (EV) model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price (TOUP), the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.

Transmit Power Optimization for Amplify-and-Forward Relay Networks With Reduced Overheads

Transmit power optimization or power control plays an important role in implementing wireless relay networks since it can significantly improve system performance, such as transmission rate, power consumption, etc. However, power optimization introduces considerable control channel overheads due to exchanging channel state information (CSI) and optimization results. In this paper, we aim to design a new power optimization scheme to reduce overheads while avoiding a large performance loss. We consider a typical three-node amplify-and-forward relay network consisting of a source, a relay, and a destination. The power of source and that of relay are optimized to minimize the total power consumption under the constraint of a minimum transmission rate or nonviolation probability. We first define and analyze two schemes based on traditional routines, which are called Strategies I and II. In Strategy I, transmit power of both source and relay is optimized based on instantaneous CSI, whereas in Strategy II, transmit power is based on statistical CSI. We then propose a new partial CSI strategy, which is called Strategy III, where source power is based on statistical CSI, whereas relay power is based on instantaneous CSI. Strategy III is formulated and solved by the two-stage stochastic programming method. With this new strategy, the control channel overhead can be reduced by 50% compared with Strategy I, and at the same time, the potential significant performance degradation as in Strategy II can be avoided. Simulation results show that the proposed strategy results in near-optimal performance when the relay is located close to the source.

Research on Power Control Algorithms Based on Stackelberg Game Model in Two-tier Network

With the continuous development of information, wireless network has become an integral part of people’s everyday lives. With the explosive growth of data in wireless network, the interferences and competition for resources are more and more serious, especially in the intensive network scenarios. Power control can effectively reduce the interference to improve throughput, save energy and prolong the service life of equipment. This paper proposes a power optimization strategy for Femtocell base station in LTE-A, a two-tier network. It plans the problem into Stackelberg Game, as Stackelberg Game model well describes the user behaviors, making it an effective analysis tool. Then this paper researches the Stackelberg equilibrium and obtains the optimal power distribution and optimal power pricing method. Based on it, a distributed non-uniform pricing algorithm, DNP, is proposed for Femtocell base station deployment. It realizes the distributed deployment with low complexity and less information interaction, and also shows a good performance through simulation, thus having a guiding significance for the power control scheme of Femtocell base station deployment.

Virtual Platform-Based Design Space Exploration of Power-Efficient Distributed Embedded Applications

Networked embedded systems are essential building blocks of a broad variety of distributed applications ranging from agriculture to industrial automation to healthcare and more. These often require specific energy optimizations to increase the battery lifetime or to operate using energy harvested from the environment. Since a dominant portion of power consumption is determined and managed by software, the software development process must have access to the sophisticated power management mechanisms provided by state-of-the-art hardware platforms to achieve the best tradeoff between system availability and reactivity. Furthermore, internode communications must be considered to properly assess the energy consumption. This article describes a design flow based on a SystemC virtual platform including both accurate power models of the hardware components and a fast abstract model of the wireless network. The platform allows both model-driven design of the application and the exploration of power and network management alternatives. These can be evaluated in different network scenarios, allowing one to exploit power optimization strategies without requiring expensive field trials. The effectiveness of the approach is demonstrated via experiments on a wireless body area network application.

Reactive Power Compensation and Optimization Strategy for Grid-Interactive Cascaded Photovoltaic Systems

Cascaded multilevel converter structure can be appealing for high-power solar photovoltaic (PV) systems thanks to its modularity, scalability, and distributed maximum power point tracking (MPPT). However, the power mismatch from cascaded individual PV converter modules can bring in voltage and system operation issues. This paper addresses these issues, explores the effects of reactive power compensation and optimization on system reliability and power quality, and proposes coordinated active and reactive power distribution to mitigate this issue. A vector method is first developed to illustrate the principle of power distribution. Accordingly, the relationship between power and voltage is analyzed with a wide operation range. Then, an optimized reactive power compensation algorithm (RPCA) is proposed to improve the system operation stability and reliability, and facilitate MPPT implementation for each converter module simultaneously. Furthermore, a comprehensive control system with the RPCA is designed to achieve effective power distribution and dynamic voltage regulation. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed reactive power compensation approach in grid-interactive cascaded PV systems.

