Dynamic Power Management Approaches Research Articles

An adaptive dynamic power management approach for enhancing operation of microgrid with grid ancillary services

The increasing prevalence of photovoltaic (PV)-wind-battery-based microgrids and their integration into distribution networks have brought new challenges in grid ancillary services, such as poor power quality (PQ), grid instability, voltage and current fluctuations, and low efficiency of microgrids. These adverse effects are due to the intermittent nature of PV and wind power generation. The present work proposes an adaptive dynamic power management (ADPM) approach and enhanced instantaneous symmetrical component theory (EISCT) to improve the microgrid operation and grid ancillary services. The proposed ADPM-based EISCT not only utilizes a single grid side converter (GSC) but also reduces the sensor requirement and control structure complexity and thereby eliminates communication mismatch. The control structure, proposed in this research, has two major segments. The first segment focuses on active power management control through ADPM to facilitate efficient power flow between the utility grid and microgrid sources which helps in ensuring efficient power sharing. The second component is dedicated to synchronizing the renewable energy sources of the microgrid with the utility grid and simultaneously maintaining PQ compliance as per IEEE 519–2022 standards. The efficacy and adaptability of the proposed structure are validated through MATLAB®/Simulink® environment and the practical superiority of the system has been tested by using MicroLabBox® DS1202-based real-time hardware set-up. The experimental result shows improved performance of the developed structure under various microgrid operating conditions including dynamic loading situations, and battery system state-of-charge (SOC). The simulation and experimental results suggested that the DC-link ripples have been reduced to 2%, peak overshoot to 1.8%, settling time during the transition to 1.2s, battery response time to 0.3s, and the total harmonic distortion (THD) to less than 5%. These results demonstrated the superiority of the ADPM-based EISCT over conventional strategies.

Power control management system model using wireless sensor network

Several researchers would study the development of controlling and centralized management approaches in response to improvements in wireless communication, microsystems, microcontrollers, deeply integrated computers, deeply integrated devices, and the expanding demand for more productive controlled electric equipment. The objective is to develop a computerized device that employs Wireless Sensor Networks (WSN) and Dynamic Power Management (DPM) to supervise and troubleshoot the electrical system. The Intelligent Sensor Modules (ISM) and isolated data automation systems, that are in charge of Signal Detection, Computing, and Transmission, have been deployed on two network architectures (SDCT). The data collection devices must be accepted by the Remote Servers (RS) design, and the monitoring inverter serves as the wired connection's gateway. A DPM approach is used by the sensor networks to extend the WSN's lifespan. By simply extending the sleep duration, which could be modified according to the application operating on the sensor network, the life of the node can be easily increased.

PoweRock: Power Modeling and Flexible Dynamic Power Management for Many-Core Architectures

Energy-efficient design for green high-performance computing (HPC) is a formidable mission for today's computer scientists. Dynamic power management (DPM) is a key enabling technology involved. While DPM can have various goals for different application scenarios (e.g., enforcing an upper bound on power consumption or optimizing energy usage under a performance-loss constraint), existing DPM solutions are generally designed to meet only one goal and not adaptable to changes in optimization objectives. This paper proposes a novel flexible DPM approach based on a profile-guided dynamic voltage/frequency scaling (DVFS) scheme to meet the different goals. Our contributions include 1) an accurate power prediction model for many-core architectures; 2) a new profiling method for (distributed) shared-memory parallel applications to flexibly determine the optimal frequency and voltage for different phases of the execution; and 3) a hierarchical domain-aware power control design boosting the DPM system scalability for many-core chips. We implement the approach into a working library dubbed PoweRock and evaluate it on the Intel SCC port of the Barrelfish operating system. Experimental results obtained from several well-known benchmarks show that PoweRock attains significant energy and energy-delay product (EDP) improvements (average: 37.1% and 25.5%; best case: 64.0% and 65.4%, respectively) over a static power scheme.

An Integrated Security-Constrained Model-Based Dynamic Power Management Approach for Isolated Microgrids in All-Electric Ships

Isolated microgrid power systems are prone to large frequency and voltage deviations, since they have limited generation and finite inertia and may include pulse power loads and large proportion of dynamic loads. Thus, they require effective power management methods to operate optimally, while satisfying operating and security constraints. In this paper, a novel integrated security-constrained model-based power management approach is proposed for isolated microgrids in all-electric ships during the normal/alert operating state. The new power management method was formulated as a multi-objective optimal control problem. A method based on the Nondominated Sorting Genetic Algorithm II was developed to solve the problem. The developed integrated security-constrained power management approach was applied to a notional all-electric ship computer model. The simulation results, conducted in PSCAD, showed the effectiveness of the new power management method.

