Power Management Framework Research Articles

Generative Adversarial Network-Assisted Framework for Power Management

Generative Adversarial Network-Assisted Framework for Power Management

A Distributed AI Framework for Nano-Grid Power Management and Control

A Distributed AI Framework for Nano-Grid Power Management and Control

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management. Only depending on optimization algorithm enhancements is not enough for uplink transmission. To control intercell interference, Fractional Uplink Power Control (FUPC) should be optimized from network-wide perspective, which has to find a better traffic distribution model. Conventionally, traffic distribution is geographic-based, and ineffective due to tricky locating efforts. This paper proposes a novel uplink power management framework for Self-Organizing Networks (SON), which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model. PathLoss-based Traffic Distribution (PLTD) aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells. Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40% compared to conventional GeoGraphic-based Traffic Distribution (GGTD).

Optimal Cooperative Power Management Framework for Smart Buildings Using Bidirectional Electric Vehicle Modes

The high potential for implementing demand management approaches across multiple objectives has been significantly enhanced. This study proposes a cooperative energy management strategy based on the end-user sharing of energy. The proposed method promotes the intelligent charging and discharging of EVs to achieve vehicle-to-anything (V2X) and anything-to-vehicle (X2V) operating modes for both integrated and nonrenewable residential applications. These sharing modes have already been discussed, but resolution approaches are applicable to a specific use case. Other application cases may require additional metrics to plan the fleet of electric vehicles. To avoid that problem, this study proposes the MIP method using a robust Gurobi optimiser based on a generic framework for cooperative power management (CPM). Moreover, the CPM ensures an overall target state of charge (SoC) at leaving time for all the vehicles without generating a rebound peak in total grid power, even without introducing photovoltaic power. Two different methods are proposed based on the flow direction of the EV power. The first method only includes the one-way power flow, while the second increases the two-way power flow between vehicles, operating in vehicle-to-vehicle or vehicle-to-loads modes. A thorough analysis of the findings of the proposed model was conducted to demonstrate the robustness and efficiency of the charging and discharging schedule of several EVs, favouring a sharing economy concept, reducing peak power, and increasing user comfort.

ERIN: Energy-Aware Resource-Provisioning Framework for CPU-FPGA Multitenant Environment

This article proposes a framework for transparent resource provisioning and power management in central processing unit field-programmable gate array (CPU-FPGA) cloud environments.

Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called "preparation range" to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.

Power Flow Optimization for a Hybrid-Electric Propulsion System

Abstract This study deals with the optimization of performance for a hybrid-electric propulsion system. It focuses on the modeling and power management frameworks, while evaluation is done on a single flight basis. The main objective is to extract the maximum out of the novel powertrain archetype. Two hybridization factors are considered. The pair helps to describe the degree of hybridization at the power supply and power consumption levels. Their revised mathematical definition facilitates a unique method of hybrid-electric propulsion system modeling that maximizes the conveyed amount of information. An in-house computational tool is developed. It employs a genetic algorithm optimizer in the interest of managing power usage during flight. Energy consumption is set as the objective function. The operation of a 19-seater, commuter aircraft is investigated. Turbo-electric, series-hybrid, parallel-hybrid, and series-parallel variants are derived from a generic composition. An analysis on their optimized performance, with different technological readiness levels for 2020 and 2035, is aimed at identifying where each system performs best. Considering 2020 technology, it does not yield a viable hybrid-electric configuration, without suffering significant payload penalties. Architectures relying on mechanical propulsors show promise of 15% reduction to energy consumption, accounting for 2035 readiness levels. The concepts of Boundary Layer Ingestion and Distributed Propulsion display the potential to boost electrified propulsion. The series-hybrid and series-parallel configurations are the primary beneficiaries of these concepts, displaying up to 30% reduction in fuel and 20% reduction in energy consumption.

