基于需求响应的家庭节电省电优化策略研究

电力需求的日益增长给电网发电及输电环节带来了巨大挑战,配用电网负荷率的增加也给电力系统正常运行带来了安全隐患.运用智能电力需求响应技术,有效整合用户侧电网响应潜力以提升电网运行的安全性、稳定性和经济性值得深入研究.基于智能用电双向交互技术,在满足用户用电要求的基础上,最大限度满足用户舒适度及电网调峰需求为目标,提出了居民侧负荷参与电力需求响应的家庭用电优化方案,重点提出了节电省电控制策略,有负荷转移和负荷调控两种方法,可为家庭提供系统的、全面的省电措施.结合提出的控制策略,基于Matlab平台进行具体仿真,通过波形和数据的分析,详细阐述了需求响应控制策略的要点和价值,也为以后的技术发展提供了数据信息.     

基于随机森林非侵入式负荷分解的地区居民需求响应能力评估

需求响应在缓解供电不足、促进新能源消纳以及节约发电侧的资源投资方面意义重大,而需求响应能力评估可以为需求响应策略的制定提供依据。为解决需求响应能力评估困难的问题,本文提出一种基于非侵入式负荷分解的地区居民响应能力评估的方法。首先建立基于随机森林算法的非侵入式负荷分解模型,再使用该模型针对性的分解出单个用户的可中断负荷,然后基于可中断负荷的用电情况计算出单个用户在各个时刻的响应能力,最后再将区域内的所有用户聚合即可得到地区居民总的响应能力。在真实数据集上进行验证,结果表明所提出的基于随机森林非侵入式负荷分解模型可以更精确的得到用户可中断负荷的有功功率值,地区有功功率聚合误差也更小,可以较好的进行需求响应能力评估。     

一种改进的智能电网能耗调度算法

为了提高电网的运行效率,提出一种新的实时能耗调度算法,通过考虑负载不确定性来实现每个用户的电费最小化.我们将负载调度描述为一个优化问题.为了降低计算复杂度,提出一种近似动态规划算法,以解决电器运行的调度问题.在研究问题时,考虑了必须运行和可控运行在内的不同电器.与大部分当前需求侧管理算法假设完全知晓用户用电需求不同,算法只需知道将来部分需求的估计即可.仿真结果表明,能量调度算法既降低了用户用电支出,又提高了负载需求的峰均比,为用户和电力公司带来收益.     

家庭智能用电管理系统用电优化算法研究

为降低居民用户用电费用和提高用电效率,考虑分时电价价格激励的作用,对家庭智能用电进行了研究。构建了由智能家用电器、传统家用电器、电动汽车及其充放电设备、智能开关/插座、智能交互终端、智能手机、分布式能源和智能电表等组成的家庭智能用电管理系统。以一般家庭常用家用电器为优化对象,以用户用电费用最低为优化目标,采用遗传算法,根据用户设定的各家用电器的工作时间范围,提出了基于分时电价的家庭智能用电管理系统智能用电优化算法。以兼顾用户用电费用和负荷消耗功率均匀分布为优化目标,设定功率阈值,引入惩罚机制,提出了基于分时电价的改进智能用电优化算法。仿真结果表明,所提出的智能用电优化算法可为用户最大限度节省用电费用;所提出的改进智能用电优化算法可为用户适当节省用电费用,并可避免引入新的用电高峰。因此,该智能用电优化算法能实现既定优化目标,是有效、可行的。用户可根据实际需要确定优化目标,选用相应算法。     

基于IPSO-FHMM的非侵入式负荷分解

非侵入式负荷分解是智能用电系统的一个重要环节,可深入分析用户的用电信息,对负荷预测、需求侧管理及电网安全有重要意义.本文提出了一种基于改进粒子群优化因子隐马尔可夫模型(IPSO-FHMM)的非侵入式负荷分解方法.利用高斯混合模型(GMM)对单负荷进行状态聚类,总负载模型由因子隐马尔可夫模型表示.针对Baum-Welch算法容易收敛于局部极值的问题,将线性递减权重的粒子群优化算法引入到FHMM的参数训练中.使用AMPds2数据集进行仿真实验,结果表明,该模型可以有效地提高分解精度.     

