Individual Thermostatically Controlled Loads Research Articles

Statistical mechanics of thermostatically controlled multizone buildings.

We study the collective phenomena and constraints associated with the aggregation of individual cooling units from a statistical mechanics perspective. These units are modeled as thermostatically controlled loads (TCLs) and represent zones in a large commercial or residential building. Their energy input is centralized and controlled by a collective unit-the air handling unit (AHU)-delivering cool air to all TCLs, thereby coupling them together. Aiming to identify representative qualitative features of the AHU-to-TCL coupling, we build a simple but realistic model and analyze it in two distinct regimes: the constant supply temperature (CST) and the constant power input (CPI) regimes. In both cases, we center our analysis on the relaxation dynamics of individual TCL temperatures to a statistical steady state. We observe that while the dynamics are relatively fast in the CST regime, resulting in all TCLs evolving around the control set point, the CPI regime reveals the emergence of a bimodal probability distribution and two, possibly strongly separated, timescales. We observe that the two modes in the CPI regime are associated with all TCLs being in the same low or high airflow states, with an occasional collective transition between the modes akin to Kramer's phenomenon in statistical physics. To the best of our knowledge, this phenomenon has been overlooked in building energy systems despite its direct operational implications. It highlights a trade-off between occupational comfort-related to zonal temperature variations-and energy consumption.

A unified state space model for aggregation and coordination of large-scale TCLs and EVs for frequency regulation

This paper presents a unified state space model for aggregation and coordination of large-scale thermostatically controlled loads (TCLs) and electric vehicles (EVs) to jointly participate in the frequency regulation of power systems. The proposed approach is characterized by low communication requirements and an accurate regulation capacity estimation. In order to perform the coordination with minimum communication burden, two distinct control signals, namely TCL-control signal and EV-control signal are developed. The former is communicated to all individual TCLs while the latter is communicated to all individual EVs. The simulations are conducted for a community-level microgrid including a large population of TCLs and EVs, a conventional generator and a wind generation system. It is shown that the proposed control structure can accurately describe the aggregated behaviour of a large population of TCLs and EVs and can efficiently counteract the frequency deviations. Moreover, the proposed control structure reduces the excessive dependence on one type of resource, minimizes the short cycling of TCLs, guarantees the energy requirements of the EVs and respects the energy constraints of the EV batteries.

Game-Theoretic Demand Side Management of Thermostatically Controlled Loads for Smoothing Tie-Line Power of Microgrids

Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users' characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods.

Robust Hierarchical Control Mechanism for Aggregated Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) are good candidates for direct load control (DLC). They can be aggregated to join the electricity market through a centralized management performed by virtual power plant (VPP). However, there are two main concerns that arise when TCLs participate in DLC. The first is related to the dispatchability of VPP against normal energy demand of individual TCLs in uncertain time-variant environments. The second refers to the tremendous increase of communication and computational requirements needed to perform DLC on a large population of TCLs. This paper introduces aggregators between the DLC controller and TCLs with a novel robust control mechanism to reconcile these concerns. The control mechanism is implemented with two layers: the upper layer suppresses the control payback effect with a quadratic optimization model, and the lower layer addresses the power trajectory tracking with a novel payback tracking error model (PTEM). The control method needs minimum sensing infrastructure since it requires power data only at the aggregation level. Our simulations resulted in a robust reference power tracking by the aggregator with a percentage root mean squared error between 3.33% – 5.69% under uncertain time-variant environments. The continuous responsiveness indicates that the aggregators manage to convert the aggregated TCLs into “manageable resources” that ensure the dispatchability of VPP.

Grouping control strategy for aggregated thermostatically controlled loads

Thermostatically controlled loads (TCLs) have great potential to participate in demand response (DR) programs due to their thermal storage and transfer characteristics. In this paper, we propose a grouping control strategy based on customer comfort and power matching degree for aggregated TCLs. We also model an individual TCL based on equivalent thermal parameter (ETP). The aggregated TCLs are modeled with the time-varying Markov chain and 2D state-bin transition with the consideration of their heterogeneities. A load selecting process is introduced based on a multi-scale priority sorting composed of normalized temperature distance, power rating similarity and controlled times. These indexes increase the accuracy and speed of the tracking response to the optimum control signal. The simulation results verify that the proposed control strategy has a comprehensive performance on the tracking response, customer comfort, and the fairness of TCLs participated in DR programs.

A strategy for contingency management with aggregated thermostatically controlled loads in china’s electricity market

Faced with the deregulation of power grid in China, the traditional operation dispatch of system and the management of customers will change enormously. In the new environment, the power consumption of customers could be adjusted with the information and communication technology, which provide a new approach for system operator to maintain the power balance between supply and demand during contingencies. In China, thermostatically controlled loads (TCLs) account for a large proportion of power consumptions and have a great potential to provide regulating reserve for system. Therefore, a strategy for contingency management with TCLs is proposed in this paper to guarantee the reliable operation of power system. Firstly, the regulating capacity models of aggregated TCLs are proposed by the aggregation of the model for one individual TCL considering the customer behaviour and electric-thermal characteristics. What’s more, an optimization model integrating the aggregated TCLs and conventional generators is proposed to determine the operation dispatch during contingencies. The proposed methods are validated using the modified IEEE 30-bus system.