Decentralized Active Power Management in Multi-Agent Distribution Systems Considering Congestion Issue

Recently, due to the restructuring of power systems and the high penetration level of local renewables, distribution systems have encountered with the complexity of power management. Therefore, the modern systems would be operated in a multi-agent structure which facilitates the power management as well as privacy protections of independent entities. In this structure, the distribution system is assumed to compose of several agents who independently schedule their local resources in order to maximize their own profits. Consequently, this paper provides an efficient peer-to-peer (P2P) active power management framework in a multi-agent distribution system while considering network constraints (i.e., line loadings and losses). In this context, in the proposed P2P scheme, the distribution system operator (DSO) model the network constraints in the form of line-usage costs within the transactive signals. Respectively, the developed transactive control signals enable the DSO to model the power loss as well as alleviate the congestion in the grid. Therefore, the agents automatically consider the network constraints in their power transactions management procedure without any direct interferences of the DSO in their resource scheduling. Finally, the proposed model is implemented on the modified-IEEE-37-bus-test system in order to investigate its effectiveness in the energy management of multi-agent systems.

Optimal LED Power Allocation Framework for a Location-Assisted Indoor Visible Light Communication System

The widespread deployment of white light-emitting6 diodes (LEDs) for illumination provides a unique opportunity to create a flexible, accurate, and ubiquitous indoor communication and positioning system. In this paper, we exploit the location information obtained via LEDs to improve the communication performance of an indoor visible light communication (VLC) system. Specifically, we propose an optimal LED power management framework to maximize the average data rate across the room while satisfying the bit error rate (BER) and illumination constraint across the room. The maximum allowed localization error, as a function of the number of blockages and the LED irradiance angle, have been calculated. In addition, the closed-form expression for the BER is derived for the proposed optimal LED power allocation scheme. We have also formulated an optimization problem that will maximize the power savings among the LEDs with respect to the number of blockages and permissible error in localization. It has been shown that, by employing the proposed optimal LED power allocation will results in a significant amount of power-saving, which is approximately 40% for 4 LEDs configuration and 70% 26 for 8 LEDs configuration as compared to equal power allocation. Further, the maximum allowed localization error is found out to be 28 approximately 7 cm and 18 cm with 4 and 8 LEDs, respectively, to 29 achieve the maximum achievable data rate. Finally, it is shown that 30 the dimming range of up to 70% can be achieved for the proposed system.

Flexible reactive power management using PV inverter overrating capabilities and fixed capacitor

The rapid growth of photovoltaic resources is an opportunity for reactive power management of low voltage distribution grids. This study presents an optimal planning framework for reactive power management considering the photovoltaic inverter capability and fixed capacitor. In this regard, the inverter capacity along with location and number of the fixed capacitors are simultaneously determined to minimize the investment of reactive power compensation. To be more realistic, investment, maintenance and operation costs of compensation devices as well as lifetime and oversizing of the inverter are considered in the objective function. In this paper, accurate AC load flow equations along with technical constraints are integrated into a mixed-integer second-order conic programming model. The suggested methodology can be optimally done using commercial solvers to satisfy all technical constraints such as voltage limits and capacity of distribution branches at any penetration level of photovoltaic resources. Two case studies are implemented and simulated using MATLAB software in order to show the analysis and effectiveness of the proposed reactive power management. The comparison of the suggested technique with the conventional methods confirms the considerable reduction of investment and energy losses in a real low voltage distribution grids (400 volt).

An enhanced predictive hierarchical power management framework for islanded microgrids

This paper proposes an enhanced three-layer predictive hierarchical power management framework for secure and economic operation of islanded microgrids. The tertiary control, guaranteeing the microgrid economic operation, is built upon the semi-definite programming-based AC optimal power flow model, which periodically sends power references to secondary control. To mitigate uncertainties arising from renewable generations and loads, a centralized linear model predictive control (MPC) controller is proposed and implemented for secondary control. The MPC controller can effectively regulate the microgrid system frequency by closely tracking reference signals from the tertiary controller with low computational complexity. Droop-based primary controllers are implemented to coordinate with the secondary MPC controller to balance the system in real time. Both synchronous generators (SGs) and solar photovoltaics (PVs) are simulated in the microgrid power management framework. A unified linear input-state estimator (ULISE) is proposed for SG state variable estimation and control anomaly detection due to compromised cyber-physical system components, etc. Simulation results demonstrated that SG states can be accurately estimated, while inconsistency in control signals can be effectively detected for an enhanced MPC. Furthermore, comparing with conventional proportional-integral (PI) control, the proposed hierarchical power management scheme exhibits superior frequency regulation capability whilst maintaining lower system operating costs.

