Power Management Framework Research Articles

An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids

In this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.

A Robust Optimization Approach for Active and Reactive Power Management in Smart Distribution Networks Using Electric Vehicles

This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. First, based on duality theory, the max min form of the model is converted to a max form. Second, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.

Online learning based on a novel cost function for system power management

A novel system power management technique is proposed that employs a novel cost function based on state-action. Compared with the conventional algorithm, by using multiple parameter constraints in cost function of power management framework, the improved Q-learning can effectively make decisions to achieve a rational optimisation room. The proposed power management framework does not need any prior data and is running on a power model. As uncertainties can be effectively captured and modelled, the framework based on the model can help to explore an ideal trade-off and converge to the best power management policy. The results obtained showed that improved algorithm achieved remarkable significance.

Power Management for Kinetic Energy Harvesting IoT

Human kinetic energy is regarded as a promising sustainable energy source to solve the energy bottleneck of Internet of Things (IoT). The low power harvested from human motion and scarce hardware resource of IoT severely restrain the operation of kinetic energy harvesting IoT and stress the need for power management strategies to improve the energy efficiency. In this paper, we propose a novel power management framework for kinetic energy harvesting IoT, composed of an off-line inertial harvester optimization algorithm and an on-line joint sink selection and transmission power control module. By analyzing the characteristics of human daily motion and the inertial harvester model, the optimal inertial harvester parameters are determined to maximize the power generation from human daily motion. The on-line scheme improves energy efficiency by joint consideration of optimal sink selection (i.e., on-body sink or off-body sink) and transmission power control. The real world human motion data set is used to evaluate the proposed framework. The simulation results indicate that, compared with the existing approach, the proposed kinetic harvester optimization algorithm achieves 83.31% to 135.69% improvement in harvested power from the same human motion trace. In addition, the proposed on-line joint sink selection and transmission power control incurs 7.07% to 34.23% improvement in transmission energy efficiency.

Predictive Power Management for Internet of Battery-Less Things

Energy harvesting technology provides a promising solution to enable internet of battery-less things (IoBT), as the lifetime and size of batteries become major limiting factors in the design and effective operation of internet of things (IoT). However, with constrained energy buffer size, the variation of ambient energy availability and wireless communication cast adverse effect on the operation of IoBT. There is a pressing demand for developing IoBT-specialized power management. In this paper, we propose a novel predictive power management (PPM) framework combining optimal working point, deviation aware predictive energy allocation, and energy efficient transmission power control. The optimal working point guarantees minimum power loss of IoBT systems. By predictively budgeting the available energy and using the optimal working point as a set-point, PPM mitigates the prediction error so that both power failure time and system power loss is minimized. The transmission power control module of PPM improves energy efficiency by dynamically selecting optimal transmission power level with minimum energy consumption. Real-world harvesting profiles are tested to validate the effectiveness of PPM. The results indicate that compared with the previous predictive power managers, PPM incurs up to $17.49\%$ reduction in system power loss and $93.88\%$ less power failure time while maintaining a high energy utilization rate. PPM also achieves $9.4\%$ to $23.22\%$ of maximum improvement of transmission energy efficiency compared with the state-of-the-art transmission power control schemes.

Implementation of Optimization-Based Power Management for All-Electric Hybrid Vessels

The increasing concern in environmental impacts contributed by the shipping industry leads to a growing interest toward incorporating sustainable energy sources to conventional power systems, to reduce fuel consumption and emissions. However, such advance power system architectures tend to face greater challenges in optimal power allocation due to the increased flexibility to split the load among multiple power sources and diversified operations of different vessel types. To address the power management challenges, in this paper, an instantaneous optimization-based approach is proposed for an all-electric hybrid power system, which adopts the equivalent consumption minimization strategy (ECMS). First, the dynamic models of the all-electric hybrid power system are formulated in MATLAB $\backslash $ Simulink and validated on a laboratory-scaled hybrid power system testbed. The power management problem is then formulated using ECMS approach, and a multi-level power management framework is proposed to integrate ECMS solution for practical implementation on an all-electric hybrid vessel. Finally, the feasibility of the proposed approach is validated on a full-scale setup identical to an actual all-electric hybrid vessel with dc distribution in MARINTEK, Trondheim, Norway. The performance of ECMS is also benchmarked against an improve rule-based strategy. The experimental results demonstrated that ECMS can achieve a substantial fuel savings of up to 24.4% as compared with improved rule-based strategy over one cycle of harbor tugboat operations, with the presence of available shore charging.

