Problem Of Energy Harvesting Research Articles

Immersive Multimedia Service Caching in Edge Cloud with Renewable Energy

Immersive service caching, based on the intelligent edge cloud, can meet delay-sensitive service requirements. Although numerous service caching solutions for edge clouds have been designed, they have not been well explored. Moreover, to the best of our knowledge, there is no work to consider the immersive service caching scheme under the supply of renewable energy. In this article, we investigate the service caching problem under the renewable energy supply to minimize service latency while making full use of renewable energy. Specifically, we formulate the service caching and renewable energy harvesting problem, which considers the dynamic renewable energy, unknown service requests, and limited capacity of the edge cloud. To solve this problem, we propose an effective algorithm, called OSCRE. Our algorithm first uses Lyapunov optimization to convert the time-average problem into time-independence optimization and thus realizes optimal renewable energy harvesting. Then, it realizes the service caching scheme using data-driven combinatorial multi-armed bandit learning. The simulation results show that the OSCRE scheme can save service latency while making sufficient use of renewable energy.

Dynamic Thermostable Cellulosic Triboelectric Materials from Multilevel-Non-Covalent Interactions.

Triboelectric materials present great potential for harvesting huge amounts of dispersed energy, and converting them directly into useful electricity, a process that generates power more sustainably. Triboelectric nanogenerators (TENGs) have emerged as a technology to power electronics and sensors, and it is expected to solve the problem of energy harvesting and self-powered sensing from extreme environments. In this paper, a high-temperature-resistant triboelectric material is designed based on multilevel non-covalent bonding interactions, which achieves an ultra-high surface charge density of 192 µC m-2 at high temperatures. TENGs based on the triboelectric material exhibit more than an order of magnitude higher power output (2750mW m-2 at 200°C) than the existing devices at high temperatures. These remarkable properties are achieved based on enthalpy-driven molecular assembly in highly unbonded states. Thus, the material maintains bond strength and ultra-high surface charge density in entropy-dominated high-temperature environments. This molecular design concept points out a promising direction for the preparation of polymers with excellent triboelectric properties.

An Evaluation of ANN Algorithm Performance for MPPT Energy Harvesting in Solar PV Systems

In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithms constructed using artificial neural networks (ANN) are applied to the problem of MPPT energy harvesting in solar photovoltaic (PV) systems for the purpose of creating a comparative evaluation of the performance of the six distinct algorithms. The goal of this analysis is to determine which of the six algorithms has the best overall performance. In the study, the performance of managing the training dataset is compared across the algorithms. The maximum power point tracking energy harvesting system is created using the environment of MATLAB or Simulink, and the produced model is examined using the artificial neural network toolkit. A total of 1000 datasets of solar irradiance, temperature, and voltage were used to train the suggested model. The data are split into three categories: training, validation, and testing. Eighty percent of the total data is used for training the model, and the remaining twenty percent is divided equally for testing and validation. According to the results, the regression values of LM, RP, BR, and BFGS are 1, whereas the regression values for SCG and GDM are less than 1. The gradient values for LM, RP, BFGS, SCG, BR, and GDM are 7.983 × 10−6, 0.033415, 1.0211 × 10−7, 0.14161, 0.00010493, and 11.485, respectively. Similarly, the performance values for these algorithms are 2.0816 × 10−10, 2.8668 × 10−6, 9.98 × 10−17, 0.052985, 1.583 × 10−7, and 0.15378. Overall, the results demonstrate that the LM and BFGS algorithms exhibit superior performance in terms of gradient and overall performance. The RP and BR algorithms also perform well across various metrics, while the SCG and GDM algorithms show comparatively less effectiveness in addressing the proposed problem. These findings provide valuable insights into the relative performance of the six evaluated algorithms for MPPT energy harvesting in solar PV systems.

