绿电交易场景下计及温控负荷的高铁站两阶段调度策略

考虑绿电交易场景下清洁能源出力的不确定性和负载的波动性,基于随机模型预测控制提出一种计及温控负荷的高铁站两阶段能量优化调度策略。首先,根据热功率平衡原理,建立符合高铁站特性的温控负荷模型,并引入此温控系统参与高铁站两阶段调度;其次,应用多元正态分布描述随机变量误差间的概率相关性,采用蒙特卡洛抽样和基于概率距离的场景快速削减方法生成高铁站日内绿电、光伏和负荷的典型场景,基于模型预测控制应用典型场景对日内高铁站用电系统进行滚动优化调度;最后以某高铁站为算例,分析典型季节下引入此温控模型的调度结果,验证所提模型在改善高铁站经济运行、新能源消纳等方面的优势及所提两阶段调度策略的鲁棒性。

面向电网调峰的聚合温控负荷多目标优化控制方法

为使聚合温控负荷参与电网调峰时兼顾运行的经济性和清洁性,提出面向电网调峰的聚合温控负荷多目标优化控制方法。建立温控负荷聚合模型,在常规模型预测控制的基础上,计及温控负荷聚合特性,设计预测时间自适应优化策略,动态调整域参数以协调处理优化精度与计算时间之间的矛盾,同时建立双闭环反馈机制以实时反馈温控负荷状态信息,提升实现调峰计划的可靠性;引入拉格朗日乘子和松弛因子,将多输出约束条件变成多目标函数的附加部分,提高控制方法的稳定性;提出改进的哈里斯鹰算法,对多目标优化问题进行求解,缩短模型预测控制的执行延时,提高寻优速度和寻优精度。所提控制方法与其他控制方法的仿真对比结果表明,所提控制方法有效降低了电网峰谷差以及运行成本和排放成本,同时兼顾了聚合温控负荷参与电网调峰的技术性、经济性和清洁性。

针对温控负载变化的虚拟电厂控制策略研究

温控负载是一种变化剧烈、波动频繁、高峰负荷比例较高的负载,对电网的安全产生了极大的影响。为了应对这一问题,系统的输出功率往往会超过90%负荷最大值的调峰限制。针对虚拟电厂(VPP)系统内的超限过程进行了研究,并提出了2种控制策略,以降低超限总电量或者利用超限电量的同时获取收益。在验证策略的控制效果时,选取了2种由温控负载引起的功率需求的典型变化。研究结果表明,温控负载引起的功率需求峰值的变化对策略具有重要影响。在安全模式下,超限比例与峰值的增加呈反比,而与平均功率的增加呈正比;在收益模式下,峰值变化会影响储能放电过程,平均功率变化则会影响储能充电过程。随着功率需求的增加,收益逐渐减小,收益范围为3.62万~3.25万元。

空调温控负荷集群参与光伏消纳的潜力评估与互动框架

空调温控负荷集群作为当下最具调节潜力的需求侧响应资源之一,在削峰、填谷、分布式光伏消纳和电网调控中将发挥重要作用。因此,提出一种电力市场环境下,基于数据驱动和深度置信网络的空调温控负荷集群参与分布式光伏消纳的可调节潜力评估与互动框架。首先,利用数据驱动构建了基于深度置信网络的可调节潜力评估模型,实时输出温控负荷集群的可调节潜力;其次,考虑功率调整量在一定范围内变化的前提下,构建基于深度置信网络的需求互动模型,对温控负荷集群进行实时温度调控。最后,以冀北地区某10kV馈线作为实际算例进行分析,结果表明:所提框架能够充分利用空调温控负荷集群的可调节潜力,参与分布式光伏的消纳。

计及通信资源优化的温控负荷调控策略

海量需求侧数据的传输需求使得通信网络的压力成倍增加,容易造成通信延迟、拥塞、中断等现象,影响供需互动业务实时性,不利于负荷调控的进一步实施。针对上述问题,以实现通信受限情况下新能源发电的精准消纳为目标,提出一种计及通信资源优化的负荷精细化调控策略。首先基于信息物理融合技术,建立考虑通信网络影响的温控负荷调控机制。进而基于通信网络模型和计及通信时延的改进温控负荷模型,采用自适应权重与反向学习策略相结合的改进粒子群算法,实现考虑通信资源均衡的温控负荷精细化调控。最后通过算例仿真,验证了所提算法能够合理分配通信资源,使得温控负荷在通信链路时延较大的情况下仍具备良好的消纳能力。

