Power Management Techniques Research Articles

Power Management Strategy Based on Virtual Inertia for DC Microgrids

This article presents a power management strategy (PMS) to control the power flow in a dc microgrid operating in the grid-connected mode. The microgrid model is composed of the ac utility grid interfaced with a voltage source inverter operating as a grid-forming converter (VSC), an energy storage system (ESS) formed by a battery bank and a bidirectional dc-dc converter operating as a grid-supporting unit, a distributed generation acting as a grid-feeding unit, and the customer loads with strict voltage regulation. The power management technique applies a virtual inertia concept together with a state of charge-based management function to regulate the charging and discharging process of the battery bank according to the dc microgrid power flow. Thus, high-rate peaks of power are avoided, which improves the ESS life cycle. With the proposed PMS, an autonomous power flow is achieved inside the dc microgrid, while the ac utility grid focuses exclusively upon forming the dc bus and processing the surplus or shortage of power. In addition, the proposed strategy simplifies the communication link between the grid inverter and the ESS, since the VSC power is the sole information exchanged. The experimental results show that the proposed PMS is reliable, leading to high ESS performance and power flow control within the dc microgrid, without degrading the dc bus voltage.

Proactive power management in IBM z15

The IBM z15 processor power management enhances several on-chip power management techniques over z14 processor with a specific focus on reducing response time for voltage droop management. The IBM z15 processor puts a specific emphasis on proactive voltage droop management strategy to reduce conservative static guard band that is added to the supply voltage in order to protect against worst-case voltage droops. The z15 processor relies on selected events from the earlier stages of a deep pipeline processor as indicators to predict sharp changes in the power consumption over a short period of time. The early information of the selected events allows to throttle the execution flow through the processor pipeline and prevents the sharp power change before it takes place and thus reduces the voltage droop. In z15, as one of the proactive schemes, we combine both the digital power-proxies, which are direct indicators of the processor activity and the Critical Path Monitors (CPMs) to give an earlier and proactive indication of voltage droop events. This proactive indication provides enough time for the throttle actuation circuits to prevent the voltage droop. CPMs act as real-time timing margin indicators, and power-proxies act to serve as the activity monitors.

Assessments of data centers for provision of frequency regulation

There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation-based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. When chillers are deactivated, both strategies have a similar regulation capacity.

Power consumption management under a low-level performance constraint in the Xen hypervisor

Virtualization is the main building block of many architectures and systems from embedded computing to large scale data-centers. Managing efficiently computing resources and their power consumption becomes fundamental to optimize the performance of the workloads running on those systems, however, hardware tools like Intel RAPL can only introduce power caps without considering performance. This paper presents a performance-aware power capping orchestrator for the Xen hypervisor. The tool exploits hybrid power management techniques to minimize power consumption respecting a given SLA, leveraging RAPL and overcoming its limitations. Experimental evaluation shows that the proposed approach guarantees good results for almost all the analyzed benchmarks (e.g. CPU-, memory- and IO-bound).

An Application-Specific Approach to Energy Optimization on Android Mobile Devices

On current Android mobile devices, DVFS is a key power management technique. Besides CPU, memory and GPU support DVFS as well. OS modules known as governors implement algorithms that dynamically adjust operating frequency for their respective hardware components to balance performance and power. However, the stock governors do not consider application characteristics, manager power instead of energy, and operate independently of each other, thus leading to energy inefficiency. In this paper, we present an application-specific approach to energy optimization targeting Android mobile devices. In this two-step approach, we first perform offline profiling of the target app to obtain its performance and power measurements under different DVFS settings. The profiling data are then used by a feedback loop based online controller to dynamically select energy-optimal DVFS settings for the app while trying to meet a performance target. In contrast with the default governors, our approach is application-specific, manages energy, and controls both CPU and memory in unison. Tested on a few mainstream applications, our solution achieves considerable energy savings with minimal performance loss. This paper describes the design and evaluation of the system in detail with a particular focus on problems related to the robustness of the approach.