Joint Energy Harvesting and Transmission Optimization for Cell-Free Massive MIMO With Network-Assisted Full Duplexing

This paper investigates the problem of joint energy harvesting (EH) and transmission optimization for cell-free massive MIMO with network-assisted full duplexing (NAFD), where each access point (AP) works in full-duplex mode, half-duplex mode or another flexible mode and is connected to a central processing unit (CPU) via compressed fronthaul links. The high demand on the fronthaul links and series cross-link interference (CLI) are the two main problems in cell-free massive MIMO with NAFD. Therefore, in our scheme, we propose a joint strategy of EH, fronthaul compression, and CLI cancelation to maximize the energy efficiency (EE) of the system under constraints of guaranteed quality-of-service (QoS), EH and compressed fronthaul requirements. A two-stage strategy of EH and transceiver design is proposed to solve the complicated optimization problem. Specifically, in the first stage, we propose an EH/information receiving (IR) antenna selection algorithm to design the operation mode of the antennas for uplink users and receiving APs. In the second stage, with the antennas selected in the first stage, we further attempt to solve the EE maximization problem. Furthermore, to solve the highly nonconvex EE maximization problem, we propose a fractional programming (FP)-based two-tier iterative algorithm in which a series of nonconvex–convex approximation methods, such as FP, iterative convex approximation and equivalent formulation, are used. Simulation results demonstrate that the proposed algorithms yield a higher EE gain than the traditional time-division duplexing (TDD) and co-frequency co-time full duplexing (CCFD) schemes.

System Performance Analysis of Sensor Networks for RF Energy Harvesting and Information Transmission

This paper investigates the problem of RF energy harvesting in wireless sensor networks, with the aim of finding a suitable communication protocol by comparing the performance of the system under different protocols. The network is made up of two parts: first, at the beginning of each timeslot, the sensor nodes harvest energy from the base station (BS) and then send packets to the BS using the harvested energy. For the energy-harvesting part of the wireless sensor network, we consider two methods: point-to-point and multi-point-to-point energy harvesting. For each method, we use two independent control protocols, namely head harvesting energy of each timeslot (HHT) and head harvesting energy of dedicated timeslot (HDT). Additionally, for complex channel states, we derive the cumulative distribution function (CDF) of packet transmission time using selective combining (SC) and maximum ratio combining (MRC) techniques. Analytical expressions for system reliability and packet timeout probability are obtained. At the same time, we also utilize the Monte Carlo simulation method to simulate our system and have analyzed both the numerical and simulation solutions. Results show that the performance of the HHT protocol is better than that of the HDT protocol, and the MRC technology outperforms the SC technology for the HHT protocol in terms of the energy-harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio (SNR), and length of energy harvesting time.

Blockchain-Enabled Task Offloading With Energy Harvesting in Multi-UAV-Assisted IoT Networks: A Multi-Agent DRL Approach

Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist Internet of Things (IoT) networks, solving various problems such as extending network coverage, enhancing network performance, transferring energy to IoT devices (IoTDs), and perform computationally-intensive tasks of IoTDs. Heterogeneous IoTDs connected to IoT networks have limited processing capability, so they cannot perform resource-intensive activities for extended periods. Additionally, IoT network is vulnerable to security threats and natural calamities, limiting the execution of real-time applications. Although there have been many attempts to solve resource scarcity through computational offloading with Energy Harvesting (EH), the emergency and vulnerability issues have still been under-explored so far. This paper proposes a blockchain and multi-agent deep reinforcement learning (MADRL) integrated framework for computation offloading with EH in a multi-UAV-assisted IoT network, where IoTDs obtain computing and energy resources from UAVs. We first formulate the optimization problem as the joint optimization problem of computation offloading and EH problems while considering the optimal resource price. And then, we model the optimization problem as a Stackelberg game to investigate the interaction between IoTDs and UAVs by allowing them to continuously adjust their resource demands and pricing strategies. In particular, the formulated problem can be addressed indirectly by a stochastic game model to minimize computation costs for IoTDs while maximizing the utility of UAVs. The MADRL algorithm solves the defined problem due to its dynamic and large-dimensional properties. Finally, extensive simulation results demonstrate the superiority of our proposed framework compared to the state-of-the-art.