基于能量块分配机制的温控负荷主动平衡控制方法

温控负荷是配电网供需平衡调控的重要资源,高效可行的负荷控制策略,能够使配电网达到理想的供需平衡目标。分布式光伏出力的不确定性,负荷启停的无序随机性,易造成配电网电压与负荷的波动。为此,该文提出一种基于能量块分配机制的温控负荷主动供需平衡控制方法,以平稳负荷曲线。采用吝啬算法调节设备使用能量块的顺序,使负荷调节量逐次逼近调节目标,避免多台设备同时启/停造成峰谷波动;考虑用户舒适度和用电公平,为设备分配能量块数量,避免设备长期占用电能资源;结合电压差分和时间槽延时,针对电压波动设计快速感知的分布式主动调节策略。仿真表明,该文方法能够实现负荷的有序控制、平稳负荷曲线,降低峰谷差。

A day-ahead scheduling framework for thermostatically controlled loads with thermal inertia and thermal comfort model

This paper proposes a day-ahead dispatch framework of thermostatically controlled loads(TCLs) for system peak load reduction. The proposed day-ahead scheduling framework estimates the user’s indoor thermal comfort degree through the building thermal inertia modelling. Based on the thermal comfort estimation, a dayahead TCL scheduling model is formulated, which consists of 3 stages: TCL aggregator estimates maximal controllable TCL capacities at each scheduling time interval by solving a optimization model;[ the system operator performs the day-ahead system dispatch to determine the load shedding instruction for each aggregator;and ′the TCL aggregator schedules the ON/OFFcontrol actions of the TCL groups based on the instruction from the system operator. A heuristic based optimization method, history driven differential evolution(HDDE)algorithm, is employed to solve the day-ahead dispatch model of the TCL aggregator side. Simulations are conducted to validate the proposed model.

Day-ahead scheduling of large numbers of thermostatically controlled loads based on equivalent energy storage model

Due to their heat/cool storage characteristics, thermostatically controlled loads(TCLs) play an important role in demand response programmers. However, the modeling of the heat/cool storage characteristic of large numbers of TCLs is not simple. In this paper, the heat exchange power is adopted to calculate the power instead of the average power, and the relationship between the heat exchange power and energy storage is considered to develop an equivalent storage model, based on which the time-varying power constraints and the energy storage constraints are developed to establish the overall day-ahead schedulingmodel. Finally, the proposed scheduling method is verified using the simulation results of a six-bus system.

Review of modeling and control strategy of thermostatically controlled loads for virtual energy storage system

The increasing penetration of renewable energy sources (RESs) brings more power generation fluctuations into power systems, which puts forward higher requirement on the regulation capacities for maintaining the power balance between supply and demand. In addition to traditional generators for providing regulation capacities, the progressed information and communication technologies enable an alternative method by controlling flexible loads, especially thermostatically controlled loads (TCLs) for regulation services. This paper investigates the modeling and control strategies of aggregated TCLs as the virtual energy storage system (VESS) for demand response. First, TCLs are modeled as VESSs and compared with the traditional energy storage system (ESS) to analyze their characteristic differences. Then, the control strategies of VESS are investigated in microgrid and main grid aspects, respectively. It shows that VESS control strategies can play important roles in frequency regulation and voltage regulation for power systems’ stability. Finally, future research directions of VESS are prospected, including the schedulable potential evaluation, modeling of TCLs, hierarchical control strategies of VESS considering ESSs and RESs and reliability and fast response in frequency control for VESS.

Distributed Control of Thermostatically Controlled Loads in Distribution Network with High Penetration of Solar PV

High penetration of solar energy can result in voltage rise in midday,while growth in residential air conditioning is the main contributor of overloading and voltage drop issues during peak demand time.This paper provides a hierarchical control scheme to coordinate multiple groups of aggregated thermostatically controlled loads to regulate network loading and voltage in a distribution network.Considering the limited number of messages that can be exchanged in a realistic communication environment,an event-triggered distributed control strategy is proposed in this paper.Through intermittent on and off toggling of air conditioners,the required active power adjustment is shared among participating aggregators to solve the issue.A case study is conducted and simulation results are presented to demonstrate the performance of the proposed control scheme.