Hierarchical power management of a system with autonomously power-managed components using reinforcement learning

This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.

Online learning of timeout policies for dynamic power management

Dynamic power management (DPM) refers to strategies which selectively change the operational states of a device during runtime to reduce the power consumption based on the past usage pattern, the current workload, and the given performance constraint. The power management problem becomes more challenging when the workload exhibits nonstationary behavior which may degrade the performance of any single or static DPM policy. This article presents a reinforcement learning (RL)-based DPM technique for optimal selection of timeout values in the different device states. Each timeout period determines how long the device will remain in a particular state before the transition decision is taken. The timeout selection is based on workload estimates derived from a Multilayer Artificial Neural Network (ML-ANN) and an objective function given by weighted performance and power parameters. Our DPM approach is further able to adapt the power-performance weights online to meet user-specified power and performance constraints, respectively. We have completely implemented our DPM algorithm on our embedded traffic surveillance platform and performed long-term experiments using real traffic data to demonstrate the effectiveness of the DPM. Our results show that the proposed learning algorithm not only adequately explores the power-performance trade-off with nonstationary workload but can also successfully perform online adjustment of the trade-off parameter in order to meet the user-specified constraint.

Cooperative boosting

This paper examines the interaction between thermal management techniques and power boosting in a state-of-the-art heterogeneous processor consisting of a set of CPU and GPU cores. We show that for classes of applications that utilize both the CPU and the GPU, modern boost algorithms that greedily seek to convert thermal headroom into performance can interact with thermal coupling effects between the CPU and the GPU to degrade performance. We first examine the causes of this behavior and explain the interaction between thermal coupling, performance coupling, and workload behavior. Then we propose a dynamic power-management approach called cooperative boosting (CB) to allocate power dynamically between CPU and GPU in a manner that balances thermal coupling against the needs of performance coupling to optimize performance under a given thermal constraint. Through real hardware-based measurements, we evaluate CB against a state-of-the-practice boost algorithm and show that overall application performance and power savings increase by 10% and 8% (up to 52% and 34%), respectively, resulting in average energy efficiency improvement of 25% (up to 76%) over a wide range of benchmarks.

Neural Network Based Energy Efficiency in Wireless Sensor Networks: A Survey

The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network, have drawn attentions of many research studies. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. Dimensionality reduction and prediction of sensor data obtained simply from the outputs of the neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics show great analogy and compatibility between wireless sensor networks and neural networks. This paper aims to present the most important possible application of neural networks in reduction of energy consumption according to research studies have been done in the literature so far through a general classification of them.

Uncertainty-Aware Dynamic Power Management in Partially Observable Domains

This paper tackles the problem of dynamic power management (DPM) in nanoscale CMOS design technologies that are typically affected by increasing levels of process and temperature variations and fluctuations due to the randomness in the behavior of silicon structure. This uncertainty undermines the accuracy and effectiveness of traditional DPM approaches. This paper presents a stochastic framework to improve the accuracy of decision making during dynamic power management, while considering manufacturing process and/or environment induced uncertainties. More precisely, variability and uncertainty at the system level are captured by a partially observable semi-Markov decision process with interval-based definition of states while the policy optimization problem is formulated as a mathematical program based on this model. Experimental results with a RISC processor in 65-nm technology demonstrate the effectiveness of the technique and show that the proposed uncertainty-aware power management technique ensures system-wide energy savings under statistical circuit parameter variations.

Dynamic Power Management with Scheduled Switching Modes

A Wireless Sensor Network (WSN) comprises many sensor nodes each one containing a processing unit, one or more sensors, a power unit, and a radio for data communication. Nodes are power constrained, because they run on batteries which usually cannot be replaced due to the nature of the applications. We present a novel dynamic power management approach, named Dynamic Power Management with Scheduled Switching Modes (DPM-SSM), derived from a more realistic analysis of the battery capacity recovery effect and the switching energy. This was only possible thanks to the application of a more realistic battery model (i.e., Rakhmatov–Vrudhula battery model). We also devised a Hybrid Differential Petri Nets formalism to evaluate our power management solution. Preliminary results showed the potential for improving the battery lifetime by taking advantage of the battery recovery effect when a node transitions to a sleeping state mostly after packet transmissions. DPM-SSM provides several DPM modes which are triggered depending on the battery remaining capacity. Simulations results show that, depending on the scheduling approach, DPM-SSM can provide real battery power recovery without compromising the timeliness of the applications running on the sensor network.