An intelligent power monitoring and analysis system for distributed smart plugs sensor networks

With the growth in power demand, energy management is an important issue in the 21st century. This article proposes a smart power management framework system, which comprises three parts. Part 1: a smart plug. Controlling the switching power supply, different sensors can be mounted for different application environments. The power supply can be switched on/off automatically according to environmental changes. Added to this, it can measure voltage and current for analysis. Part 2: a smart gateway. This can act as the mediation module for communication and implement the concept of fog computing. The local inference model is built and deployed by deep learning, and the model is learned, updated, and improved continuously to increase the intelligent control efficiency. Part 3: a management platform and mobile app. This allows for data visualization and a remote control for a user interface medium for scheduling. The smart plug and smart gateway are integrated into the overall distributed sensor network, analyzing and improving the power consumption effectively. Finally, the feasibility and practicability of the overall power management framework system are described experimentally.

A novel performance constrained power management framework for cloud computing using an adaptive node scaling approach

Cloud computing is an on-demand IT resource delivery technology that is aided by server virtualization and load balancing. Power and performance management to improve operational efficiency and increase compaction are important considerations from a cloud service economic point of view. The objective of the present study was to draw new insights from existing approaches and techniques to design an innovative self-adapting mechanism to address the mismatch between server's energy-efficiency characteristics and the behavior of server-class workloads, which solves the power versus performance trade-off problem at cloud data centers. The proposed system was simulated and evaluated for highly variable cloud workloads. The results suggest that the proposed system functions reliably for cloud workloads and ensures an optimal server workload distribution (i.e., determines the allocations of the VM server), minimizing the average power consumption of the servers and ensuring that the average task response time does not exceed given performance limitations.

Multimedia Content Delivery with Network Function Virtualization: The Energy Perspective

Multimedia now accounts for the largest share of all Internet traffic, highlighted by its volume, variety, multicast nature, and QoS constraints. Downstream toward consumers, multimedia traffic can traverse through middleboxes, undergoing additional data processing imposed by content providers and distributors. With the advent of network function virtualization (NFV), middleboxes are progressively embedded in off-the-shelf, general-purpose servers. Despite the benefits, NFV can incur an undue amount of energy consumption during high packet forwarding. In this article, the authors investigate how switching to state-of-the-art NFV products for multimedia content delivery can result in significant energy costs. They identify energy inefficiency in the NFV data plane, which can be exacerbated if not handled properly. They outline a power management framework that considers characteristics of multimedia traffic and exploits CPU frequency scaling to save energy. This article is part of a special issue on advancing multimedia distribution.

Accuracy-Aware Power Management for Many-Core Systems Running Error-Resilient Applications

Power capping techniques based on dynamic voltage and frequency scaling (DVFS) and power gating (PG) are oriented toward power actuation, compromising on performance and energy. Inherent error resilience of emerging application domains, such as Internet-of-Things (IoT) and machine learning, provides opportunities for energy and performance gains. Leveraging accuracy-performance tradeoffs in such applications, we propose approximation (APPX) as another knob for close-looped power management, to complement power knobs with performance and energy gains. We design a power management framework, APPEND+, that can switch between accurate and approximate modes of execution subject to system throughput requirements. APPEND+ considers the sensitivity of the application to error to make disciplined alteration between levels of APPX such that performance is maximized while error is minimized. We implement a power management scheme that uses APPX, DVFS, and PG knobs hierarchically. We evaluated our proposed approach over machine learning and signal processing applications along with two case studies on IoT—early warning score system and fall detection. APPEND+ yields $1.9\times $ higher throughput, improved latency up to five times, better performance per energy, and dark silicon mitigation compared with the state-of-the-art power management techniques over a set of applications ranging from high to no error resilience.