Stochastic power management strategy for optimal day-ahead scheduling of wind-HESS considering wind power generation and market price uncertainties

Wind power uncertainty is one of the problems in large-scale wind farms (WFs) integration to the electrical network. In the restructured markets, WFs have to participate in the day-ahead (DA) and the real-time (RT) markets. Due to the intermittent nature of wind speed, the system operator may impose penalty to them because of their uncertainties and deviations from the announced schedule in the DA market. The use of energy storage systems (ESSs) is a practical solution for WFs power management. This paper proposes a stochastic framework for power management of a wind-hybrid ESS (HESS) to maximize the DA market profit through bidding a scheduled power, based on a mixed-integer linear programming. In the scenario-based model of DA optimal scheduling, the wind power generation and DA market price uncertainties are considered in the wind-HESS power management and the scenarios are generated using Monte-Carlo method and roulette wheel. Thus, the DA optimal power bidding strategy and real-time operational management are incorporated to submit power to DA market with no deviation, while keeping the state of charge of HESS in a safely range. Simulation results prove increase in the profit obtained from the modified version of limited min–max wind power management method.

RETRACTED: Optimal power management framework for smart homes using electric vehicles and energy storage

Residential Buildings are considered as the most important and eventual beneficial part of the future smart energy system. Therefore, development of an efficient home power management framework (HPMF) to intelligently manage the prosumers power procurement is necessary. This system will be used to optimally govern the use of home appliances, it will also manage the power generated by renewables (PGR), as well as this system will also optimization the charging/discharging of the energy storage units (ESU) and electric vehicles (EVs). In this paper, we have proposed a holistic method to optimize the use of different home appliances according to the prosumers preferences and defined schedule. Moreover, a separate system for controlling the charging/discharging of ESU and EVs has been designed which considers their capital costs while integrating them with the HPMF to enable flexibility in the use of stored power which enhances the desired economic outcomes besides it also increases the ESU life. Using Mixed integral linear programming’ (MILP’) and the proposed power management framework it is possible to intelligently optimize the energy procurement of a smart home, which not only guarantee the minimal energy procurement cost as well as it increases consumer comfort. A comprehensive performance analysis of the proposed model has been carried out to prove its robustness and effectiveness.

Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

Decentralized transactive energy management of multi-microgrid distribution systems based on ADMM

Presence of microgrids (MGs) and renewable energy sources (RESs) in distribution power systems could result in dramatic changes in operation and planning of these systems. In this regard, operational procedures employed by utilities should take into account the intermittent nature of RESs, while coping with the independent operation of MGs. In this perspective, this paper develops a distributed power management framework based on alternating direction method of multipliers (ADMM) for distribution networks with multi-MG (MMG) structures. The proposed approach develops a transactive signal in the context of ADMM to coordinate operation of MGs in a distributed manner. In this context, the configuration of MMG distribution systems is devised in a way that facilitates modern distributed control of the system by eliminating the role of a central coordinator in traditional distribution systems. Moreover, robust optimization technique is deployed in the MG operational scheduling to handle the uncertainty associated with RESs. Finally, through a numerical case study, the efficiency of the proposed framework to schedule local resources for MMG distribution systems is analyzed.

A power and thermal-aware virtual machine management framework based on machine learning

Energy consumption in data centers grows rapidly in recent years. As a widely-applied energy-efficient method, workload consolidation also has its own limitations that may bring some negative effects, such as performance degradation, QoS violation, localized hotspots and so on, which is especially true when optimal objectives are inherently conflict. In this paper, we present a power and thermal-aware VM management framework called PTM-ML, which relies on machine learning technique to find optimal host configuration based on workload characteristics and cooling system’s working state. Based on such an optimal host configuration, it then makes VM migration and consolidation decisions by enforcing an efficient load-balancing policy, with aiming at achieving a better trade-off between energy efficiency and performance. The prototype of PTM-ML framework is deployed and evaluated in a real-world cloud data center. Extensive experiments are conducted by using different workload traces with distinctive characteristics, and the results are compared with four similar approaches in terms of total energy consumption, real-time power consumption, average latency and etc. Experimental results show that the proposed PTM-ML outperforms the existing approaches in terms of multiple metrics, and it also exhibits better robustness and adaptability in presence of dynamic workloads.

