Effective Energy Management Scheme by IMPC

The primary purpose of the Energy Management Scheme(EMS)is to monitor the energy fluctuations present in the load profile.In this paper,the improved model predictive controller is adopted for the EMS in the power system.Emperor Penguin Optimization(EPO)algorithm optimized Artificial Neural Network(ANN)with Model Predictive Control(MPC)scheme for accurate prediction of load and power forecasting at the time of preoptimizing EMS is presented.For the power generation,Renewable Energy Sources(RES)such as photo voltaic(PV)and wind turbine(WT)are utilized along with that the fuel cell is also presented in case of failure by the RES.Such a setup is connected with the grid and applies to the household appliances.In improved model predictive control(IMPC),the set of constraints for the powerflow in the system is optimized by the ANN,which is trained by EPO.Such a tuning based prediction model is presented in the IMPC technique.The proposed work is implemented in the MATLAB/Simulink platform.The energy management capability of the proposed system is analyzed for different atmospheric conditions.The total system cost,life cycle cost and annualized cost for IMPC are 48%,45%and 15%,respectively.From the performance analysis,the cost obtained by the proposed method is very low compared to that obtained by the existing techniques.

A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks

How to design an energy-efficient algorithm to maximize the network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor networks (HWSN). In this paper, a clustering-tree topology control algorithm based on the energy forecast (CTEF) is proposed for saving energy and ensuring network load balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the network is accurately predicted per round (the lifetime of the network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function (including the energy, link quality and packet loss rate) and their distance. The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several non-cluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the load of each cluster-head and prolong the lifetime of the network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy (LEACH), energy dissipation forecast and clustering management (EDFCM) and efficient and dynamic clustering scheme (EDCS) protocols, CTEF has longer network lifetime and receives more data packets at base station. © 2014 Chinese Association of Automation.

ENERGY MANAGEMENT AND CONTROL TECHNOLOGY IN THE NONFERROUS METALS INDUSTRY

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ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

Real-time Energy Management of Low-carbon Ship Microgrid Based on Data-driven Stochastic Model Predictive Control

With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

Prospect of energy management system for large steel enterprise

The energy management system(EMS),which acts as the heart of the energy management center of a steel enterprise,is a large computer system focused on the concentrative monitor and control of the production and utilization of energy.Although Chinese steel industry was well developed in the latest decade, so far the levels of the comprehensive energy consumption per ton steel among Chinese steel enterprises are remarkably distinct,and the average value of the comprehensive energy consumption per ton steel of them has still been much higher than the value of those in developed countries.This bad situation,in the opinion of the author,partially results from the poor ability for most Chinese steel enterprises to manage the production and utilization of energy.National policies associated to energy-saving and ejection-decreasing call for steel enterprises to build the EMS;and more and more steel enterprises themselves also desire to achieve EMS projects so that they can optimize their energy production and utilization.Baosteel,the largest and most advanced steel enterprise in China,has got plenty of experience in the EMS due to its incessant practice for more than 30 years in the design,construction,application,and revampment of its EMS.In the present article,the features of an advanced EMS is described and discussed based on the design practice of the EMS of Baosteel Zhanjiang Project.An advanced EMS should be an optimized and integrated system,which possesses of the characteristic of high managing efficiency,enough openness in expansion,friendly interfaces, and simple structure.Furthermore,it could support many-sided applications,e.g.,energy related data mineing,energy network combination and co-supply,application of geographic information technology,and other technical researched on energy-saving aspects.It is known that some energy-related indexes of Baosteel have stood on a high level better than those of some worldwide famous steel enterprises.Moreover,it goes without saying that the indexes of Baosteel Zhanjiang will be better than those of present Baosteel.Therefore, one can easily expect that the new EMS of Baosteel Zhanjiang will be much more advanced,which will be more helpful to fulfil systematiclly saving of energy,to elevate the efficiency of energy utilization,to lower the comprehensive energy consumption per ton steel.

Average Current Control with Internal Model Control and Real-time Frequency Decoupling for Hybrid Energy Storage Systems in Microgrids

Among hybrid energy storage systems(HESSs),battery-ultracapacitor systems in active topology use DC/DC power converters for their operations.HESSs are part of the solutions designed to improve the operation of power systems in different applications.In the residential microgrid applications,a multilevel control system is required to manage the available energy and interactions among the microgrid components.For this purpose,a rule-based power management system is designed,whose operation is validated in the simulation,and the performances of different controllers are compared to select the best strategy for the DC/DC converters.The average current control with internal model control and real-time frequency decoupling is proposed as the most suitable controller according to the contemplated performance parameters,allowing voltage regulation values close to 1%.The results are validated using real-time hardware-in-the-loop(HIL).These systems can be easily adjusted for other applications such as electric vehicles.