Model-Free Reinforcement Learning and Bayesian Classification in System-Level Power Management

To cope with uncertainties and variations that emanate from hardware and/or application characteristics, dynamic power management (DPM) frameworks must be able to learn about the system inputs and environmental variations, and adjust the power management policy on the fly. In this paper, an online adaptive DPM technique is presented based on the model-free reinforcement learning (RL) method, which requires no prior knowledge of the state transition probability function and the reward function. In particular, this paper employs the temporal difference (TD) learning method for semi-Markov decision process (SMDP) as the model-free RL technique since the TD method can accelerate convergence and alleviate the reliance on the Markovian property of the power-managed system. In addition, a novel workload predictor based on an online Bayesian classifier is presented to provide effective estimation of the workload characteristics for the RL algorithm. Several improvements are proposed to manage the size of the action space for the learning algorithm, enhance its convergence speed, and dynamically change the action set associated with each system state. In the proposed DPM framework, power-latency tradeoffs of the power-managed system can be precisely controlled based on a user-defined parameter. Extensive experiments on hard disk drives and wireless network cards show that the maximum power saving without sacrificing any latency is 18.6 percent compared to a reference expert-based approach. Alternatively, the maximum latency saving without any power dissipation increase is 73.0 percent compared to the existing best-of-breed DPM techniques.

Integrated Optimization of Battery Sizing, Charging, and Power Management in Plug-In Hybrid Electric Vehicles

This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.

A Framework for Dynamic Power Management at Network Level

the current scenario of computing, distributing is gaining popularity as many of users are connected with their handheld device across the globe. The connected device generally emits the energy which is of different categories and it is major challenge to optimize the entire energy of the system. The present paper proposes a frame work for the dynamic power management for completion of tasks in minimum time frame which are routed from one node to another node across the network. For this purpose, a step topological network is selected at the application level of the model. Computed results are depicted for the dynamic power management in the forms of graphs.

Coordinating workload balancing and power switching in renewable energy powered data center

There has been growing concern about energy consumption and environmental impact of datacenters. Some pioneers begin to power datacenters with renewable energy to offset carbon footprint. However, it is challenging to integrate intermittent renewable energy into datacenter power system. Grid-tied system is widely deployed in renewable energy powered datacenters. But the drawbacks (e.g. Harmonic disturbance and costliness) of grid tie inverter harass this design. Besides, the mixture of green load and brown load makes powermanagement heavily depend on software measurement and monitoring, which often suffers inaccuracy. We propose DualPower, a novel power provisioning architecture that enables green datacenters to integrate renewable power supply without grid tie inverters. To optimize DualPower operation, we propose a specially designed power management framework to coordinate workload balancing with power supply switching. We evaluate three optimization schemes (LM, PS and JO) under different datacenter operation scenarios on our trace-driven simulation platform. The experimental results show that DualPower can be as efficient as grid-tied system and has good scalability. In contrast to previous works, DualPower integrates renewable power at lower cost and maintains full availability of datacenter servers.

A New Framework for Microgrid Management: Virtual Droop Control

Microgrids can provide the most promising means of integrating large amounts of distributed sources into the power grid and can supply reliable power to critical loads. A generalized control framework is required to regulate microgrid voltage and frequency, maintain system stability, maintain power quality, and manage distributed generations. A microgrid control framework for power management and voltage and frequency regulation is proposed in this paper. The proposed method, virtual droop control, is described and formulated and compared with the existing natural droop control technique. Unit commitment algorithm has also been implemented to manage nonrenewable sources to improve the system efficiency. The proposed technique operates the microgrid at a constant voltage and frequency, and uses communication for power sharing. It also provides the means to operate the microgrid in case of lost communication and sabotage on communication network. The proposed method has been applied to Fort Sill microgrid and the modeling results have been compared with natural droop control technique. A laboratory setup, which consists of a 100 kW natural gas generator, 56 kWh Li-ion based battery with 250 kW inverter, and 100 kW load bank, has been built and tested. The results of the setup have been provided, which confirms the viability of the proposed technique.

