Three-stage day-ahead scheduling strategy for regional thermostatically controlled load aggregators

Thermostatically controlled loads(TCLs)are regarded as having potential to participate in power grid regulation.This paper proposes a scheduling strategy with three-stage optimization for regional aggregators jointly participating in day-ahead scheduling to support demand response.The first stage is on the profit of aggregators and peak load of the grid.The line loss and voltage deviation of regulation are considered to ensure stable operation of the power grid at the second stage,which guarantees the fairness of the regulation and the comfort of users.A single tempera-ture adjustment strategy is used to control TCLs to maximize the response potential in the third stage.Finally,digital simulation based on the IEEE 33-bus distribution network system proves that the proposed three-stage scheduling strategy can keep the voltage deviation within±5%in different situations.In addition,the Gini coefficient of distribu-tion increases by 20%and the predicted percentage of dissatisfied is 48%lower than those without distribution.

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.

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.

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.

Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator

Thermostatically controlled loads(TCLs)have great potentials to participate in the demand response programs due to their flexibility in storing thermal energy.The two-way communication infrastructure of smart grids provides opportunities for the smart buildings/houses equipped with TCLs to be aggregated in their participation in the electricity markets.This paper focuses on the realtime scheduling of TCL aggregators in the power market using the structure of the Nordic electricity markets a case study.An International Organization of Standardization(ISO)thermal comfort model is employed to well control the occupants’thermal comfort,while a rolling horizon optimization(RHO)strategy is proposed for the TCL aggregator to maximize its profit in the regulation market and to mitigate the impacts of system uncertainties.The simulations are performed by means of a metaheuristic optimization algorithm,i.e.,natural aggregation algorithm(NAA).A series of simulations are conducted to validate the effectiveness of proposed method.

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.

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.

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。

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。

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。

Controllability and stability of primary frequency control from thermostatic loads with delays

There is an increasing interest in exploiting theflexibility of loads to provide ancillary services to the grid.In this paper we study how response delays and lockout constraints affect the controllability of an aggregation of refrigerators offering primary frequency control(PFC).First we examine the effect of delays in PFC provision from an aggregation of refrigerators, using a two-area power system. We propose a framework to systematically address frequency measurement and response delays and we determine safe values for the total delays via simulations. We introduce a controllability index to evaluate PFC provision under lockout constraints of refrigerators compressors. We conduct extensive simulations to study the effects of measurement delay, ramping times, lockout durations and rotational inertia on the controllability of the aggregation and system stability. Finally, we discuss solutions for offering reliable PFC provision from thermostatically controlled loads under lockout constraints and we propose a supervisory control to enhance the robustness of their controllers.

Optimization modeling method for coal-to-electricity heating load considering differential decisions

Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids because of the randomness and dispersed integration of the load. However, the electric heating load may also function as an energy storage system with optimal operational control. Therefore, the optimal modeling of electric heating load characteristics, considering its randomness, is important for grid planning and construction. In this study, the heating loads of distributed residential users in a certain area are modeled based on the Fanger thermal comfort equation and the predicted mean vote thermal comfort index calculation method. Different temperatures are considered while modeling the users' heating loads. The heat load demand curve is estimated according to the time-varying equation of interior temperature. A multi-objective optimization model for the electric heating load with heat energy storage is then studied considering the demand response(DR), which optimizes economy and the comfort index. A fuzzy decision method is proposed, considering the factors influencing DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performs better than the traditional method.