基于机器学习的主动配电网能量管理与运行控制

随着分布式资源和灵活负荷广泛接入,配电网正演变成为主动配电网,其能量管理与运行控制面临着巨大挑战:1)海量分布式资源并网使得调控需求大增,同时引入了大量随机性使得运行风险增加,需要挖掘其主动支撑能力;2)分布式资源量大且异动频繁,难以及时维护,配电网模型精度差,基于精确建模的运行控制和优化调度技术的工程应用困难。为应对上述挑战,文中介绍了基于机器学习的理论和方法,提出了“测-辨-控”一体化的主动配电网能量管理与运行控制技术体系,实现少/免模型维护的运行控制与优化调度。同时,分析了以下核心技术:1)配电网弱/无模型实时调控技术,实现自律优化;2)分布式资源集群自适应动态控制技术,实现对电网的主动支撑;3)风险量化的概率优化调度方法,实现风险与经济的平衡。最后,简要介绍了适应含极高比例分布式资源的主动配电网的能量管理与运行控制系统架构。

基于需求功率预测的电动拖拉机能量管理策略

针对电动拖拉机在犁耕工况下电机需求电流波动比较大的特点,为了改善动力电池的输出电流过高或过低及电动拖拉机犁耕持续作业时间短的现象,利用超级电容高功率密度的特点,设计了一种锂电池+超级电容结构的双电源电动拖拉机,并建立了Amesim/Simulink联合仿真模型。以模型预测控制作为双电源系统的能量管理方法,基于长短期记忆神经网络建立电动拖拉机犁耕工况下的需求功率预测模型,使用动态规划算法求解最佳的锂电池输出电流。仿真结果表明:相比于模糊控制策略,基于模型预测控制策略有效降低了锂电池大电流放电的频率且峰值电流降低了40%,有效提高了锂电池的使用寿命;超级电容的SOC保持在比较高的范围内,且电动拖拉机在犁耕工况下的单位里程能量消耗降低了2.17%,实现了双电源电流分配最优,提高了电动拖拉机的动力性和经济性。

智能网联汽车能耗管理系统设计与开发

在智能网联化和软件定义汽车新形势下,江铃汽车对公司平台化产品进行了全面的迭代升级,整车电子控制器部署和软件功能策略复杂度急剧增加。功能策略的复杂度越高就越容易出现车辆馈电导致整车无法启动的情况,整车低压能耗管理面临严峻挑战。设计一套合理的整车低压能量管理体系已是江铃汽车当下必需。文章以节省整车功耗和保障整车不出现车辆馈电目标为导向,从预见性服务、整车能量管理系统、整车模式管理、智能发电系统、智能配电和补电系统五个维度系统性地设计和描述了一套整车低压能耗管理系统,保障和助力江铃汽车智能网联化健康快速发展。

基于5G通信技术的智能配电网实时监测与控制

本研究旨在探讨基于5G通信技术的智能配电网实时监测与控制,通过分析5G通信技术的优势、关键技术和典型应用,为智能能源系统的发展提供理论支持和实践指导。文章首先简要阐述5G通信技术在智能配电网中的应用优势,如高速数据传输、大规模设备连接等;其次深入剖析5G通信技术的关键技术,如毫米波通信、大规模多天线传输等;最后结合实际案例,分析5G通信技术在智能配电网实时监测与控制中的典型应用。研究发现基于5G通信技术的智能配电网实时监测与控制能够实现高效、稳定的能源管理和智能化决策,为电力系统的安全运行和节能减排提供重要支撑。

CROSS LAYER COORDINATED ENERGY SAVING STRATEGY IN MANET

Mobile Ad hoc NETwork (MANET) consists of a set of mobile hosts which can operate independently without infrastructure base stations. Energy saving is a critical issue for MANET since most mobile hosts will operate on battery powers. A cross layer coordinated framework for energy saving is proposed in this letter. On-demand power management, physical layer and medium access control layer dialogue based multi-packet reception, mobile agent based topology discovery and topology control based transmit power-aware and battery power-aware dynamic source routing are some of new ideas in this framework.