Modelling of Flow-Induced Vibration of Bluff Bodies: A Comprehensive Survey and Future Prospects

A comprehensive review of modelling techniques for the flow-induced vibration (FIV) of bluff bodies is presented. This phenomenology involves bidirectional fluid–structure interaction (FSI) coupled with non-linear dynamics. In addition to experimental investigations of this phenomenon in wind tunnels and water channels, a number of modelling methodologies have become important in the study of various aspects of the FIV response of bluff bodies. This paper reviews three different approaches for the modelling of FIV phenomenology. Firstly, we consider the mathematical (semi-analytical) modelling of various types of FIV responses: namely, vortex-induced vibration (VIV), galloping, and combined VIV-galloping. Secondly, the conventional numerical modelling of FIV phenomenology involving various computational fluid dynamics (CFD) methodologies is described, namely: direct numerical simulation (DNS), large-eddy simulation (LES), detached-eddy simulation (DES), and Reynolds-averaged Navier–Stokes (RANS) modelling. Emergent machine learning (ML) approaches based on the data-driven methods to model FIV phenomenology are also reviewed (e.g., reduced-order modelling and application of deep neural networks). Following on from this survey of different modelling approaches to address the FIV problem, the application of these approaches to a fluid energy harvesting problem is described in order to highlight these various modelling techniques for the prediction of FIV phenomenon for this problem. Finally, the critical challenges and future directions for conventional and data-driven approaches are discussed. So, in summary, we review the key prevailing trends in the modelling and prediction of the full spectrum of FIV phenomena (e.g., VIV, galloping, VIV-galloping), provide a discussion of the current state of the field, present the current capabilities and limitations and recommend future work to address these limitations (knowledge gaps).

Beamforming Design for Reconfigurable Intelligent Surface via Bayesian Optimization

This letter investigates the transmission design in the multi-user downlink system using the reconfigurable intelligent surface (RIS). The conventional transmission scheme requires the channel estimation by transmitting pilots and feeds back the channel state information (CSI) to the base station, which causes lots of overhead and substantial radio-frequency chains at the RIS. In this letter, we propose a cost-efficient downlink transmission scheme via Bayesian optimization that dispenses with CSI. The beamformer at the BS and the phase rotation at the RIS are jointly designed by minimizing the sum mean square error (MSE). Since the proposed scheme dispenses with CSI, the channel estimation and the CSI feedback are not required, which saves the number of training slots and dispenses with RF chains at the RIS. Moreover, the proposed scheme can be extended to the minimax MSE problem and the energy harvesting problem. Simulation results show that the proposed transmission scheme outperforms the particle swarm optimization scheme.

RETRACTED: Energy Efficiency Maximization for Hybrid-Powered 5G Networks with Energy Cooperation

The extensive deployment of 5G cellular networks causes increased energy consumption and interference in systems, and to address this problem, this paper investigates the optimization problem of joint energy harvesting and energy cooperation to maximize energy efficiency (EE). First, considering user equipment (UE) quality of service (QoS) constraints, cellular base station power constraints, and renewable energy harvesting constraints, we construct a mixed-integer nonlinear programming problem for joint resource allocation. This problem is difficult to solve directly, thus we combine the fixed-variable method to solve the complex original problem in three less difficult subproblems of user association, power allocation, and energy cooperation by solving them separately using Lagrangian method, improved particle swarm optimization algorithm, and matching theory, respectively. Finally, the final solution to the original problem is obtained by combining the above three algorithms through convergent iterative algorithms. The simulation results show that the joint algorithm proposed in this paper has a better performance in throughput and energy efficiency compared with the comparison algorithms.

Mathematical modelling, nonlinear dynamics, bifurcation, synchronization and control of mechanisms driven by power supply

The special issue entitled “Mathematical modelling, nonlinear dynamics, bifurcation, synchronization and control of mechanisms driven by power supply,” published by The European Physical Journal: Special Topics (EPJST), aims to bring into discussion works involving nonlinear and dynamic and chaotic behaviors, which are frequently found in mechanical and electromechanical systems, nonlinear circuits, and biologically inspired systems, stimulating the integration of findings on the nonlinear dynamics that can be observed in various systems in the physical sciences and engineering. This special issue presents 27 papers concerning the dynamics and control of flexible structure systems, nonideal systems, energy harvesting problems, and vibration absorbers. In addition, the papers address problems involving chaos control of nonlinear oscillations, phase-locked loops, synchronization of nonlinear oscillators, dynamics of bio-inspired and biostructural systems, nonlinear methods, and nonlinear circuits.