Hierarchical Control of Thermostatically Controlled Loads for Primary Frequency Support

This paper proposes a hierarchical control of thermostatically controlled loads (TCLs) to provide primary frequency control support. The control architecture is composed of three levels. At the high level, an aggregator coordinates multiple distribution substations and dispatches the primary reserve references. At the middle level, distribution substations estimate the available power of TCLs based on the aggregated bin model, and dispatch control signals to individual TCLs. At the local level, a supplementary frequency control loop is implemented at the local controller, which makes TCLs respond to the frequency event autonomously. Case studies show that the proposed controller can efficiently respond to frequency events and fulfill the requirement specified by the system operator. The users' comforts are not compromised and the short cycling of TCLs is largely reduced. Due to the autonomous control, the communication requirement is minimized.

Managing Communication Delays and Model Error in Demand Response for Frequency Regulation

This study develops and compares several networked control and estimation algorithms to manipulate the power consumption of a population of residential thermostatically controlled loads (TCLs) to fulfill PJM frequency regulation requests given an imperfect communication network and modeling error. The algorithms rely on a model of the plant to reduce the effects of communication delays, and include a stochastic, predictive controller and two Kalman filter-based state estimation techniques. The first estimator uses a set of independent Kalman filters that run in parallel, and the second incorporates individual TCL models that rely on identified thermal parameters. We use simulations to examine 1) the algorithms’ ability to adequately provide frequency regulation under a range of delay severities, and 2) the effect of increased modeling error. We find that both estimator–controller combinations provide acceptable frequency regulation with average delays of 20 s and minor modeling error. When we increase the modeling error by using a higher order model to represent the TCLs within the plant, the first estimator provides acceptable frequency regulation, whereas the second estimator provides poor frequency regulation.

Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals

Thermostatically controlled loads (TCLs) have been studied to provide operating reserve for maintaining power balance between supply and demand. However, operating reserve capacity (ORC) supplied by aggregated TCLs is difficult to evaluate, due to the insufficient information of heterogeneous TCLs and consumer behaviors. This paper proposes a quantitative ORC evaluation method for large-scale aggregated heterogeneous TCLs without sufficient measurement data. Firstly, an individual TCL model on account of consumer behaviors is developed to characterize the impact of fluctuated electricity prices and different thermal comfort requirements. Secondly, a novel optimization model of heterogeneous TCLs, which can guarantee consumer satisfaction, is proposed to provide operating reserve for power systems. Thirdly, the probability density estimation (PDE) method is developed to evaluate the ORC provided by large-scale heterogeneous TCLs with insufficient data. Numerical studies illustrate the effectiveness of the proposed models and methods.

A Geometric Approach to Aggregate Flexibility Modeling of Thermostatically Controlled Loads

Coordinated aggregation of a large population of thermostatically controlled loads (TCLs) presents a great potential to provide various ancillary services to the grid. One of the key challenges of integrating TCLs into system level operation and control is developing a simple and portable model to accurately capture their aggregate flexibility. In this paper, we propose a geometric approach to model the aggregate flexibility of TCLs. We show that the set of admissible power profiles of an individual TCL is a polytope, and their aggregate flexibility is the Minkowski sum of the individual polytopes. In order to represent their aggregate flexibility in an intuitive way and achieve a tractable approximation, we develop optimization-based algorithms to approximate the polytopes by the homothets of a given convex set. As a special application, this set is chosen as a \emph{virtual battery model} and the corresponding optimal approximations are solved efficiently by equivalent linear programming problems. Numerical results show that our algorithms yield significant improvement in characterizing the aggregate flexibility over existing modeling methods. We also conduct case studies to demonstrate the efficacy of our approaches by coordinating TCLs to track a frequency regulation signal from the Pennsylvania-New Jersey-Maryland (PJM) Interconnection.

Demand Response of a TCL Population Using Switching-Rate Actuation

This paper considers the problem of actively managing the power consumption of a large number of thermostatically controlled loads (TCLs), namely, a TCL population and a case study of household refrigerators. Control is performed using a new randomized actuation that consists of switching units ON and OFF at given rates, while at the same time respecting the nominal constraints on each individual unit. Both the free and the controlled behavior of individual TCLs can be aggregated, making it possible to handle a TCL population as if it were a single system. The aggregation method uses the distribution of the TCLs individual states across the population. The distribution approach has two main advantages. It scales excellently, since the computational requirements do not increase with the number of units, and it allows data from individual units to be used anonymously, which solves privacy concerns relevant for consumer adoption.

Impact of Disturbances on Modeling of Thermostatically Controlled Loads for Demand Response

Aggregations of thermostatically controlled loads (TCLs) have been shown to hold promise as demand response resources. However, the evaluation of these promises has relied on simulations of individual TCLs that make important assumptions about the thermal dynamics and properties of the loads, the end-user’s interactions with individual TCLs and the disturbances to their operation. In this paper, we first propose a data-driven modeling strategy to simulate individual TCLs—specifically, household refrigeration units (HRUs)—that allows us to relax some of these assumptions and evaluate the validity of the approaches proposed to date. Specifically, we fit probability distributions to a year-long dataset of power measurements for HRUs and use these models to create more realistic simulations. We then derive the aggregate system equations using a bottom-up approach that results in a more flexible [linear time invariant (LTI)] system. Finally, we quantify the plant-model mismatch and evaluate the proposed strategy with the more realistic simulation. Our results show that the effects of invalid assumptions about the disturbances and time-invariant properties of individual HRUs may be mitigated by a faster sampling of the state variables and that, when this is not possible, the proposed LTI system reduces the plant-model mismatch.