基于尖峰电价的家庭能量管理最优控制算法

智能电网技术的发展,为家庭能量管理系统(HEMS)提供了新的研究方向。针对HEMS,提出了一种基于尖峰电价的家庭用电设备最优控制算法,该方法使用层次分析法(AHP)对可调整用电设备的综合满意度进行评估,建立以满意度最高和满意度与费用最少兼顾为目标的数学模型,同时考虑家庭分布式电源及储能设备对家庭用电的影响,使用户满足电力公司在需求响应时段的约束要求并获得最满意、舒适的控制策略。最后通过案例仿真验证所提出的控制算法的实用性和有效性。     

基于改进人工蜂鸟算法的VRV空调需求响应功率削减策略

针对夏季用电高峰时期用户对空调设定温度随意调节造成能源浪费以及需求侧对电网控制指令响应不够精确的问题，提出了一种基于功率削减的空调温度分档需求响应调控策略；以某办公建筑VRV空调为研究对象，分别建立该办公建筑空调物理仿真模型以及功耗数学模型，并对模型的准确性进行验证；提出基于不同舒适度和激励电价的VRV空调温度控制档位，构建室内机温度分档调控多目标优化模型，优化目标为调控时期空调实际功率与调控目标值的平均偏差以及负荷聚合商对用户的激励补偿费用同时最小；选取人工蜂鸟算法作为优化算法，针对该算法存在搜索速度慢、寻优精度低、易早熟收敛等缺点，在种群初始化阶段采用Hammersley序列生成更加均匀的初始种群以提高算法的收敛速度与精度，在搜索阶段采用高斯变异算子对蜂鸟位置进行扰动以进一步提升算法的探索能力。运用改进人工蜂鸟算法对模型进行求解，并与人工蜂鸟算法、粒子群算法、灰狼优化算法和鲸鱼优化算法的求解结果进行对比，以证明所提策略的有效性；实验结果表明，应用改进人工蜂鸟算法求解后的结果在保证用户舒适度的条件下最多可将功率调控精度提高83.1%并且将激励费用减小8.36%。     

实时电价下用户侧电力需求响应模型优化策略及数字仿真谈竹奎1,汪元芹2,赵菁2,刘斌1,刘敏2

为解决智能电网发展中用户参与电力市场运营的响应积极性以及用户收益最大化问题,本文在经济学原理基础上,引用需求价格弹性系数表征用户的用电量随电价的变化情况,建立实时电价下的用户负荷调节能力模型,根据该模型,进一步研究了基于实时电价的用户侧电力需求响应模型优化策略,考虑用户在不同响应场景和不同负荷调节潜力下的需求响应。解决供电与用电间的电力供需不平衡问题,实现用户积极响应及其利益最大化,并提高系统稳定性与安全性。以某地需求响应系统为例,对进入现货市场交易的用户进行数字仿真,通过算例分析表明该模型能有效改善用电负荷曲线,减小用户购电成本,验证了基于实时电价下的电力需求响应优化策略的优化效果。     

集中式低压减载下电力用户负荷侧需求自动响应方法

为确保电力系统安全稳定运行,吸引电力用户主动参与电网调峰当中,减轻电网运行压力,提出一种集中式低压减载下电力用户负荷侧需求自动响应方法.仿真实验结果表明,本文方法可大幅增强居民用电行为的感知水平,提高电网设备利用率和运行效率,实现居民用户与供电企业双赢.     