Energy-Aware Cellular Deployment Strategy Under Coverage Performance Constraints

The last ten years have witnessed explosive growth in mobile data traffic, which leads to rapid increases in energy consumption of cellular networks. One potential solution to this issue is to seek out a green deployment strategy. In this paper, we investigate the energy-efficient deployment strategy under coverage performance constraints for both homogeneous and heterogeneous cellular networks. Unlike just considering the base station (BS) density in previous work, we jointly optimize the BS density and the BS transmission power. First, we derive the relation between the average coverage probability and deployment strategy (i.e., BS density and BS transmission power) with stochastic geometry tools. Then, based on the expression results, we formulate a network energy consumption minimization framework considering coverage performance constraints and jointly determine the optimal macro BS (MaBS) density, MaBS transmission power, and micro BS (MiBS) density. With practical data sets, numerical simulation results show the following: 1) compared with homogeneous network deployment, heterogeneous network deployment has the advantage in energy efficiency performance, and 2) our joint BS density and BS transmission power optimization strategy exceeds the existing strategy, which just considers the BS density optimization in terms of energy efficiency.

Contactless energy transfer at the bedside featuring an online power optimization strategy

This paper introduces the principle of contactless energy transfer to wearable bedside electronics. As a proof of concept, a platform containing a matrix of magnetic energy transmitters has been developed and integrated into a mattress. This system is able to feed one or more wearable sensor nodes, attached to the patient, with the purpose of wirelessly recording vital signals over an extended period of time. Each of these power transmitters is designed to transfer up to 10mW of power over a maximal distance of 300mm. Moreover, the platform embeds online optimization software to deal with vertical as well as horizontal sensor node movements. The position and orientation of each freely moving sensor node can be dynamically determined and a local magnetic field, with a matching radiated power level, can be selectively induced in the area where the sensor nodes are positioned. This approach therefore limits the radiation exposure to the patient to the minimum viable level. The prototype is successfully tested in a laboratory set-up.

Compilers for Low Power with Design Patterns on Embedded Multicore Systems

Minimization of power dissipation can be considered at algorithmic, compiler, architectural, logic, and circuit level. Recent research trends for multicore programming models have come to the direction that parallel design patterns can be a solution to develop multicore applications. As parallel design patterns are with regularity, we view this as a great opportunity to exploit power optimizations in the software layer. In this paper, we investigate compilers for low power with parallel design patterns on embedded multicore systems. We evaluate four major parallel design patterns, Pipe and Filter, MapReduce with Iterator, Puppeteer, and Bulk Synchronous Parallel (BSP) Model. Our work attempts to devise power optimization schemes in compilers by exploiting the opportunities of the recurring patterns of embedded multicore programs. The proposed optimization schemes are rate-based optimization for Pipe and Filter pattern , early-exit power optimization for MapReduce with Iterator pattern, power aware mapping algorithm for Puppeteer pattern, and multi-phases power gating scheme for BSP pattern. In our experiments, real world multicore applications are evaluated on a multicore power simulator. Significant power reductions are observed from the experimental results. Therefore, we present a direction for power optimizations that one can further identify additional key design patterns for embedded multicore systems to explore power optimization opportunities via compilers.

Multi-objective reactive power optimization strategy for distribution system with penetration of distributed generation

The study investigates multi-objective reactive power optimization (MORPO) of distribution system penetrated with distributed generation (DG). Integrating the reactive power of DG as one type of decision variables, a multi-objective model for RPO has been established to decrease the system active power loss, reduce voltage deviation and minimize the total capacity of reactive power compensation (RPC) devices (or minimize investments on RPC devices). Instead of converting the multiple objectives into a single one, a dynamically adaptive multi-objective particle swarm optimization (DAMOPSO) algorithm with introduction of special adaptive techniques has been proposed and validated and then applied to the MORPO problem with continuous and discrete variables. In order to the proposed MORPO model and the application of DAMOPSO, and to obtain a deep insight into MORPO with different objectives, a series of simulations on IEEE 33-bus system along with analysis and discussion are carried out. The results verified the feasibility and effectiveness of the proposed strategy.