Closed-Form Solutions of Bending-Torsion Coupled Forced Vibrations of a Piezoelectric Energy Harvester Under a Fluid Vortex

Abstract Vibration energy harvesting problems have strongly developed in recent years. However, many researchers just consider bending vibration models of energy harvesters. As a matter of fact, torsional vibration is also important and cannot be ignored in many cases. In this work, closed-form solutions of bending-torsion coupled forced vibrations of a piezoelectric energy harvester subjected to a fluid vortex are derived. Timoshenko beam model is used for modeling the energy harvester, and the extended Hamilton’s principle is used in the modeling process. Since piezoelectric effects in both bending and torsional directions are considered, two kinds of electric coupling effects appear in forced vibration equations, and a new model for the electric circuit equation is developed. Lamb–Oseen vortex model is considered in this study. Both the external aerodynamic force and moment are simple harmonic loads. Three damping coefficients are considered in the present model. Based on Green’s function method, closed-form solutions of the piezoelectric energy harvester subjected to the fluid vortex are derived. Some published results are used to verify the present solutions. It can be concluded through analysis that when torsional vibration is considered, the bandwidth of the high energy area of the voltage becomes large and the bending-torsion coupled vibration energy harvester can produce more power than a transverse vibration energy harvester.

Propulsion and energy harvesting performances of a flexible thin airfoil undergoing forced heaving motion with passive pitching and deformation of small amplitude

The fluid–structure interaction in a foil undergoing prescribed heave with passive pitching and flexibility about any pivot is formulated in the linear inviscid limit using a quartic approximation for the deflection. The resulting system of three algebraic equations is valid for arbitrary mass and stiffness distributions of the foil. The small pitching and deformation amplitudes result linearly from two of the equations, while the third equation provides the force at the pivot point that generates the heaving motion, and hence the power input. This general formulation allows to analyze jointly both the propulsion and the energy harvesting problems for this class of flapping foils. In the first case, the thrust force is readily obtained from the prescribed heave and the resulting pitching and deformation, and consequently the propulsive efficiency once the power input is computed. In the second problem, the energy may be harvested by linear and/or torsional dampers at the pivot point, so that the efficiency of the system is readily computed once the pitch motion and the power input are obtained. Thus, the present work allows for a depth parametric survey and analysis of these two physical problems. The best performance is usually obtained around the first natural frequency of the fluid–structure system, which is obtained here by minimizing an algebraic function. The formulation is validated by reproducing some previous results for both problems, most of them obtained numerically for rigid foils and without the simplicity nor the richness in the parameter space of the present formulation. The parametric range for which flexibility maximizes the propulsion and the energy harvesting efficiencies in relation to an otherwise identical rigid-foil system is analyzed.

Nonlinear dynamic performance of buckled piezoelectric ribbon-substrate energy harvester

With utilizing of the wearable electronics in daily life, the energy harvesting problem of the electronic devices has become an urgent problem. In this paper, the dynamic behaviour of the energy harvester, which is constructed by bonding piezoelectric ribbons on a pre-strained compliant substrate, is investigated. Taking the geometrical nonlinearity into account, and by using the extended Lagrange equations, the governing equations of the buckled piezoelectric energy harvester are derived. To obtain the steady-state dynamic responses of the governing equations, the harmonic balance method is used. The analytical solutions, which predict the influences of the pre-strain and substrate’s Young’s modulus on the dynamic response of the energy harvester, are obtained. Several numerical examples are carried out to discuss the effects of the pre-strain and substrate’s Young’s modulus on energy harvester dynamic performance. By modulating those two parameters, the high-energy orbit of the buckled structure would be modulated, which is helpful for improving the voltage output performance of the energy harvesting. The results of this paper would be useful for the optimization of energy harvester based on the buckled piezoelectric ribbon-substrate structure.

