配电网规划中负荷预测实际问题及措施

随着智能电网和大数据技术的不断发展,传统的配电网负荷预测分析模式已无法满足需求.针对负荷预测基础数据准确性、电力用户侧数据有效性等方面的实际问题,结合云计算技术设计出基于大数据系统的负荷预测技术模型,用随机森林分析方法设计负荷预测实验,确保规划实施后配电网的安全、节能与可靠运行,从而最大限度地解决农村地区电网规划负荷预测难题.     

智能电网在电力系统中的应用分析

随着智能电网建设速度的加快,智能需求侧管理系统作为智能电网中合理供配电的重要决策系统,已经成为许多电力营销研究的一个重要课题。本文在分析了智能电网需求侧管理系统现状后,对实际运行中电力需求侧管理存在的一些问题和不足进行了探讨,并结合自己多年的工作经验,提出了相应的改进措施,完善智能电网电力需求侧管理系统的综合功能,有效保障社会、电力公司和用户的经济效益。     

电力负荷管理系统的建设

随着国家经济的大踏步发展和电网设施的逐步完善,电力负荷管理系统在电网营销管理工作中的地位又提升了一步.负荷管理系统通过无线信道,对安装在用户侧的终端设备进行监测,从而具备了远程抄表、预购电、防窃电、用电质量监测、负荷控制、用电信息服务、控制地方上网电厂等功能以适合市场的发展需要.负荷管理系统是促进电力需求侧管理,实现有序用电的重要技术支持手段.     

面向节能服务的智能化需求侧能效管理系统

当今社会正面临着节能减排的压力和考验,电网企业迫切需要一种科学的方法来综合考虑客户需求和电网体系,并采取有效的节能措施提供优质的节能服务;在此从建设特点、体系架构、业务流程等多方面介绍一种面向节能服务的智能化需求侧能效管理系统,该系统不仅可为电网企业多维度评估客户提供了一种科学而系统的方法和工具,还能综合考虑客户需求和电网复杂性的能力,并有针对性地提供能效方案。     

基于配电系统节电器的谐波抑制研究

模拟配电网用户侧典型负荷组成,搭建了节电器节电行为研究平台.通过电能质量分析仪、三相可编程电源等在线监测与分析仪器,研究了配电系统节电器在阻感负荷运行工况下对负荷侧电网谐波的抑制行为.结果表明,配电系统节电器抑制电力用户侧谐波效果明显,单相电流谐波可降低2％ ～5％,波型稳定,电能质量得到有效提高.     

面向智能电网峰值负荷降低的用电数据分析

随着用电负荷需求不断增加所引发的峰值负荷过载问题,文中建立了智能家居(SH)和智能电网(SG)服务器之间数据通信模型,给出了配电网负荷需求管理的总体条件,并提出了一种面向智能电网(SG)的负荷数据分析DR管理方法,通过对用户的SH收集用电数据进行分析,设计了峰值负荷情况下的DR决策,分别从用户、电力公司和瞬时负荷变化三个角度设计了不同的峰值负荷降低算法。仿真结果表明,所提出的方法在很大程度上有效地降低了配电网的峰值负荷。     

需求侧管理与响应环境下的新能源发电

为了实现需求侧管理与响应,需要建立一个具备高级量测能力、信息实时交互的信息管理平台。在该平台的支持下,电网对新能源发电的消纳能力将大幅度提高。本文分析了影响需求侧与新能源发电侧联动的因素：用户响应资源与用户响应速度;论述了为提高新能源消纳能力及促进需求侧管理与响应的发展,需求侧管理响应平台建设、需求侧管理响应与新能源服务集成所需考虑的要点。     

基于需求响应的风电-抽水蓄能系统调度模型

针对大规模风电并网影响电网稳定运行的问题,基于日前用户侧电力负荷预测和实时电价预测,文中提出了一种电网吸纳风能的新模型。以最小化风电—抽水蓄能联合系统发电成本为目标,在目标求解过程中确定最佳自弹性系数和互弹性系数,运用基于实时电价的需求价格来弹性引导电力用户以实现负荷转移。考虑到用户侧电价弹性限制的约束,该方法采用遗传优化算法编程求解。仿真结果表明,需求侧资源主动参与优化调度可降低负荷峰谷差,利于系统稳定运行,并且可以减少发电成本,提高联合系统经济性。     