Multi-scale optimization for process systems engineering

Efficient nonlinear programming (NLP) algorithms and modeling platforms have led to powerful process optimization strategies. Nevertheless, these algorithms are challenged by recent evolution and deployment of multi-scale models (such as molecular dynamics and complex fluid flow) that apply over broad time and length scales. Integrated optimization of these models requires accurate and efficient reduced models (RMs). This study develops a rigorous multi-scale optimization framework that substitutes RMs for complex original detailed models (ODMs) and guarantees convergence to the original optimization problem. Based on trust region concepts this framework leads to three related NLP algorithms for RM-based optimization. The first follows the classical gradient-based trust-region method, the second avoids gradient calculations from the ODM, and the third avoids frequent recourse to ODM evaluations, using the concept of ϵ-exact RMs. We illustrate these algorithms with small examples and discuss RM-based optimization case studies that demonstrate their performance and effectiveness.

Performance analysis and power allocation for multi-hop multi-branch amplify-and-forward cooperative networks over generalized fading channels

In this article, efficient power allocation strategies for multi-hop multi-branch amplify-and-forward networks are developed in generalized fading environments. In particular, we consider the following power optimization schemes: (i) minimizing of the all transmission powers subject to an outage constraint; and (ii) minimizing the outage probability subject to constraint on total transmit powers. In this study, we first derive asymptotically tight approximations for the statistics of the received signal-to-noise ratio (SNR) in the system under study with maximal ratio combining and selection combining receiver. With the statistical characterization of the received SNR, we then carry out a thorough performance analysis of the system. Finally, the asymptotic expression of the outage probability is used to formulate the original optimization problems using geometric programming (GP). The GP can readily be transformed into nonlinear convex optimization problem and therefore solved efficiently and globally using the interior-point methods. Numerical results are provided to substantiate the analytical results and to demonstrate the considerable performance improvement achieved by the power allocation.

On the Outage Performance of Full-Duplex Selective Decode-and-Forward Relaying

We evaluate the outage performance in a three-terminal full-duplex relay channel that adopts a selective decode-and-forward protocol, taking relay self-interference into account. Previous work focused on coverage extension scenarios where direct source-destination transmissions are neglected or considered as interference. In this work, we account for the relay self-interference, and exploit the cooperative diversity offered by the independently fading source/relay message replicas that arrive at the destination. We present an approximate, yet accurate, closed-form expression for the end-to-end outage probability that captures their joint effect. With the derived expression in hand, we propose a relay transmit power optimization scheme that only requires the relay knowledge of channel statistics. Finally, we corroborate our analysis with simulations.

Global sensitivity analysis of wind turbine power output

ABSTRACTThe dynamics of wind turbine behavior are complex and a critical area of study for the wind industry. Identification of factors that cause changes in turbine performance can sometimes prove to be challenging, whereas other times, it can be intuitive. The quantification of the effect that these factors have is valuable for making improvements to both power performance and turbine health. In commercial farms, large quantities of meteorological and performance data are commonly collected to monitor daily operations. These data can also be used to analyze the relationship between each parameter in order to better understand the interactions that occur and the information contained within these signals. In this global sensitivity analysis, a neural network is used to model select wind turbine supervisory control and data acquisition system parameters for an array of turbines from a commercial wind farm that exhibit signs of wake interaction. An extended Fourier amplitude sensitivity test is then performed for 2 years of 10‐min averaged data. The study examines the primary and combined sensitivities of power output to each selected parameter for two turbines in the array. The primary sensitivities correspond to single parameter interactions, whereas combined sensitivities account for interactions between multiple parameters simultaneously. Highly influential parameters such as wind speed and rotor rotation frequency produce expected results; the extended Fourier amplitude sensitivity test method proved effective at quantifying the sensitivity of a wide range of more subtle inputs. These include blade pitch, yaw position, main bearing and ambient temperatures as well as wind speed and yaw position standard deviation. The technique holds promise for application in full‐scale wake studies where it might be used to determine the benefits of emerging power optimization strategies such as active wake management. The field of structural health monitoring can also benefit from this method. Copyright © 2013 John Wiley & Sons, Ltd.