A Joint Beamforming and Power-Splitter Optimization Technique for SWIPT in MISO-NOMA System

In this paper, we propose a joint beamforming and power-splitter optimization technique for simultaneous wireless power and information transfer in the downlink transmission of a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. Accordingly, each user employs a power splitter to decompose the received signal into two parts, namely, the information decoding and energy harvesting. The former part is used to decode the corresponding transmitted information, whereas the latter part is utilized for harvesting energy. For this system model, we solve an energy harvesting problem with a set of design constraints at the transmitter and the receiver ends. In particular, the beamforming vector and the power splitting ratio for each user are jointly designed such that the overall harvested power is maximized subject to minimum per-user rate requirements and the available power budget constraints at the base station. As the formulated problem turns out to be non-convex in terms of the design parameters, we propose a sequential convex approximation technique and demonstrate a superior performance compared to a baseline scheme.

Variational Control Approach to Energy Extraction from a Fluid Flow

Energy harvesting from the environment is an important aspect of many technologies. The scale of energy capturing and storage can involve the power range from mWatt up to MWatt, depending on the used devices and the considered environments (from ambient acoustic and vibration to ocean wave motion, or wind). In this paper, the wind turbine energy harvesting problem is approached as an optimal control problem, where the objective function is the absorption of an amount of energy in a given time interval by a fluid-flow environment, that should be maximized. The interest relies on outlining general control models of fluid-flow-based extraction plants and identifying an optimum strategy for the regulation of an electrical machine to obtain a maximum-efficiency process for the related energy storage. The mathematical tools are found in the light of optimal control theory, where solutions to the fundamental equations are in the frame of Variational Control (the basis of the Pontryagin optimal control theory). A special problem, named Optimally Controlled Betz’s Machine OCBM-optimal control steady wind turbine, is solved in closed form, and it is shown that, in the simpler steady case, it reproduces the maximum efficiency machine developed in Betz’s theory.

Energy- and Spectral-Efficiency Tradeoff With $\alpha$-Fairness in Energy Harvesting D2D Communication

Energy Harvesting (EH) technology enables Device-to-Device (D2D) User Equipments (DUEs) to harvest energy from ambient energy, making contributions to green communications and breaking the single battery-powered and the regional limitations of device deployment. The joint problem of energy harvesting and resource allocation in EH-based D2D Communication Networks (EH-DCNs) is a challenge issue. In this paper, we investigate the joint problem of resource allocation and EH time slot allocation of DUEs in EH-DCNs, where the DUEs harvest energy and multiplex Cellular User Equipments (CUEs) uplink resources. A channel assignment, power allocation and EH time slot allocation problem in EH-DCNs is formulated. The goal is to maximize energy-and spectral-efficiency with α-fairness while guaranteeing the EH constraints of DUEs and the quality of service of CUEs. The formulated problem is a non-convex mixed integer multi-objective optimization problem. In order to solve the formulated problem, the multi-objective optimization problem is transformed into a single-objective optimization problem based on the weight sum method. We propose a joint iterative algorithm based on Lagrangian dual decomposition for α > 0 and α = 0, respectively. Numerical results illustrate that the proposed algorithm achieves higher energy efficiency and spectral efficiency for different network parameter settings.