浅谈用电信息采集异常数据消缺管理

用电信息采集系统是智能电网的重要组成部分,是实现与电力用户双向互动、提升用户服务能力、建立智能用电服务体系的技术基础.用电信息采集系统能够为"SG186工程"营销业务提供电力用户实时的用电信息数据,有利于推进公司营销管控标准化建设和公司信息化建设,也为提升电力需求侧管理水平、全面预付费的营销业务策略的实施提供技术支撑,符合国际电网技术发展的方向.     

一种基于LSTM神经网络的短期用电负荷预测方法

负荷预测目前已成为保障电力系统用电安全的重要基础工作,在我国市场经济制度持续完善过程中,能源产业也逐渐发展为以市场为导向,这就要求负荷预测要更加可靠、实时与准确.保证负荷预测的准确性,能为电力销售计划的制定奠定坚实基础并提供可靠依据,确保电网运行更加安全与经济.在智能电网时代下,电力用户侧涉及到海量数据,且用户数据具有...     

基于改进烟花算法的电动汽车与新能源协同调度

为提高电力系统对新能源出力的接纳能力,降低区域电网的等效负荷波动,维护系统安全性的同时,增强电动车车主响应的积极性,以区域电网系统内的等效负荷波动量最小和电动车车主经济效益最优为优化目标,建立规模化电动汽车与新能源协同调度模型,来合理安排电动汽车的充放电行为。运用最大模糊满意度法将多目标问题化为单目标问题,提出一种改进烟花算法,通过优化初始种群分布,以“双精英—锦标赛”的选择策略提升算法性能。通过算例结果对比,验证规模化电动汽车与新能源协同调度可有效平抑等效负荷波动,为电动汽车用户创造收益。改进的算法降低了计算开销,求解精确度更高。     

An efficient energy and power manager for autonomous systems based on energy harvesting

This paper describes the design of an energy and power manager for energy-harvesting devices consisting of four power converters operating in PFM mode for a low-energy and load current. The harvested energy can be maximized using a maximum power point tracking technique by a battery-charging converter, and stable voltages are generated and supplied to the charger and output loads by the other converters. These converters are optimized for high conversion efficiency with optimal switch sizes and an inductor peak current based on a numerical approach. For a further increase at a light load, the power management reduces the average bias current dynamically. It also controls the consumed or output power according to the operating condition. Using an IC fabricated in a 0.18 μm process, we verified experimentally that the energy and power manager achieves high efficiency through an optimal design with a numerical approach, and through the power management, especially for a light load, and is therefore efficient for low-energy harvesting.     

基于连续凸逼近的协作式非正交多址接入联合无线携能通信的能效优化方案

在传统的非正交多址(NOMA)系统中,通常将更多的功率分配给边缘用户以此来保证其通信质量,系统公平性以牺牲系统容量为代价。基于协作通信的NOMA系统虽可解决上述问题,但在协作阶段中心用户需承担中继的作用,这种方式必将给中心用户带来一定的负担。为了兼顾系统容量和公平性,该文提出一种基于协作通信和无线携能通信(SWIPT)的新型资源分配方案,该方案在满足边缘用户通信质量情况下,使用能量收集设备完成能量收集,通过连续凸逼近(SCA)求解目标问题最大化系统能效。仿真结果表明,与传统NOMA和协作式非正交多址接入系统(CNOMA)相比,CNOMA-SWIPT系统的能量效率得到了较大的提高,在基站最大发射功率为30 dBm时相比NOMA系统能达到60.8%的增益,相比CNOMA系统能达到比CNOMA系统高出约11.5%的增益,更符合绿色通信的发展理念。     