基于多策略协同进化差分算法的社区居民负荷优化调度

针对需求响应下负荷调度的问题,为提供满足居民利益的响应方案,并提高电网运行稳定性,综合考虑电价、激励型需求响应机制与居民用电需求,以用电成本和社区负荷方差最小化为目标,建立了多用户负荷调度高维目标优化模型。结合模型特征提出一种基于多策略的合作协同进化差分进化算法,设计了基于居民用电特征的混合编码与种群初始化策略,以提高解的质量;引入合作协同进化思想将问题变量分解,依据高维目标分组与聚合对种群进行划分,避免陷入局优;各子种群进化时采取双差分模式协同策略,并构建知识迁移个体实现种群间信息交互,最后经贪婪与随机选择结合的种群合并策略保留完整优秀解至外部档案,以提高Pareto最优集的收敛性与分布性。算例仿真表明所提方法可降低社区居民用电成本18%左右、负荷波动方差30%以上;随着居民数量增加,算法的收敛性与多样性与同领域其他算法相比优势更为明显。     

A self-learning scheme for residential energy system control and management

In this paper, we apply intelligent optimization method to the challenge of intelligent price-responsive management of residential energy use, with an emphasis on home battery use connected to the power grid. For this purpose, a self-learning scheme that can learn from the user demand and the environment is developed for the residential energy system control and management. The idea is built upon a self-learning architecture with only a single critic neural network instead of the action-critic dual network architecture of typical adaptive dynamic programming. The single critic design eliminates the iterative training loops between the action and the critic networks and greatly simplifies the training process. The advantage of the proposed control scheme is its ability to effectively improve the performance as it learns and gains more experience in real-time operations under uncertain changes of the environment. Therefore, the scheme has the adaptability to obtain the optimal control strategy for different users based on the demand and system configuration. Simulation results demonstrate that the proposed scheme can financially benefit the residential customers with the minimum electricity cost.     

Modelling and evaluation of control schemes for enhancing load shift of electricity demand for cooling devices

Load balancing in electricity grids becomes a more sophisticated problem by the increased availability of time-varying stochastic supply of electricity from conversion of renewable resources like wind or sunlight. Due to the fact that large quantities of electrical energy cannot be stored easily, demand side management by shifting electrical loads is one attempt to cope with this problem. In this paper we discuss and compare two types of control signals to use the thermal storage of electrical household appliances as balancing power. As the system of our research consists of a high number of controllable refrigerators with independent parameters and behaviour, we investigate the synergetic behaviour by a simulation model. For this objective we analyze a simulation model of controllable refrigerators with respect to their ability to shift their energy demand depending on parameterized external signals. We show that both types of control signals can be used for short term reserves with delivery within 15 min of time, but they differ in possible shapes of the resulting load curves and in the reaction time of the controlled system.In addition to the simulation model we develop a model of the synergetic behaviour of an ensemble of refrigerators' reaction on control signals. This mathematical model predicts the electricity demand of ensembles of controlled appliances. As it reduces the simulation model's complexity it could be used in a sophisticated control strategy, e.g. in a model predictive control approach. The general attempt to integrate the load shift potential of cooling devices into the control of an electricity grid can probably be transferred to other electrical appliances with thermal storage capacities.     

基于电力大数据的用户用电感知研究

在基于电力大数据对用户提供多元化服务的研究中,发现电网在不同时刻停电,不同用户的停电感受不一样以及在调度计划制定时,由于不同线路所带用户不同需进行差异化服务。为此,提出基于电力大数据的用户用电感知研究。首先通过电网内部系统及外部系统进行数据采集,然后基于大数据从多维度进行数据处理和分析,建立了用电需求模型和用户用电感知模型并进行了深入应用;通过该模型可以实现有限投资供电可靠性提升最快,最大限度满足用户需求;可最大限度实现不同行业、类别的用户用电互补,提高设备利用率;可实现用电感知最低时段停电,停电涉及用户更精准。该模型的引用实现了电网规划、用户接入、调度运行的智能决策,使电网规划投资更精准,固定投资提升可靠性最快,提高设备利用效率和用户满意度。     