Real‐time power management technique for microgrid with flexible boundaries

In order to diminish the impacts brought by high penetration of renewable energy on the reliability of distribution systems, some distribution networks (e.g. Chattanooga electric power board system) have deployed smart switches (SSs) to island some areas to mitigate outage losses. However, due to intermittency and sharply changing rate of renewable energy, it is likely to experience insufficient or excessive power for islanded areas. Therefore, a microgrid controller featured with flexible boundaries is proposed. With proposed microgrid controller, the microgrid can not only shrink or expand its boundaries according to current renewable energy supply, but also disconnect/connect to the main grid with a designated SS. Moreover, to ensure the microgrid controller could obtain suitable boundaries on the time scale of seconds, a real-time power management technique with alternative generating algorithm is designed to generate all possible alternative boundaries and choose the optimal one, which is scalable to any topology. In addition, in order to maintain state of charge of batteries within a desirable range, anti-overcharge/discharge strategies are designed. Four comprehensive experiments verify that the implementation of the microgrid controllers can realise flexible boundaries and deal with sharply changing rate of renewable generation or load on the time scale of seconds.

A Study of Power Management Techniques in Green Computing

A Study of Power Management Techniques in Green Computing

Sim2Win: How simulation can help data centers to benefit from controlling their power profile

To support the grid and integrate renewables, demand response schemes reward the power flexibility of energy consumers. Data centers can profit from this by using power management techniques on all levels of data center architecture: infrastructure, hardware, workload, applications. Even though lately, demand response with data centers has been well researched, most works focus on just one or two techniques and one or two valorization options. This leaves data centers stranded that are not represented by the specific combinations of assumptions and techniques presented in research, and thus a huge potential remains barely touched. To address this challenge, the goal of the presented work is to provide data centers with a framework that can be flexibly instantiated by each data center to assess its individual demand response potential. To achieve this goal, this work presents Sim2Win, a data center simulation framework that can replay any set of different power management strategies in the face of any set of markets for power flexibility. A part of the framework is then instantiated and applied to the workload of a real high-performance data center. It uses workload shifting and frequency scaling in order to market their flexibility on the EPEX spot market and the secondary reserve market in Germany. The results show that by using the inherent flexibility of their power profile on the EPEX spot market the considered data center in 2014 could have earned savings of 7.3% of their power bill.

Controlled Shunt Reactor for UHVAC System Reactive Power Control

Background: In this paper, a novel technique for reactive power management of the Ultra High Voltage (UHV) lines having voltage level of 1200 kV line and above is suggested. The UHV power grid has to frequently face the problems associated with the power- frequency and switching over-voltages. Methods: The technique involves the use of three-phase UHV Transformers as a Controlled Shunt Reactor (CSR) in combination with the use of a fixed reactor. The performance of the UHV AC transmission line is studied using PSCAD software and working of CSR is evaluated. Results: Standard parameters of the proposed Wardha-Aurangabad 1200 kV transmission line in India are considered for simulation model. The use of CSR has been found to be effective in mitigating system problems. Conclusion: The system can be used for maintaining voltage profile, resulting in enhancement of the reliability of the UHV transmission system.

Power management techniques for grid-connected DC microgrids: A comparative evaluation

DC microgrids are pointed out as a good alternative in distribution systems with integration of renewable energy sources. However, the management of the power flow in the DC microgrid is still an issue under investigation by the scientific community. This article presents a qualitative comparison analysis of power management systems (PMS) for grid-connected DC microgrids. The studied DC microgrid is a basic structure formed by the AC utility grid, a battery energy storage system (BESS), a distributed generator, and the customer loads. Due to the lack of control structures specifically designed to manage grid-connected DC microgrids, a master–slave and an enhanced droop control are proposed as power management techniques. These methods are compared with the conventional droop control, an adaptive droop control based on the battery bank state-of-charge, and a hierarchical control with voltage regulation adapted to operate as PMS. The autonomy of the energy storage system, necessity of communication line, presence of voltage deviation, and inclusion of a battery management system are the most relevant PMS characteristics under analysis. The contribution of this study is a deep comparative evaluation of the PMS control techniques; considering the impact over the DC link voltage, the power flow, and the BESS state-of-charge. Moreover, the performance of each method is evaluated for a distributed generation based on solar and wind renewable energy sources. The experimental results show that the proposed master–slave technique leads to a smooth transition of the power flow inside the DC microgrid, which returns a better performance when compared to the traditional methods. On the other hand, the enhanced droop strategy is a viable alternative to be used when the communication link between power electronic converters is not desired.

