Optimal Power Management for Antagonizing Between Radar and Jamming Based on Continuous Game Theory

To solve the problem of dynamic power resource allocation for cooperative penetration combat,the continuous game theory is introduced and a two-person general-sum continuous-game-based model is put forward with a common payoff function named collaborative detection probability of netted radar countermeasures.Comparing with traditional optimization methods,an obvious advantage of game-based model is an adequate consideration of the opposite potential strategy.This model guarantees a more effective allocation of the both sides′power resource and a higher combat efficiency during a combat.Furthermore,an analysis of the complexity of the proposed model is given and a hierarchical processing method is presented to simplify the calculating process.Simulation results show the validity of the proposed scheme.

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.

A Power Management Scheme for Grid-connected PV Integrated with Hybrid Energy Storage System

The penetration of renewable energy sources(RESs)in the distribution system becomes a challenge for the reliable and safe operation of the existing power system.The sporadic characteristics of sustainable energy sources along with the random load variations greatly affect the power quality and stability of the system.Hence,it requires storage systems with both high energy and high power handling capacity to coexist in microgrids.An efficient energy management structure is designed in this paper for a grid-connected PV system combined with hybrid storage of supercapacitor and battery.The combined supercapacitor and battery storage system grips the average and transient power changes,which provides a quick control for the DC-link voltage,i.e.,it stabilizes the system and helps achieve the PV power smoothing.The average power distribution between the power grid and battery is done by checking the state of charge(SOC)of a battery,and an effective and efficient energy management scheme is proposed.Additionally,the use of a supercapacitor lessens the current stress on the battery system during unexpected disparity in the generated power and load requirement.The performance and efficacy of the proposed energy management scheme are justified by simulation studies.

Autonomous Power Management for Interlinked AC-DC Microgrids

The existing power management schemes for interlinked AC-DC microgrids have several operational drawbacks.Some of the existing control schemes are designed with the main objective of sharing power among the interlinked microgrids based on their loading conditions,while other schemes regulate the voltage of the interlinked microgrids without considering the specific loading conditions.However,the existing schemes cannot achieve both objectives efficiently.To address these issues,an autonomous power management scheme is proposed,which explicitly considers the specific loading condition of the DC microgrid before importing power from the interlinked AC microgrid.This strategy enables voltage regulation in the DC microgrid,and also reduces the number of converters in operation.The proposed scheme is fully autonomous while it retains the plug-nplay features for generators and tie-converters.The performance of the proposed control scheme has been validated under different operating scenarios.The results demonstrate the effectiveness of the proposed scheme in managing the power deficit in the DC microgrid efficiently and autonomously while maintaining the better voltage regulation in the DC microgrid.

Cognitive Power Management in Wireless Sensor Networks

Dynamic power management （DPM） in wireless sensor nodes is a well-known technique for reducing idle energy consumption. DPM controls a node＇s operating mode by dynamically toggling the on/off status of its units based on predictions of event occurrences. However, since each mode change induces some overhead in its own right, guaranteeing DPM＇s eificiency is no mean feat in environments exhibiting non-determinism and uncertainty with unknown statistics. Our solution suite in this paper, collectively referred to as cognitive power management （CPM）, is a principled attempt toward enabling DPM in statistically unknown settings and gives two different analytical guarantees. Our first design is based on learning automata and guarantees better-than-pure-chance DPM in the face of non-stationary event processes. Our second solution caters tor an even more general setting in which event occurrences may take on an adversarial character. In this case, we formulate the interaction of an individual mote with its environment in terms of a repeated zero-sum game in which the node relies on a no-external-regret procedure to learn its mini-max strategies in an online fashion. We conduct numerical experiments to measure the performance of our schemes in terms of network lifetime and event loss percentage.

Online power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning and driving cycle reconstruction

This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm.Three parallel soft actor-critic(SAC)networks are trained for high speed,medium speed,and low-speed conditions respectively;the reward function is designed as minimizing the cost of energy cost and battery aging.During operation,the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization(LVQ)neural network.On top of that,a driving cycle reconstruction algorithm is proposed.The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed,medium speed,and low speed,based on which the algorithms are online updated.The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data.The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38%after the online update for uncertain driving conditions.