一种基于遗传算法的智能电网调度方法

随着智慧电网的发展,调度控制系统中的数据规模和种类呈指数型上升并且处理复杂度较高。为了更好地进行电力调度,给予电力系统相应的决策支持和更好地为客户服务,满足用户在不同时段的电力需求,本文基于遗传算法提出一种多种类型可控电器的G-DSM算法,将负荷调度问题定义为成本最小化问题,并用遗传算法求解;结合从用户侧获取的电力大数据对用户的电力需求进行规划,降低了用户的花销以及峰值电力负荷,从而避免电力资源的浪费,提高了电网的工作效率。实验结果表明,该算法具有较好的可行性,并在实际操作中易于实现。     

针对延迟容忍用户的智能电网需求响应

针对用电请求可被延迟响应的设备,首先将调度这些设备的用电请求响应问题建立成随机优化模型.然后利用Lyapunov优化技术设计一种需求响应算法来实时计算设备用电请求何时被响应及被舍弃的用电请求量,从而降低用户们的电费支出及由于电能供不应求或电网承载超限而被舍弃的部分用电请求对他们造成的影响.进一步通过理论分析,当可再生能源发电量、用电请求量及市场电价服从独立同分布时,建立设备的用电请求被响应的最大等待时间与代价函数值之间的权衡关系.最后,仿真实验验证了理论分析结果的有效性.     

Robust design of a linear quadratic power control algorithm in multiple-access networks

In wireless networks under interference, power control is of the utmost importance to guarantee Quality of Service during data transmissions. A distributed perspective is commonly preferred to design controllers in each mobile user in the network for power allocation. The round-trip delay is a characteristic feature of wireless networks, and it was considered a known quantity in previous works. In this paper, we do not follow this assumption and propose the design of the power controller only with the information of upper and lower bounds on the round-trip delay as functions of a frequency gain. In a second stage, we relate the proposed robust design to common performance indicators, such as the step response overshoot. The proposed design rules can be applied to design a suitable robust controller for power control in a wireless network subject to interference.     

基于电网需求响应约束的多播路由

针对智能电网多播路由通信中,通常存在只考虑多播通信的时延约束而没有考虑电网需求侧带负载的情况,所构建的多播树会出现控制信息传输到大功率负载设备的通信时延较大的问题,提出一种考虑负载功率和通信时延的多播树构造方法,称为基于需求响应（DR）能力约束的多播路由算法。首先,根据电网拓扑信息生成满足约束条件的完全图;然后,采用Prim算法构造较低费用的多播树;最后,将多播树还原到原网络。仿真结果表明该算法能够有效地减小大功率负载设备的需求响应时延,与基于时延约束的多播路由算法相比,能够使电网频率波动大幅度减小。该算法能够有效地提高智能电网中需求响应的实时性,稳定电网频率。     

On making energy demand and network constraints compatible in the last mile of the power grid

In the classical electricity grid power demand is nearly instantaneously matched by power supply. In this paradigm, the changes in power demand in a low voltage distribution grid are essentially nothing but a disturbance that is compensated for by control at the generators. The disadvantage of this methodology is that it necessarily leads to a transmission and distribution network that must cater for peak demand. So-called smart meters and smart grid technologies provide an opportunity to change this paradigm by using demand side energy storage to moderate instantaneous power demand so as to facilitate the supply-demand match within network limitations. A receding horizon model predictive control method can be used to implement this idea. In this paradigm demand is matched with supply, such that the required customer energy needs are met but power demand is moderated, while ensuring that power flow in the grid is maintained within the safe operating region, and in particular peak demand is limited. This enables a much higher utilisation of the available grid infrastructure, as it reduces the peak-to-base demand ratio as compared to the classical control methodology of power supply following power demand. This paper investigates this approach for matching energy demand to generation in the last mile of the power grid while maintaining all network constraints through a number of case studies involving the charging of electric vehicles in a typical suburban low voltage distribution network in Melbourne, Australia.     

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into multiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility.