Energy efficient design of portable wireless systems

Photovoltaic-based integrated energy systems act as a possible modern technological solution for clean and affordable green hydrogen production. However, research attempts are required from the scientific community to develop power management, control, optimization algorithms, and new techniques for these integrated energy systems effective and economical operation. The integrated energy system considered in this work consists of a solar photovoltaic, battery, grid and proton exchange membrane (PEM) electrolyzer. PEM electrolyzer with a power rating of 100 kW is modelled as a controlled current sink to interfere with the DC bus directly. This work proposes a power management and control algorithm for the photovoltaic-based integrated energy system considering different parameters like availability of solar photovoltaic power, DC link voltage, battery state of charge (SOC), tariff and availability of grid power. Effective power-sharing among the different power sources increases system reliability and stability. Optimization is performed for optimal sizing and costing to achieve economical operation such that these photovoltaic-based integrated energy systems become affordable and are encouraged for wide use. The photovoltaic system operates at the maximum power point through a neural network-based control strategy; in addition to this, stacking in neural network topologies is also discussed for improving system accuracy in tracking the reference voltage for MPPT operation. The DC link voltage is controlled by interfacing the battery with the DC link via a bidirectional buck-boost power converter. The contribution of the work is to highlight the role of renewable energy sources for continuous green hydrogen production. Green hydrogen will be a critical driving force for the future transportation system, fuel cell-based power generation, and industries utilizing hydrogen in direct or indirect forms-that will help transition towards a carbon-free society. Optimization and time-domain simulation results indicated the economic and technical feasibility of the proposed photovoltaic-based integrated energy system for green hydrogen production with the best optimal configuration producing hydrogen at the cost of $ 4.806/kg and total net present cost of $749904.

Several strategies have been suggested to optimize power extracted from the Standalone Variable Speed Wind Energy Conversion System (SVSWECS) using a Squirrel Cage Induction Generator (SCIG). Where, many of them use the battery as a storage unit for future use. However, the latter is assumed as an ideal component and that no maximum charging or critical limit. With the considered strategy, the generated wind power can be managed using a fast and reliable fully rugged electrical control. Indeed, the use of reliable Power Management and Control (PMC) algorithm where the battery current is well controlled. However, this solution allows maintaining the system operation in two stable modes.The considered system consists of an SCIG connected mechanically to the wind turbine through a gearbox, and electrically through a PWM rectifier to the DC bus capacitor, the latter is connected to the load and the battery through a PWM inverter and buck-boost converter respectively. In this paper, to approach the system to reality, a new PMC is proposed using a purely reliable electrical control. The Direct Torque Control (DTC) is used. The latter is combined with the Maximum Power Point Tracking (MPPT) technique and the State Of Charge (SOC) of battery. The important key of the proposed control strategy are the SOC and the sign of the difference between the generated and the demanded powers. The obtained simulations results are assessed and carried out using MATLAB/Simulink package and show the effectiveness and the performance of the proposed algorithm.

Most of the power control (PC) algorithms assumed that the link gains are fixed, and the power control command can take any value in the real field. These two assumptions are unrealistic in actual communication systems, since link gains are time varying and power control commands must be sent over capacity limited channels. In this paper, we propose a binary feedback power control algorithm (BFPC) for time-varying link gain environments. This algorithm can possibly be implemented as a 1-bit increase/decrease power control command, and we construct a connection between the link gains and the step-size of the proposed algorithm. It is shown that the proposed algorithm drives the CIR to an interval in time-varying link gain environments. Simulation results show that the proposed algorithm is more robust against link gains changes as compared to Foschini and Miljanic's distributed algorithm (FMA) and proportional power control algorithm (PropPC), and the proposed algorithm can be adopted in practical mobile communication systems.

This paper surveys research methodologies different QoS and power management algorithms that guarantee enhancement of services in modern wireless communication systems. Power management is one of the most important system requirements for any wireless transmission to guarantee QoS. In this paper we will show different approaches, which solves QoS issues with respect to power management for wireless transmission. We will define the power management problems and requirements for all types of wireless technologies and what researcher should focus on, regarding power management to guarantee QoS in wireless transmission.

Power control algorithms for mobile ad hoc networks

Joint power control with optimal beamforming design and power control with minimum antenna selection under signal to inference noise ratio constraints have been considered in separate wireless communication scenarios. In power control algorithms, multiple antennas with linear beamforming are used to reduce the interference power, which also minimizes the total transmitted power in uplink/downlink channels, while the minimum antenna selection problem is solved to reduce the computational complexity at the base stations as well as to reduce the transmits power within a group of users. In this paper, we jointly consider the power control and optimal beamforming design algorithm with limited number of best antenna set selection in uplink communication. Simulation results show that the power control algorithm combined with the best number of antenna selection method can minimize power. Consequently, the proposed algorithm can reduce significantly the computational complexity while retaining the benefits that stem from solving the sum of transmitted power minimization problem.

A new approach for dynamic reliability and power management of integrated systems, such as systems on chips (SoCs) and networks in chips (NoCs) is presented. With aggressive transistor scaling, decreased voltage margins, and increased processor power and temperature, reliability assessment has become a significant issue in design. Our work combines for the first time dynamic power management with reliability models. The joint model is used to determine system level reliability as a function of failure rates, system configuration and power management policies. We show that the overall system reliability is strongly affected by reliability network topology and power management policy.

In this paper, power management algorithms for energy harvesting sensors (EHS) that operate purely based on energy harvested from the environment are proposed. To maintain energy neutrality, EHS nodes schedule their utilization of the harvested power so as to save/draw energy into/from an inefficient battery during peak/low energy harvesting periods, respectively. Under this constraint, one of the key system design goals is to transmit as much data as possible given the energy harvesting profile. For implementational simplicity, it is assumed that the EHS transmits at a constant data rate with power control, when the channel is sufficiently good. By converting the data rate maximization problem into a convex optimization problem, the optimal load scheduling (power management) algorithm that maximizes the average data rate subject to energy neutrality is derived. Also, the energy storage requirements on the battery for implementing the proposed algorithm are calculated. Further, robust schemes that account for the insufficiency of battery storage capacity, or errors in the prediction of the harvested power are proposed. The superior performance of the proposed algorithms over conventional scheduling schemes are demonstrated through computations using numerical data from solar energy harvesting databases.

This paper studies an uplink cell-free massive multiple-input multiple-output (MIMO) system with imperfect channel state information and maximum-ratio combining detectors over Rayleigh fading channel. For such a system, this paper analyzes the performance of symbol error rate (SER), which plays a key role in system reliability. Firstly, the probability density function of the desired signal and the equivalent noise are analytically calculated for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>-ary pulse amplitude modulation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>-ary quadrature amplitude modulation, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>-ary phase-shift keying, respectively. Next, based on these analyses, the novel analytical expressions for the SER of different modulations are derived, which is verified by numerical results. Then, in order to enhance the SER performance of all users in different geographical locations, a geometric programming based power control (GPPC) algorithm is proposed. Finally, numerical results show that compared with four benchmark schemes of the full power algorithm, the fractional power control algorithm, the traditional max-min fairness power control algorithm, and the genetic algorithm, the proposed GPPC algorithm can effectively minimize the maximum SER among all users through collaborative control of the power control coefficient while ensuring lower computational complexity.

Power management systems for embedded devices can be developed in operating system (OS) or in applications. If power management policy is applied in OS, then developers can concentrate only on application development. OS contains specific and accurate information about the various tasks being executed. Therefore, it is logical to place algorithms that place components not being used into lower power states. This can significantly reduce the energy consumption by the system. Real-time-operating-system (RTOS) has a comprehensive set of power management application programming interfaces (APIs) for both device drivers and applications within a power management component. In this paper, we provide abstracted concepts of a system power manager (PM), device power managers, and application power managers. We present relationship and interactions of these managers with each using Unified Modeling Language (UML) class diagrams, sequence diagrams and state charts. We recommend that PM must be implemented at operating system level in any embedded device. We also recommend the interfaces for interactions between PM and the devices power manager, as well as PM and application power manager. Device driver and application developers can easily use this object oriented approach to make the embedded system more power efficient, easy to maintain, and faster to develop

With a narrowband multiuser detector (MUD), multiple users can simultaneously share the same channel without using a bandwidth expanding signature code when sufficient power differences at the receiver are maintained. In this paper, we estimate the capacity that can be achieved in a hypothetical cellular system with centralized power control, using a narrowband MUD based on successive cancellation. The power control algorithm is adapted to multiple users per channel in a cell. Capacity results from simulations based on user removal are presented for three removal algorithms. It is shown that the narrowband MUD has a significant potential for capacity gain due to a tighter packing of users in a channel.

In this paper we study the performance of two Power Control algorithms: the Adaptive Standard Power Control (ASPC) and the Standard Power Control (PCA) algorithms. Simulation of both algorithms is being done under three different network conditions including the case with varying SIR target. In order to achieve robust estimates of the performance we have undertaken Monte-Carlo analysis of 500 runs for each case. These results show that the performance ASPC is be better than PCA under the different network conditions considered in this paper. All simulations are done in MATLAB.

In wireless communication systems, mobile users adapt to a time varying radio channel by regulating transmitter powers. This power control is intended to provide each user an acceptable connection, as measured by a carrier to interference ratio (CIR), by eliminating unnecessary interference. It is important that a power control algorithm can converge quickly to a fixed point at which either all users have acceptable connections or an infeasibility can be detected. In this work, we show that an iterative power control and base station assignment algorithm based on CIR measurements converges to a unique fixed point at a geometric rate. This conclusion is shown to hold even if some or all of the users are subject to maximum power constraints. The rate of convergence is evaluated by simulation of a one dimensional CDMA system.

Power dissipation has become a critical design metric in microprocessor-based system design. In a multi-core system, running multiple applications, power and performance can be dynamically traded off using an integrated power management (PM) unit. This PM unit monitors the performance and power of each core and dynamically adjusts the individual voltages and frequencies in order to maximize system performance under a given power budget (usually set by the operating system). This paper presents a performance and power analysis methodology, featuring a simulation model for multi-core systems that can be easily reconfigured for different scenarios and a PM infrastructure for the exploration and analysis of PM algorithms. Two algorithms have been implemented: one for discrete and one for continuous power modes based on non-linear programming. Extensive experiments are reported, illustrating the effect of power management both at the core and the chip level.

In distributed power generation systems, the two fundamental issues are generation capacity and sustainability. Recently, these challenges have been greatly impacted by lighting loads. DC-fed LED lighting offers a superior approach. The traditional grid requires a rectifier, which comes at a high cost and lower efficiency to obtain DC outputs. To address these issues, this research work presents a dependable Independent PV-Battery (IPVB) systems fed LED load that uses a Power Management Algorithm (PMA) with Maximum Power Point Tracking (MPPT). Employing the different step size- Improved INC method significantly reduces the undesired fluctuations at MPP caused by the traditional Incremental Conductance (INC) technique. With a controlled DC voltage bus of 36 V, the non - isolated dc bilateral converter in the PMA serves as a charge controller that chooses the batteries charging and discharging phase based on the battery's level of capacity, solar irradiance power, and converters output power. Through simulations, the efficiency of three MPPT and PMA has indeed been examined.