Energy Aware Processor Architecture for Effective Scheduling and Power Management in Cloud Using Inclusive Power-Cognizant Processor Controller

The fast acceptance of cloud technology to industry explains increasing energy conservation needs and adoption of energy aware scheduling methods to cloud. Power consumption is one of the top of mind issues in cloud, because the usage of cloud storage by the individuals or organization grows rapidly. Developing an efficient power management processor architecture has gained considerable attention. However, the conventional power management mechanism fails to consider task scheduling policies. Therefore, this work presents a novel energy aware framework for power management. The proposed system leads to the development of Inclusive Power-Cognizant Processor Controller (IPCPC) for efficient power utilization. To evaluate the performance of the proposed method, simulation experiments inputting random tasks as well as tasks collected from Google Trace Logs were conducted to validate the supremacy of IPCPC. The research based on Real world Google Trace Logs gives results that proposed framework leads to less than 9% of total power consumption per task of server which proves reduction in the overall power needed.

Integrated torque vectoring and power management framework for electric vehicles

An integrated vehicle control framework is presented, which uses torque vectoring across independently driven wheels for control. The approach is general in nature, but is particularly well suited for electric vehicles due to increased control bandwidth. The novel algorithm optimizes wheel torque outputs in real time, constraining against power management, traction control, chassis configuration, actuator limits, and fault-case limitations. The structure is modular, and designed to adapt for differing vehicles with minimal re-tuning. Simulation and experimental results are provided for a modified electric SUV platform, under a range of dynamic maneuvers in 4WD, FWD, and RWD modes.

Load-adaptive networking for energy-efficient wireless access

Energy-efficient operation is essential for mobile network operators to meet the growing demand for higher data rates while managing rising operating costs. Here, the main challenge is to guarantee the quality of user experience whilst saving energy. This challenge demands adaptive algorithms that enable a load-aware network operation that dynamically configures different network elements according to user needs. To this end, in this paper, we present an adaptive and context-aware power management framework for networks composed of different radio access technologies. We implement and evaluate our framework in an indoor and outdoor testbed. The experimental results confirm that significant energy can be saved in practice by efficiently adapting resources to the actual traffic demand.

Frequency-Coordinating Virtual Impedance for Autonomous Power Management of DC Microgrid

In this paper, the concept of frequency-coordinating virtual impedance is proposed for the autonomous control of a dc microgrid. This concept introduces another degree of freedom in the conventional droop control scheme, to enable both time-scale and power-scale coordination in a distributed microgrid system. As an example, the proposed technique is applied to the coordinating regulation of a hybrid energy storage system composed of batteries and supercapacitors. With an effective frequency-domain shaping of the virtual output impedances, the battery and supercapacitor converters are designed to absorb low-frequency and high-frequency power fluctuations, respectively. In this way, their complementary advantages in energy and power density can be effectively exploited. Furthermore, the proposed concept can be integrated into a mode-adaptive power management framework with autonomous mode transitions. The entire solution features highly versatile functions based on fully decentralized control. Therefore, both flexibility and reliability can be enhanced. The effectiveness of the presented solution is verified by experimental results.

Automated OS-level Device Runtime Power Management

Non-CPU devices on a modern system-on-a-chip (SoC), ranging from accelerators to I/O controllers, account for a significant portion of the chip area. It is therefore vital for system energy efficiency that idle devices can enter a low-power state while still meeting the performance expectation. This is called device runtime Power Management (PM) for which individual device drivers in commodity OSes are held responsible today. Based on the observations of existing drivers and their evolution, we consider it harmful to rely on drivers for device runtime PM. This paper identifies three pieces of information as essential to device runtime PM, and shows that they can be obtained without involving drivers, either by using a software-only approach, or more efficiently, by adding one register bit to each device. We thus suggest a structural change to the current Linux runtime PM framework, replacing the PM code in all applicable drivers with a single kernel module called the central PM agent. Experimental evaluations show that the central PM agent is just as effective as hand-tuned driver PM code. The paper also presents a tool called PowerAdvisor that simplifies driver PM efforts under the current Linux runtime PM framework. PowerAdvisor analyzes execution traces and suggests where to insert PM calls in driver source code. Despite being a best-effort tool, PowerAdvisor not only reproduces hand-tuned PM code from stock drivers, but also correctly suggests PM code never known before. Overall, our experience shows that it is promising to ultimately free driver developers from manual PM.