Unified Control Strategy of Heterogeneous Thermostatically Controlled Loads with Market-based Mechanism

This paper studies the coordination of heterogeneous thermostatically controlled loads(TCLs)to provide the real-time ancillary services.A market-based control framework is adopted for its advantages.The first advantage is that the demand curve-oriented approach makes it possible to form a unified control scheme for heterogeneous loads without identifying their different characteristics.The second one is that the broadcast price signal helps simplify the downlink control and reduce the implementation cost.Then,the separate demand curve construction strategies based on a virtual price for different types of TCLs are presented.The flexibility of each TCL is reflected through the curve,and its practical constraints,i.e.,comfort requirements of users and operation constraints of devices,are satisfied explicitly.To ensure the control fairness and full utilization for the regulation ability of TCL cluster,a comfort-levelequality principle is applied in demand curve construction.Simulations are carried out to verify the effectiveness of the proposed method in providing frequency regulation services,for which a regulation capacity estimation method is developed.Finally,a series of case studies are conducted considering the practical situations,e.g.,model errors,imperfect communication and sudden load change after the end of services.

Power Fluctuation Damping of Thermostatically Controlled Loads Based on State Estimation

As a kind of important demand side resource,the thermostatically controlled loads(TCLs)play an important role in the peak load shifting and load balancing programs.This paper establishes a state-queuing(SQ)model for the large-scale TCLs,and estimates the system states based on the Kalman-filtering.Based on state estimation,the control strategy,whose parameters are optimized by the genetic algorithm(GA),is designed to damp the power fluctuation of the TCLs.Finally,the effectiveness of the proposed method is verified.

Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads

This work investigates an uncertainty quantification(UQ)framework that analyses the uncertainty involved in modelling control systems to improve control strategy performance.The framework involves solving four problems:identifying uncertain parameters,propagating uncertainty to the quantity of interest,data assimilation and making decisions under quantified uncertainties.A specific group of UQ approaches,known as the ensemble-based methods,are adopted to solve these problems.This UQ framework is applied to coordinating a group of thermostatically controlled loads,which relies on simulating a second-order equivalent thermal parameter model with some uncertain parameters.How this uncertainty affects the prediction and the control of total power is examined.The study shows that uncertainty can be effectively reduced using the measurement of air temperatures.Also,the control objective is achieved fairly accurately with a quantification of the uncertainty.

基于时间触发机制的温控负荷集群综合惯性控制研究

面对新型电力系统的低惯量趋势,规模庞大而位置分散的温控负荷在参与电力系统频率响应的同时,能够为电网提供虚拟惯量和一次调频支撑将是提升电网频率安全的新思路。为此,以集中-分布式调控框架下的温控负荷集群为研究对象,首先在详细分解控制层调控中心、协调层聚合商、响应层温控负荷终端三者在辅助调频过程中的采样、计算、控制、执行一系列实时任务的基础上,设计了事件触发与时间触发相结合的温控负荷集群终端异步响应机制,实现温控负荷集群功率遵从综合惯性控制律的效果。随后,对所提综合惯性控制下温控负荷集群参与调频的电力系统进行频率响应建模,并进一步利用改进劳斯近似实现系统降阶,从而获得系统频率响应关键特征的时域解析表达式。最后,通过仿真算例验证了所提基于时间触发机制的温控负荷集群综合惯性控制的有效性,并分析了采用改进劳斯近似对系统模型降阶的可行性。

基于市场机制与移动式储能的有源配电网阻塞管理方法

针对用电负荷时空聚集与新能源出力尖峰导致的配电网双向阻塞问题,鉴于市场环境下温控型负荷的响应潜力和移动式储能技术的时空灵活性,提出一种综合考虑市场机制与移动式储能的有源配电网日前–日内2阶段阻塞管理方法。首先,构建考虑温控型负荷响应特性的电力市场分层架构与交易机制,通过调整温控型负荷用电功率来实现配电网阻塞的日前管理;然后,考虑负荷响应不确定性及日前新能源发电功率预测误差,引入可适应配电网阻塞情况时空变化的移动式储能,搭建基于市场机制与移动式储能的日前–日内阻塞管理框架,在确保系统阻塞得以消除的同时兼顾经济性。算例结果表明所提方法可有效解决有源配电网阻塞问题,降低配电系统运营商阻塞管理成本。

