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.

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.

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.

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.

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.

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.

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.

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.

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。

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.

分布式的温控负荷集群负荷跟随控制

温控负荷(thermostatically controlled loads,TCLs)集群作为一种灵活的可调度资源,已成为促进电网经济运行和帮助电网恢复稳定的有力手段之一。然而,由于温控负荷单体功率小、位置分散且参数各异,给调度带来了困难。为了灵活利用数量庞大的负荷侧资源进行负荷跟随控制,该文建立温控负荷的虚拟电池模型和负荷集群的聚合模型,并提出基于双层分布式通信网络的控制策略。上层利用分布式交替方向乘子法(alternating direction method of multipliers,ADMM)来解决不同负荷聚合器的最佳跟随功率问题,以确保跟随效益最优;下层提出基于快速分布式平均一致性算法的深度神经网络(deep neural networks,DNN)的方法,使得聚合器内部的所有温控负荷以相等的虚拟电池荷电状态(state of charge,SoC)快速共享上层得到的跟随功率,并有效减少了通信数据量。不同时间尺度的算例验证提出的控制策略能够实现快速的负荷跟随,并保证用户侧的效益。

基于动态下垂控制的温控负荷一次调频控制策略

利用温控负荷(thermostatically controlled load,TCL)作为调频资源参与电网调频是应对高比例可再生能源发电并网导致电力系统维持频率稳定能力下降的有效手段。文中提出一种基于动态下垂控制的TCL一次调频控制策略,考虑聚合商TCL平均温度状态(state of temperature,SOT)、调频容量和系统频率变化率,构建动态下垂控制模型,实时调整下垂控制系数,改变TCL聚合商调频参与度。当向上调频时,锁定关状态TCL;当向下调频时,锁定开状态TCL,确保频率快速恢复至额定值附近。同时,为确保用户的舒适度,利用TCL的SOT优先级排序列表派遣法,确定TCL参与调频顺序。在Matlab/Simulink平台上进行仿真分析,仿真结果验证了文中所提策略在确保用户舒适度、改善频率质量等方面的有效性。

考虑群体热适应性的楼宇综合能源系统规划

充分发挥楼宇综合能源系统能源生产侧与消费侧源荷互动特性的作用,需要由供能的舒适度来保证。基于群体热舒适感会随季节调整其对环境的热期望的热适应性原理,建立楼宇综合能源系统中的能量枢纽选型定容统一优化模型。模型计及典型日室外温度、光照辐射强度和刚性电热负荷对室内温控负荷及能量枢纽出力的影响,将室温调节策略和能量枢纽容量配置基于群体热环境舒适区间进行约束,实现楼宇热舒适供能与能量枢纽架构经济性的协同优化,并采用Benders分解方法加以求解。算例分析结果表明,对热舒适区间进行修正后的规划模型能减少能量枢纽的配置容量,可有效提高可再生能源利用率并降低系统整体的碳排放量。

考虑用户需求响应的空调负荷日前-实时聚合调控

恒温控制负荷是参与需求响应的主要负荷资源,在夏季高温下可对负荷开展集群控制,有效减小负荷峰值。但由于各用户室内的初始温度具有多样性和随机性的特点,会使聚合商在负荷削减时产生负荷波动和在用户舒适度不同时会产生用户响应度低的问题。为能够合理引导负荷参与电网运行,基于空调状态队列模型,提出了用户激励方案与空调负荷提前控制策略,不仅激励用户参与控制也减小了控制时间内的负荷波动。通过空调负荷近似聚合模型得到空调负荷聚合功率,根据用户舒适度区间进行分组控制,构建激励方案以提高用户的响应能力。在实时运行阶段,运用状态队列模型对参与用户提前控制,减少因初始温度分布不均所导致的削减负荷波动。仿真表明:提出的激励方案对用户的补偿更加全面,可以激励更多的用户参与控制,通过提前控制策略和跨组调动可实现对负荷精准控制,削弱由于初始温度不均和负荷退出对减载带来的影响。