Self-aware Power Management for Maintaining Event Detection Probability of Supercapacitor-powered Cyber-physical Systems

In this article, the self-aware power management framework is investigated for maintaining event detection probability of supercapacitor-powered cyber-physical systems, with a radar network system as an example. Maintaining the event detection probability of the radar network is decomposed as a problem of controlling the quality of service of each network node. Then a power management method based on model predictive control and particle swarm optimization is proposed for tracking the reference quality of service of each node while satisfying the operation constraints. The effectiveness of the proposed method is demonstrated through three simulation studies that cover both single node and network scenarios. In addition, to support the proposed power management method, an online state of charge prediction method is developed for the supercapacitor. The online prediction method adopts a supercapacitor model that describes both the ohmic leakage and charge redistribution phenomena and uses online model updating to more accurately capture the supercapacitor behavior and estimate the stored energy.

Power Management by LSTM Network for Nanogrids

Nanogrids can be considered smart grids that are implemented for small-scale buildings, houses, and apartments. A typical power management framework for nanogrids determines the scheduling of operations of electric appliances for each time interval with objectives related to total power consumption and total delay due to scheduling. Such a framework of power management has limitations in accommodating future operating conditions of nanogrids. Taking future outdoor temperature as a future operating condition, a proactive power management for nanogrids is presented in this paper. The goal of proactive power management for nanogrids is to achieve the proper level of indoor temperature in a cost-efficient way, sooner rather than later, by taking into account future outdoor temperature. To achieve this goal, a long short-term memory (LSTM) network is used as the controller. Simulations have been performed to verify the performance of the proposed power management. The results of the simulations demonstrate that living comfort measured in terms of room temperature is enhanced while the overall electricity cost is reduced, mainly due to the ability of the LSTM network to predict the trend of outdoor temperature.

Improving energy efficiency of mobile devices by characterizing and exploring user behaviors

Nowadays, mobile devices have become the prevailing computation platform. Due to their battery-constraint nature, delivering excellent user experience with low energy consumption is highly desirable for mobile devices. Past researches on optimizing the energy efficiency for mobile devices are at either too high or too low levels. The former can explore different QoS requirements for applications, while the latter can model the interaction details. To get both advantages, we target a level in the middle, i.e., user behavior level. In this work, we characterize user behaviors, analyze their QoS requirements and propose corresponding QoS models for each behavior type. Then, we introduce U-ACT, a user behavior aware power management framework, which can optimize CPU frequencies for all different behavior types. Our evaluation shows the proposed framework can achieve up to 62% combined improvement, i.e., the summation of QoS and energy improvements in percentage, comparing with the state-of-the-art governors.

Runtime Power Management of Battery Electric Vehicles for Extended Range With Consideration of Driving Time

Installation of a large-capacity battery pack is a straightforward method to extend the range of battery electric vehicles (BEV or all-electric vehicles). However, at the same time, a large-capacity battery pack not only occupies a big space but also significantly increases the vehicle weight, which directly impacts the fuel economy and vehicle performance. This implies that increasing the battery capacity has an obvious limitation in extending the EV range. In this paper, we introduce a system-level framework to extend the range of BEV with the consideration of the vehicle dynamics, electric powertrain characteristics, road slopes, payload, and regenerative braking. This paper particularly takes into account driving time so that the resultant BEV power management does not impractically slow down the vehicle velocity. The BEV power management framework derives energy-aware velocity planning, i.e., a desirable instantaneous velocity at each distance step (or at each time instant). The major technical contributions of this paper compared with the previous work include: 1) practically applicable velocity planning for production BEVs from superb BEV power model fidelity; 2) new performance metrics to consider both driving energy and driving time: energy-delay product (EDP), energy-square-delay product, and energy-cubic-delay product; 3) heuristics to derive EDP-aware velocity planning; 4) comparative analysis of the velocity planning between BEV and internal combustion engine vehicles; and 5) analysis of the model fidelity impact on the energy-aware velocity planning. The proposed method results in up to a 46.2% improvement of the EDP compared with the least-energy constant velocity driving.