Improved Model Predictive Control with Prescribed Performance for Aggregated Thermostatically Controlled Loads

Aggregate thermostatically controlled loads(AT-CLs)are a suitable candidate for power imbalance on demand side to smooth the power fluctuation of renewable energy.A new control scheme based on an improved bilinear aggregate model of ATCLs is investigated to suppress power imbalance.Firstly,the original bilinear aggregate model of ATCLs is extended by the second-order equivalent thermal parameter model to optimize accumulative error over a long time scale.Then,to ensure the control performance of tracking error,an improved model predictive control algorithm is proposed by integrating the Lyapunov function with the error transformation,and theoretical stability of the proposed control algorithm is proven.Finally,the simulation results demonstrate that the accuracy of the improved bilinear aggregate model is enhanced;the proposed control algorithm has faster convergence speed and better tracking accuracy in contrast with the Lyapunov function-based model predictive control without the prescribed performance.

Applications of thermostatically controlled loads for demand response with the proliferation of variable renewable energy

More flexibility is desirable with the proliferation of variable renewable resources for balancing supply and demand in power systems.Thermostatically controlled loads(TCLs)attract tremendous attentions because of their specific thermal inertia capability in demand response(DR)programs.To effectively manage numerous and distributed TCLs,intermediate coordinators,e.g.,aggregators,as a bridge between end users and dispatch operators are required to model and control TCLs for serving the grid.Specifically,intermediate coordinators get the access to fundamental models and response modes of TCLs,make control strategies,and distribute control signals to TCLs according the requirements of dispatch operators.On the other hand,intermediate coordinators also provide dispatch models that characterize the external characteristics of TCLs to dispatch operators for scheduling different resources.In this paper,the bottom-up key technologies of TCLs in DR programs based on the current research have been reviewed and compared,including fundamental models,response modes,control strategies,dispatch models and dispatch strategies of TCLs,as well as challenges and opportunities in future work.

计及离散控制偏差的典型温控负荷态势感知及调控策略

针对温控负荷离散化控制模式导致的临界温度效应累计偏差,提出一种计及离散控制偏差的温控负荷集群调控策略。通过引入修正因子改进负荷可消纳能力评估模型,并以热水器群为主要研究对象,将用户用水行为引入统一负荷模型,提升模型与实际环境的拟合性;提出负荷态势计算指标,基于其实时状态,以温度裕度、时间裕度和容量裕度为子指标进行综合,实现对于负荷调控能力的评价。仿真结果证明：综合考虑用户用能需求情况下,引入修正因子和用户行为分析的感知调控策略,可实现精准负荷调控,降低负荷资源储备需求。

居民温控负荷聚合功率及响应潜力评估方法研究

温控负荷是参与电网功率平衡的一类有效资源,在电网紧急事故下有助于快速维持系统的频率稳定。但由于其分散分布特性造成调度中心难以直接获得其聚合功率及响应潜力。为引导温控负荷参与电网调控运行,建立了温控负荷的近似聚合模型,能够较为方便地确定其聚合功率的估计值及上下限范围。基于该模型,提出了一种计及响应不确定性的温控负荷聚合响应潜力评估方法,对温控负荷的聚合响应潜力及其分布特性进行了评估。通过仿真,验证了近似聚合模型和潜力评估方法的有效性。

基于温控负荷控制技术的新能源优化利用方法

分布式电源包含风、光等新能源发电,其发电功率具有间歇性和不确定性,给微网中新能源的优化利用及其运行控制带来一定的困难。传统的微网运行控制思路是依靠蓄电池等储能设备作为主要控制手段,建设和使用成本均较高,难以推广。为此,引入需求侧响应,提出了一种基于改进coloredpower算法的加权系数排队算法,可用于直接控制家居环境常见的空调、热泵等温控负荷参与需求侧响应,进而实现微网的优化运行控制。加权系数排队算法允许用户根据自己的喜好和需要选择负荷设备的响应程度,既能够保证控制效果,又兼顾了用户的舒适度与公平性。以社区级并网运行的微网为例,对1000台家用空调设备进行仿真控制,结果表明了算法的有效性和正确性。