A power management circuit with 50%efficiency and large load capacity for tribo-electric nanogenerator

Recently, triboelectric nanogenerators （TENGs）, as a collection technology with characteristics of high reliability, high energy density and low cost, has attracted more and more attention. However, the energy coming from TENGs needs to be stored in a storage unit effectively due to its unstable ac output. The traditional energy storage circuit has an extremely low energy storage efficiency for TENGs because of their high internal impedance. This paper presents a new power management circuit used to optimize the energy using efficiency of TENGs, and realize large load capacity. The power management circuit mainly includes rectification storage circuit and DC-DC management circuit. A rotating TENG with maximal energy output of 106 mW at 170 rpm based on PCB is used for the experimental verification. Experimental results show that the power energy transforming to the storage capacitor reach up to 53 mW and the energy using efficiency is calculated as 50%. When different loading resistances range from 0.82 to 34.5 k^2 are connected to the storage capacitor in parallel, the power energy stored in the storage capacitor is all about 52.5 mW. Getting through the circuit, the power energy coming from the TENGs can be used to drive numerous conventional electronics, such as wearable watches.

Unified fuzzy logic controller and power management for an isolated residential hybrid PV/diesel/battery energy system

Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems(PMSs)aim to reduce fossil fuels and increase the efficiency of renewable energy sources to minimize greenhouse gas emissions.The influential role of the PMS contributes to improving the efficiency and effectiveness of these systems by ensuring a balance between the different sources and loads in all operating modes.However,the abrupt transitions between the various operational modes selected by the PMS generate power loss and imbalance.To handle this issue,a fuzzy logic controller(FLC)-based PMS controlling a photovoltaic(PV)and diesel hybrid system with a battery storage element connected to a DC bus is proposed in this paper.The proposed PMS is wholly based on FLC to ensure a smooth transition between the different modes of the system.The success of using the suggested PMS lies in how well the FLC parameters are chosen before the system is processed.For this purpose,the particle swarm optimization algorithm is adapted to tune the FLC parameters.The resulting optimal intelligent PMS is tested and compared with a classical one using comprehensive simulations performed in a Simscape ElectricalTM MATLAB®environment.The obtained results show an overshoot attenuation at the DC-bus voltage of 2%when changing the mode and an improvement in the PV generator efficiency by 99.5%.

Practicality of power load management system in Chongqing City verified

Began from early 1992, Chongqing Power Supply Bureau had spent 3 and half years to build up a power load management system consisting of I master station, 6 relay stations, 1280 terminals and the distributed monitoring device. This system distributes in the hilly and mountainous areas where geographically complicated and the load widely scatters, it can supervise about 72% load and curtail more than 15% load

Power Production Management of Three Gorges Project

The Three Gorges Hydropower Plant is the one with the largest installed capacity in the world today and has a decisive and important influence upon the security of the national power grids. It has been operated stably and supplied the East China, Central China, South China, Sichuan and Chongqing with clean renewable energy. This paper presents the management of the project’s dispatch, operation and maintenance and puts forward new ideas and new methods for the plant management, which may provide valuable references for the operation of oversized hydropower plants.

Technical Management in Power Plant Constructionfor Export Contract

Regarding the technical management during construction of Thailand PTA Co-generation, there are some problems due to the typical method in electric power construction in China, which show the differences between international methods and ours. In order to do the technical management in the project for export contract, it is necessary to do the work based on the Contract, to know the related laws and regulations, and national conditions, to coordinate the relationship among various parties well, and to carry out the management ahead of time.

The Separation of Powers from Functions and the Establishment of a New Structure of Management and Operation of State-owned Assets

The development of the socialist market economy demands the furtherintensification of the reform of the management operating system (MOS)of the state-owned assets, the strengthening of the efficiency in the man-agement of state-owned assets,and the improvement of the operating bene-

Self-charging power textiles integrating energy harvesting triboelectric nanogenerators with energy storage batteries/supercapacitors

Lightweight and flexible self-charging power systems with synchronous energy harvesting and energy storage abilities are highly desired in the era of the internet of things and artificial intelligences,which can provide stable,sustainable,and autonomous power sources for ubiquitous,distributed,and low-power wearable electronics.However,there is a lack of comprehensive review and challenging discussion on the state-of-the-art of the triboelectric nanogenetor(TENG)-based self-charging power textiles,which have a great possibility to become the future energy autonomy power sources.Herein,the recent progress of the self-charging power textiles hybridizing fiber/fabric based TENGs and fiber/fabric shaped batteries/supercapacitors is comprehensively summarized from the aspect of textile structural designs.Based on the current research status,the key bottlenecks and brighter prospects of self-charging power textiles are also discussed in the end.It is hoped that the summary and prospect of the latest research of self-charging power textiles can help relevant researchers accurately grasp the research progress,focus on the key scientific and technological issues,and promote further research and practical application process.

A LOW POWER TIME-TO-DIGITAL CONVERTER FOR ALL-DIGITAL PHASE-LOCKED LOOP

Time-to-Digital Converter (TDC) is a key block used as the phase/frequency detector in an All-Digital Phase-Locked Loop (ADPLL). Usually, it occupies a large proportion of ADPLL's total power consumption up to about 30% to 40%. In this paper, the detailed power consumption of different components in the TDC is analyzed. A Power Management Block (PMB) is presented for the TDC to reduce its power consumption. A 24-bits TDC core with the proposed PMB is implemented in HJTC 0.18 μm CMOS technology. Simulation results show that up to 84% power reduction is achieved using our proposed technique.

Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency

More demand-side flexible resources(DFRs)are participating in the frequency regulation of renewable power systems,whose heterogeneous characteristics have a significant impact on the system frequency response.Consequently,selecting suitable DFRs poses a formidable challenge for independent system operators(ISO).In this paper,a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system frequency.Firstly,a performance curve is developed to describe the cost,capacity,and response speed of DFRs.Moreover,a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty coefficients.Then,the frequency security criterion considering DFRs’performance is constructed,whose linearity makes it can be easily coupled into the system scheduling model and solved.Furthermore,a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency security.Finally,the model is transformed into mixed integer second-order cone programming(MISOCP)and solved by the commercial solver.The proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

Intelligent Vehicle Electrical Power Supply System with Central Coordinated Protection

The current research of vehicle electrical power supply system mainly focuses on electric vehicles（EV） and hybrid electric vehicles（HEV）.The vehicle electrical power supply system used in traditional fuel vehicles is rather simple and imperfect;electrical/electronic devices（EEDs） applied in vehicles are usually directly connected with the vehicle＇s battery.With increasing numbers of EEDs being applied in traditional fuel vehicles,vehicle electrical power supply systems should be optimized and improved so that they can work more safely and more effectively.In this paper,a new vehicle electrical power supply system for traditional fuel vehicles,which accounts for all electrical/electronic devices and complex work conditions,is proposed based on a smart electrical/electronic device（SEED） system.Working as an independent intelligent electrical power supply network,the proposed system is isolated from the electrical control module and communication network,and access to the vehicle system is made through a bus interface.This results in a clean controller power supply with no electromagnetic interference.A new practical battery state of charge（So C） estimation method is also proposed to achieve more accurate So C estimation for lead-acid batteries in traditional fuel vehicles so that the intelligent power system can monitor the status of the battery for an over-current state in each power channel.Optimized protection methods are also used to ensure power supply safety.Experiments and tests on a traditional fuel vehicle are performed,and the results reveal that the battery So C is calculated quickly and sufficiently accurately for battery over-discharge protection.Over-current protection is achieved,and the entire vehicle＇s power utilization is optimized.For traditional fuel vehicles,the proposed vehicle electrical power supply system is comprehensive and has a unified system architecture,enhancing system reliability and security.

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems.With the advancement of technology,the performance management of central processing unit(CPU)is changing.Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size.When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life cycle.In this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption.The selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)policy.Increase in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more power.The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs.The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs.Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.