Chip-Specific Power Delivery and Consumption Co-Management for Process-Variation-Aware Manycore Systems Using Reinforcement Learning

Energy efficiency has become a critical design metric for high-performance systems. Various power management techniques have been proposed for the processor cores such as dynamic voltage and frequency scaling (DVFS), whereas few solutions consider the power losses suffered on the power delivery system (PDS), despite the fact that they have a significant impact on the overall energy efficiency of the system. With the explosive growth of system complexity and highly dynamic workloads variations, it is also challenging to find the optimal power management policies which can effectively match the power delivery with the power consumption. In addition, process variations (PVs) add heterogeneity to systems and make traditional power management methods less effective. To tackle the above problems, we propose a reinforcement-learning-based Chip-Specific Power co-Management (CSPM) scheme for PV-aware manycore systems. Both PDS and processor cores are jointly adjusted by distributed agents with modular Q-learning to improve the overall energy efficiency of the system. System characteristics are naturally included in the learning process to obtain chip-specific policies. Experimental results show that when applied to PV-aware manycore systems with a hybrid PDS constructed by both on- and off-chip voltage regulators, the proposed method achieves a 60.1% reduction of the overall energy delay product (EDP) of the system, on average, compared to a traditional DVFS approach.

Towards Power Efficient High Performance Packet I/O

Recently, high performance packet I/O frameworks continue to flourish for their ability to process packets from high-speed links. To achieve high throughput and low latency, high performance packet I/O frameworks usually employ busy polling. As busy polling will burn all CPU cycles even if there's no packet to process, these frameworks are quite power inefficient. However, exploiting power management techniques such as DVFS and LPI in the frameworks is challenging, because neither the OS nor the frameworks can provide information (e.g., actual CPU utilization, available idle period, or the target frequency) required by these techniques. In this article, we establish a model that can formulate the packet processing flow of high performance packet I/O to help and address the above challenges. From the model, we can deduce the information needed for power management techniques, and gain the insights to balance the power and latency. After suggesting to use pause instruction to reduce CPU power within short idle period, we propose two approaches to conduct power conservation for high performance packet I/O: one with the aid of traffic information and the other without. Experiments with Intel DPDK show that both approaches can achieve significant power reduction with little latency increase.

Power-Management Strategies for Medical Information Transmission in Wireless Body Sensor Networks

To minimize and manage the power drain, and extend battery lifetime of wireless body sensor networks (WBSN) is one of the major challenges. There are three key purposes of this survey article, first, to examine the downsides of the classical power-management methods in WBSNs; second, considering the life-critical applications and emergency contexts that are encompassed by WBSN; and, third, studying the impact of power-management techniques on resource-confined networks for economical healthcare. A specific power-management solution is also discussed.

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

SummaryFor rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

Energy-cognizant scheduling for preference-oriented fixed-priority real-time tasks

Abstract Energy management is one of the crucial design issues when executing real-time applications with stringent timing requirements. Dynamic slowdown of processor voltage if accompanied with processor shutdown method, helps in better saving energy. Traditionally, energy management has been applied to real-time scheduling algorithms that prioritize tasks based on timing parameters only, however, recently applications having tasks with different execution-preferences on the same computing unit found significant importance in various areas. In this paper, dynamic voltage scaling (DVS) and dynamic power management (DPM) techniques are used for energy management while scheduling preference-oriented fixed-priority periodic real-time tasks. Preference-oriented energy-aware rate-monotonic scheduling (PER) and preference-oriented extended energy-aware rate-monotonic scheduling (PEER) algorithms are proposed that maximize energy savings while fulfilling preference-value of tasks. Extensive simulations show that PER and PEER outperforms in terms of energy savings when compared to several related studies.

Voltage-Stacked Power Delivery Systems: Reliability, Efficiency, and Power Management