Asymptotic RZF cooperative beamforming algorithm based on energy efficiency

A low complexity asymptotic regularized zero forcing cooperative beamforming algorithm based on energy efficiency in heterogeneous massive MIMO system was proposed,aiming at the problem that the current multi-flow regularization zero forcing beamforming algorithm sets the power constraint of each antenna in the regularization term as a fixed value and ignores the influences of factors such as the number of antennas,the number of users and QoS.The algorithm selects the optimal antenna power constraint set through the optimization method,and the optimal beamforming was asymptotically ob-tained to balance the interference among users to achieve the optimal energy efficiency,considering the impact of the number of antennas and users with the constraints of the antenna power and QoS.In view of the importance of backhaul in massive MIMO system,a backhaul power consumption model and the impact of backhaul power consumption on system performance was analyzed.Analysis and simulation results show that the proposed algorithm has great improvement of the performance,especially when the number of antennas is large.The algorithm is close to optimal performance,especially suitable for massive MIMO system of next generation communication.     

基于动态优先级的智能家居户间能源调度算法

针对智能家居中单用户能源调度所忽略的电网侧稳定性的要求,以及多用户能源调度中用户舒适度考虑不足的问题,通过分析家电设备运行特性以及用户需求,对用户用电时间以及家居设备类型进行了分类,并提出家居设备二维动态优先级,在此基础上设计了智能家居户间能源调度算法,旨在保障用户舒适度,降低户内电费支出的同时,保障电网侧的稳定性。最后通过仿真,验证了算法的可行性与有效性。     

基于量子滤波器的电能数据检测与分析

随着以智能电表为核心的智能电网快速发展,电力大数据吸引了用户、用电企业、政府的注意力.用电数据容易受到各种未知的随机干扰.探索了将自适应滤波技术引入电网信息监测.而由于用电负荷曲线复杂、不能给出一个具体的模型,利用量子递归神经网络构造了一个与模型无关的智能滤波器.最后提出了利用量子滤波器进行电力负荷预测以及利用测量误差的概率密度函数进行用户异常用电检测的设想.     

ElectroBlocks: A blockchain‐based energy trading scheme for smart grid systems

Smart grid systems are widely used across the world for providing demand response management between users and service providers. In most of the energy distributions scenarios, the traditional grid systems use the centralized architecture, which results in large transmission losses and high overheads during power generation. Moreover, owing to the presence of intruders or attackers, there may be a mismatch between demand and supply between utility centers (suppliers) and end users. Thus, there is a need for an automated energy exchange to provide secure and reliable energy trading between users and suppliers. We found, from the existing literature, that blockchain can be an effective solution to handle the aforementioned issues. Motivated by these facts, we propose a blockchain‐based smart energy trading scheme, ElectroBlocks, which provides efficient mechanisms for secure energy exchanges between users and service providers. In ElectroBlocks, nodes in the network validate the transaction using two algorithms that are cost aware and store aware. The cost‐aware algorithm locates the nearest node that can supply the energy, whereas the store‐aware algorithm ensures that the energy requests go to the node with the lowest storage space. We evaluated the performance of the ElectroBlocks using performance metrics such as mining delay, network exchanges, and storage energy. The simulation results obtained demonstrate that ElectroBlocks maintains a secure trade‐off between users and service providers when using the proposed cost‐aware and store‐aware algorithms.     

LTE系统中基于负载的能量有效性调度策略

针对绿色通信中如何能在保证一定服务质量的前提下降低能量消耗这一关键问题,根据带宽交换能量的思想,综合考虑系统吞吐量和用户间公平性,提出了LTE 下行传输中基于负载的资源调度策略,通过降低频谱效率换得能量效率的提高,以达到节能的目的。仿真结果表明,所提的两个算法在满足用户速率要求的同时都能在很大程度上降低发射功率,且保证了用户间的公平性。     

An Overview to the Concept of Smart Coupling and Battery Management for Grid Connected Photovoltaic Battery System

The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system (RES). Grid integrated photovoltaic (PV) battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems (INES), Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system (PEMS).