无线化工业控制系统:架构、关键技术及应用

大型生产设施的安全与能效监控,迫切需要低功耗、高精度的泛在感知,高实时、高可靠的无线传输以及动态灵活的生产管控.为此,本文首先提出一种扁平架构的无线化工业控制系统.然后,围绕感知、传输和控制等核心功能需求,系统阐述了高实时高可靠的工业无线网络设计及其时−空−频三元联合调控方法,感知终端的变周期精益采样和高能效精准时间同步方法,以及管控平台的语义化互操作和赋时工作流模型等核心关键技术.最后,研发了面向石油高效采收和电网全域安全监测的无线化工业控制系统,介绍了应用效果和成效.

负荷网络系统下基于状态空间协同的异质热控负荷集群管控

为了最大化调动同一区域变电站下有效电气连接的负荷资源,提出了考虑异质热控负荷集群联合控制的负荷网络系统架构。以相同电气连接的端侧负荷构成网络系统的物理感知层;受区域分散性和参数异质性限制,实际发生有效功率转移的负荷资源有限,引入边缘数据中心,解析异质热控负荷物理设备层级的共性,虚拟聚合同一区域变电站下物理分散的负荷,建立多负荷状态空间协同的联合控制模型;云端电网数据中心综合考量热控负荷可调节容量和工作状态,经过两次分配确定最佳任务参与组和每组任务量;以同质聚合组作为基本控制单元,开发边缘侧面向异质负荷集群的协同管控模型,发布统一控制指令,协调端侧负荷参与能源服务。最后以算例验证所提系统和异质热控负荷联合控制模型的有效性。

无线化工业控制系统:架构、关键技术及应用

大型生产设施的安全与能效监控,迫切需要低功耗、高精度的泛在感知,高实时、高可靠的无线传输以及动态灵活的生产管控。为此,本文首先提出一种扁平架构的无线化工业控制系统。然后,围绕感知、传输和控制等核心功能需求,系统阐述了高实时高可靠的工业无线网络设计及其时一空一频三元联合调控方法,感知终端的变周期精益采样和高能效精准时间同步方法,以及管控平台的语义化互操作和赋时工作流模型等核心关键技术。最后,研发了面向石油高效采收和电网全域安全监测的无线化工业控制系统,介绍了应用效果和成效。

虚拟电厂智慧运营管控平台系统框架与综合功能

虚拟电厂成为推动新型电力系统建设、实现“双碳”目标的重要手段,而虚拟电厂智慧运营管控平台系统则可有效地实现对虚拟电厂灵活可调资源的管理和监控。从系统框架和综合功能角度对虚拟电厂智慧运营管控平台进行了总体阐述,首先,在分析虚拟电厂运营管控框架和参与市场框架的基础上概述虚拟电厂能源生态系统;基于平台体系框架和平台结构框架2个方面特征,分析了虚拟电厂智慧运营管控平台系统框架;从功能结构蓝图、功能物理结构、功能场景应用3个方面分析了虚拟电厂智慧运营管控平台的综合功能。

基于序列二次规划算法的插电式混合动力汽车模型预测控制策略

为提升插电式混合动力汽车(PHEV)的车速预测精度和燃油经济性,提出基于序列二次规划(SQP)算法的模型预测控制能量管理策略。以卷积神经网络(CNN)构建的车速预测模型为基础,选取三类典型历史工况数据作为CNN车速预测模型的训练集,使用鲸鱼优化算法(WOA)优化CNN参数,通过优化的WOA-CNN模型预测未来时域内的车速;采用SQP算法对模型预测控制策略进行求解,且与基于规则的电量消耗和电量保持(CD-CS)策略和基于全局优化的动态规划(DP)策略的控制结果进行对比分析,验证所提策略的有效性。结果表明:通过WOA-CNN模型可提高车速预测精度,为4.88%~8.39%;与DP控制策略相比,本文提出策略的燃油消耗量高出1.98%,但计算时间减少了74.32%,能量管理的实时性得到大幅提升;与CDCS控制策略相比,提出策略的节油率为20.37%。综合考虑,本文提出策略的整车能量消耗和计算成本较优,可合理实现对PHEV转矩分配的智能控制。

微电网群能量管理系统架构设计与应用

针对区域大规模光伏建设和接入、现有分布式能源缺乏管控的问题,提出了一种适应有源配电网的微电网群的能量管理架构及方法,构建区域级核心区、协调控制单元、多类型微电网支撑的三层两阶段协同互动体系,并对相关关键技术进行了研究和应用。结合北京某城区的实际情况进行了实际系统的建设,完成了微电网群能量管理系统的构建,并对协调控制单元的划分、分布式能源的集群调度、微电网特性分析、微电网间互济等问题进行了探索,有效提高了有源配电网的弹性空间,增加了电网调度对分布式能源的管控能力。