In today's manycore processors, the energy loss of more than 20% may result from inherent inefficiencies of conventional power delivery system (PDS) design. By stacking multiple voltage domains in series to lower the step-down conversion ratio of the off-chip voltage regulator module (VRM) and reduce the energy loss along the path of the power delivery network (PDN), voltage stacking (VS) offers a novel alternative power delivery technique to fundamentally improve power delivery efficiency (PDE). However, VS suffers from aggravated supply voltage noise from the current imbalance, which hinders its adoption. In this article, we investigate practical VS implementation in manycore processors to improve PDE and achieve reliable performance, while maintaining compatibility with advanced power management techniques. We first present the system configuration of a voltage-stacked manycore processor. We then systematically characterize supply voltage noise in VS, identify global, and residual differential currents as its dominant contributors, and calculate the possible worst supply voltage noise. We next propose a hybrid voltage regulation solution, based on a charge-recycling off-chip voltage regulator and distributed integrated voltage regulators, to mitigate supply voltage noise effectively. We also study the compatibility of VS with higher-level power management techniques. Finally, the performance of a voltage-stacked GPU system is comprehensively evaluated. The simulation results show that our approach can achieve 93.5% PDE, reducing the power loss by 13.6% compared to conventional single-layer PDS.

Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey

Due to the high integration density and roadblock of voltage scaling, modern multicore processors experience higher power densities than previous technology scaling nodes. When unattended, this issue might lead to temperature hot spots, that in turn may cause nonuniform aging, accelerate chip failure, impair reliability, and reduce the performance of the system. This paper presents an overview of several research efforts that propose to use machine learning (ML) techniques for power and thermal management on single-core and multicore processors. Traditional power and thermal management techniques rely on a certain a-priori knowledge of the chip’s thermal model, as well as information of the workloads/applications to be executed (e.g., transient and average power consumption). Nevertheless, these a-priori information is not always available, and even if it is, it cannot reflect the spatial and temporal uncertainties and variations that come from the environment, the hardware, or from the workloads/applications. Contrarily, techniques based on ML can potentially adapt to varying system conditions and workloads, learning from past events in order to improve themselves as the environment changes, resulting in improved management decisions.

HiLITE: Hierarchical and Lightweight Imitation Learning for Power Management of Embedded SoCs

Modern systems-on-chip (SoCs) use dynamic power management (DPM) techniques to improve energy efficiency. However, existing techniques are unable to efficiently adapt the runtime decisions considering multiple objectives (e.g., energy and real-time requirements) simultaneously on heterogeneous platforms. To address this need, we propose HiLITE, a hierarchical imitation learning framework that maximizes the energy efficiency while satisfying soft real-time constraints on embedded SoCs. Our approach first trains DPM policies using imitation learning; then, it applies a regression policy at runtime to minimize deadline misses. HiLITE improves the energy-delay product by 40 percent on average, and reduces deadline misses by up to 76 percent, compared to state-of-the-art approaches. In addition, we show that the trained policies not only achieve high accuracy, but also have negligible prediction time overhead and small memory footprint.

A Comprehensive Survey on Energy-Efficient Power Management Techniques

In the current age of machine intelligence, computer literacy has been achieved in terms of its knowledge and ability of utilization at various stages. As compared to the urge of demanding for high computer functionalities as well as performance, the concern towards the cost in attaining such goals has been limited. The developers try to empower a large number of operations which may consume a large amount of energy. The power consumption has been perceived as an important aspect for the stability and growth of an economy of the country; it has also been recognized as one of the factors that impact the environment. This article has considered the prime implication of energy-efficient power management techniques; it discusses the necessity of adopting the energy-aware computer and reviews the existing works on relevant components. This article may be helpful to attain the essentials of green computing.

Fault-Tolerant Energy-Aware Task Scheduling on Multiprocessor System for Fixed-Priority Real-Time Tasks

Energy-aware real-time scheduling is gaining attention in recent years owing to environmental concerns and applications in numerous fields. System reliability also gets affected adversely with increasing energy dissipations posing serious challenges before the researchers. Keeping these in view, in recent times researchers have diverted to combining issues of fault-tolerance and energy efficiency. In literature, DVFS and DPM, most commonly used techniques for power management in task scheduling, are often combined with Primary/Backup technique to achieve fault tolerance against transient and permanent faults. Optimal algorithms, Earliest deadline first (EDF) and Rate-Monotonic (RM), meant for scheduling dynamic and fixed priority tasks respectively, have mainly been analyzed using a dual-processor approach for fault-tolerance and energy efficiency. In this paper, to handle higher workload of fixed-priority real-time tasks, energy-aware fault-tolerant scheduling algorithms are proposed for multiprocessor systems with balanced and unbalanced number of main and auxiliary processors. Simulations over extensive task-sets indicate that balanced approach is more energy-efficient than the unbalanced one.