Distributed power optimization for spectrum-sharing femtocell networks: A fictitious game approach

Power control techniques are becoming increasingly important for a two-tier network, where a central macrocell is underlaid with femtocells, since cross-tier and co-tier interference severely limits network performance. In this paper, we propose a distributed power control scheme for the uplink transmission of spectrum-sharing femtocell networks based on fictitious game. Each user announces a price that reflects its sensitivity to the current interference level, and adjusts its power to maximize its utility. Power and price are updated at terminals and base stations, respectively. The scheme is proved to converge to a unique optimal equilibrium. Furthermore, we propose a simple macrocell link protection scheme, where a macro user can protect itself by increasing its price. Most importantly, we investigate the power optimization scheme proposed in frequency-selective channels based on the Stackelberg game, in which each user prices its limited power allocated to subchannels. Numerical results show that the proposed schemes are effective in resource allocation for spectrum-sharing two-tier networks.

Substation Bus Reactive Load Forecasting in Large Iron and Steel Enterprise

Impact loads in large iron and steel enterprise bring the power system reactive power impact, which makes the fluctuation of the system voltage, power factor and other parameters are out of the limitation of the national standard. Substation bus reactive load forecasting in large iron and steel enterprise can be introduced to determine reactive power optimization strategy and the switching of capacitors. In this paper, a combination forecasting model of quadratic self-adaptive exponential smoothing (QSES) model and converse exponential (CE) model has been proposed for substation bus reactive load forecasting. The numerical results in Jinan iron and steel Group show the application of this model is encouraging. Introduction

Joint Power Optimization for Multi-Source Multi-Destination Relay Networks

In this paper, low-complexity joint power assignment algorithms are developed for multi-source multi-destination relay networks where multiple sources share a common relay that forwards all received signals simultaneously to destinations. In particular, we consider the following power optimization strategies: (i) Minimization of the total transmission power of the sources and the relay under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirement of each source-destination pair is satisfied, and (ii) Maximization of the minimum SINR among all source-destination pairs subject to any given total power budget. Both optimization problems involve K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop a methodology that allows us to obtain an asymptotically tight approximation of the SINR and reformulate the original optimization problems to single-variable optimization problems, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization schemes are scalable and lead to power assignment algorithms that exhibit the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, we apply the methodology that we developed to solve a related max-min SINR based optimization problem in which we determine power assignment for the sources and the relay to maximize the minimum SINR among all source-destination pairs subject to any given total power budget. Extensive numerical studies illustrate and validate our theoretical developments.

An Automated Power Emulation Framework for Embedded Software—Detecting Power-Critical Code Regions and Optimizing Software-Induced Power Consumption Peaks

Software-induced power consumption peaks can lead to internal supply voltage drops that threaten the reliability of power-constrained mobile systems such as RF-powered smart cards. In this paper we investigate the use of the power emulation technique for automatically detecting power consumption peaks and hence supply voltage drops caused by embedded software applications. Within an automated framework this information is employed to identify the power-critical source code regions causing these power peaks. Afterwards, power optimization strategies that are available on the given system, such as frequency scaling or non-functional instruction (NFI) insertion, are automatically applied to the source code in order to mitigate the impact of the power peaks. We illustrate the applicability of our automated approach on an RF-powered smart card system and present the benefits of these fine-grained code optimizations. With respect to manual optimizations, the automatic approach decreases the execution time overhead while only slightly increasing the required code size.

Distributed Interference-Aware Energy-Efficient Power Optimization

Power optimization techniques are becoming increasingly important in wireless system design since battery technology has not kept up with the demand of mobile devices. They are also critical to interference management in wireless systems because interference usually results from both aggressive spectral reuse and high power transmission and severely limits system performance. In this paper, we develop an energy-efficient power optimization scheme for interference-limited wireless communications. We consider both circuit and transmission powers and focus on energy efficiency over throughput. We first investigate a non-cooperative game for energy-efficient power optimization in frequency-selective channels and reveal the conditions of the existence and uniqueness of the equilibrium for this game. Most importantly, we discover a sufficient condition for generic multi-channel power control to have a unique equilibrium in frequency-selective channels. Then we study the tradeoff between energy efficiency and spectral efficiency and show by simulation results that the proposed scheme improves both energy efficiency and spectral efficiency in an interference-limited multi-cell cellular network.