Nonlinear piezoelectric plate framework for aeroelastic energy harvesting and actuation applications

The use of piezoelectric materials in various applications, including the development of bio-inspired structures, vibration control, energy harvesting, among others, has been investigated by several researchers over the last few decades. In most cases, linear piezoelectricity is assumed in modeling and analysis of such systems. However, the recent literature shows that non-linear manifestations of piezoelectric materials are relevant and can modify the electromechanical behavior especially around the resonance. This work extends the investigation of non-linear piezoelectricity, by adding geometric nonlinearities and aerodynamic effects, to aeroelastic problems such as wind energy harvesting. A piezoaeroelastic model that combines a non-linear coupled finite element model and the doublet lattice model of unsteady aerodynamics is presented. The electromechanically coupled finite element model includes the non-linear behavior of piezoelectric material under weak electric fields. Model predictions are validated by experimental data for 1) a double bimorph actuation case and 2) a vibration based energy harvesting case. Later, the piezoaeroelastic behavior of a generator plate-like wing for wind energy harvesting is numerically investigated when linear as well as non-linear piezoelectricity is considered. The experimentally validated geometrically and materially non-linear framework presented here is applicable to both energy harvesting and actuation problems in the presence of air flow.

Cooperative Small Cell HetNets With Dynamic Sleeping and Energy Harvesting

This paper considers a heterogenous wireless cellular network (HetNet) where many small base stations (SBS) coexist. SBSs can be deactivated and put to sleep to save energy and are equipped with two power sources, harvested energy (HE) and a grid power source, where an SBS will use its available HE to serve the associated users first. Then, the SBS will request any shortage of its energy from other active or deactivated SBSs that have a surplus of HE. Finally, if there is still an energy shortage, the SBS will use power drawn from the grid. This transfer of energy is facilitated through the use of the promising smart grid (SG)technology. We investigate the grid energy minimization problem by optimizing both the transmission power and activation/deactivation (dynamic sleeping) of the SBSs. However, since the formulated problem is a mixed integer nonLinear problem (MINLP), generalized Benders decomposition (GBD) is used to decompose the problem into two subproblems: user association and energy harvesting which are solved iteratively. Further, a new heuristic approach is proposed that provides a computationally efficient algorithm to solve and optimize the user association and energy harvesting problems of the system model. This approach uses network centrality to develop a measuring parameter, base station centrality (BSC), of SBS centrality in the network. BSC is presented to mark the SBSs that have the most potential to be deactivated without affecting the quality of service (QoS) of users. Finally, extensive simulations are performed to verify the superiority of the proposed BSC-based strategy over GBD in terms of operational cost.

Exploiting double jumping phenomenon for broadening bandwidth of an energy harvesting device

The steady state response of a particular harvesting system is investigated under the condition of external and internal resonance, with emphasis on the double jump phenomenon. Following the trend of recent researches on exploring nonlinearity aspects for broadening the frequency resonance bandwidth, the problem of effective energy harvesting from a broadband source is dealt with. The proposed harvester can be embedded within vibrational structures and can dually act as a vibration absorber. The governing non-linear partial differential equations are truncated into a set of perturbed equations via Galerkin method. The method of multiple scales is applied to derive the modulated amplitude versus frequency at the vicinity of flapwise and chordwise primary resonances, as well as around other internal resonance frequencies. The amplitude-frequency response plot reveals resonance peaks bending to the left and right, i.e., splitting into two different tongues in contrast to conventional jumps which lean only toward higher or lower frequency directions. Numerical results demonstrate that this internal resonance-based harvesting design can produce sufficient power for the consumption of typical MEMS devices.

Kinetic Energy Harvesting for Wearable Medical Sensors.

The process of collecting low-level kinetic energy, which is present in all moving systems, by using energy harvesting principles, is of particular interest in wearable technology, especially in ultra-low power devices for medical applications. In fact, the replacement of batteries with innovative piezoelectric energy harvesting devices can result in mass and size reduction, favoring the miniaturization of wearable devices, as well as drastically increasing their autonomy. The aim of this work is to assess the power requirements of wearable sensors for medical applications, and address the intrinsic problem of piezoelectric kinetic energy harvesting devices that can be used to power them; namely, the narrow area of optimal operation around the eigenfrequencies of a specific device. This is achieved by using complex numerical models comprising modal, harmonic and transient analyses. In order to overcome the random nature of excitations generated by human motion, novel excitation modalities are investigated with the goal of increasing the specific power outputs. A solution embracing an optimized harvester geometry and relying on an excitation mechanism suitable for wearable medical sensors is hence proposed. The electrical circuitry required for efficient energy management is considered as well.