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Thermostatically controlled load (TCL) contributes a relatively higher proportion of energy consumption. Its mathematical models can be used for quantifying the real-time supply and demand balance between the power generations and load systems. The relevant studies have received great attention with the development of smart grids in recent years. This study mainly presented the development of TCL mathematical models from a single model to aggregated models. Finally, the advantages of these models were compared and analyzed based on the simulation results. This study could provide a theoretical basis for the applications of TCLs in the supply and demand balances. [ABSTRACT FROM AUTHOR]

This paper introduces new insights into the integration of thermostatically controlled loads (TCLs) as hybrid energy sources for grid ancillary and demand response services. Leveraging a generalized virtual battery (VB) model emerges as an effective approach to determine maximum reserve capacity of these aggregated devices. Our research extends the VB model and endeavors to establish a pragmatic framework for EWH control for peak shaving and managing the payback effect. The importance of aggregator capacity in mitigating the impacts of TCL external control, such as customer comfort and safety, is emphasized. Electric water heaters (EWHs) are used as the residential TCL device given their extensive availability and thermal capacity. Two TCL control scenarios, OFF control and ON/OFF control are compared using the Model Predictive Control (MPC) method. The ON/OFF control was found to improve peak shaving capability by approximately 47% when compared to the more rudimentary OFF control mechanism. The main contributions of this study are threefold: assessment of maximum reserve capacity using a modified VB model, creation of a reference control signal based on this result, and development of effective control strategies for managing the payback effect when maximal reserves are utilized. The robustness of the maximum capacity estimation is analyzed through a sensitivity analysis mainly driven by variations in hot water consumption and communication loss. Comprehensive and comparative simulation results show improved capabilities in the utilization of the maximum reserved energy of loads, minimizing the expected payback to avoid additional energy peaks and assessing the impacts of external factors that can affect the expected maximum capacity of the VB.

This paper presents a new aggregate power tracking control scheme for populations of thermostatically controlled loads (TCLs). The control design is performed in the framework of partial differential equations (PDEs) based on a late-lumping procedure without truncating the infinite-dimensional model describing the dynamics of the TCL population. An input-output linearization control scheme, which is independent of system parameters and uses only partial state measurement, is derived, and a sliding mode-like control is applied to achieve finite-time input-to-state stability for tracking error dynamics. Such a control strategy can ensure robust performance in the presence of modeling uncertainties, while considerably reducing the communication burden in large-scale distributed systems similar to that considered in the present work. A rigorous analysis of the closed-loop stability of the underlying PDE system was conducted, which guarantees the validity of the developed control scheme. Simulation studies were performed while considering two TCL populations with a significant difference in their size, and the results show that the developed control scheme performs well in both cases, thereby confirming the effectiveness of the proposed solution.

The existing research on orderly participation of air conditioning load in frequency regulation needs a relatively complete communication architecture, but it cannot be achieved on a large scale in practice. This paper proposes a decentralized control method for the air-conditioning load control to participate in the power system primary frequency regulation, so that the air-conditioning loads can respond to system frequency deviation in an orderly manner without communication. Firstly, the triggering frequency is set to prevent the air-conditioning load from being over-or under-adjusted based on the frequency response interval, which is determined according to the outdoor temperature. Secondly, the response priority is arranged according to the holding time to ensure an orderly response of the air-conditioning load without communication. Finally, a simulation example of a thousand air-conditioning loads under different control strategy proves that the proposed method can achieve better frequency regulation effect. [ABSTRACT FROM AUTHOR]

To achieve the flexible operation of the active distribution network (ADN), an optimal control method for the ADN considering the soft open point (SOP) and thermostatically controlled loads under distributed photovoltaic (PV) access is proposed. First, the mathematical model of the SOP and thermostatically controlled loads are constructed based on the flexibility of the SOP and the thermal storage characteristic of thermostatically controlled loads, respectively. Then, the mathematical model of theADNconsidering the SOP and thermostatically controlled loads are further constructed. The optimal control of the ADN is realized by adjusting the transmission power of the SOP and the switching state of the electric water heater. Finally, the optimal operation analysis of the SOP and thermostatically controlled loads under various control methods is carried out, and the impact of the SOP and thermostatically controlled loads on the economic and secure operation of the ADN is further investigated. The results show that the proposed method taps the demand response potential of thermostatically controlled loads while satisfying the water consumption habits of different customers, and utilizes the network reconfiguration with the SOP to further improve the security and economy of the ADN operation. [ABSTRACT FROM AUTHOR]

A test station was designed and built to measure the thermal stratification (vertical thermal gradients) in a horizontal 150 L electric water heater storage device for different external environmental and user-emulating inputs, ambient temperature, inlet water temperature, water draw flow rate, thermostat temperature and heating coil active time. Stratification measurement consists of 66 temperature sensors that coarsely map three-dimensional temperature gradients within the horizontally configured heating device, under different operational conditions. Other significant temperatures such as inlet, outlet, ambient, thermostat feedback, and wall temperatures were also recorded. An ambient temperature control (climatic) chamber was built to emulate the environmental conditions.A custom-built controller was used to control thermal actuators, the power to the thermostat and the state of the outlet valve. In addition, it also acted as a data acquisition system, responsible for recording all relevant sensor data that was present in the system as well as the test station operating states.The dataset includes heating and thermostat control data and the corresponding thermal stratification profiles under static operating conditions (no water draw) at different ambient temperatures (20 °C, 30 °C, and 40 °C).Additionally, dynamic operating conditions (with water draw) are recorded, maintaining inlet water temperatures at approximately 10 °C and 30 °C. The dataset highlights thermal profiles with and without thermostat control and ambient cooling data without thermostat control.Existing research primarily focuses on vertically oriented water heaters, with limited attention to thermal modelling and data acquisition in horizontal configurations. This work provides valuable insights into the internal thermal dynamics of horizontally configured electric water heater storage devices, widely used worldwide.

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

The existing research on orderly participation of air conditioning load in frequency regulation needs a relatively complete communication architecture, but it cannot be achieved on a large scale in practice. This paper proposes a decentralized control method for the air-conditioning load control to participate in the power system primary frequency regulation, so that the air-conditioning loads can respond to system frequency deviation in an orderly manner without communication. Firstly, the triggering frequency is set to prevent the air-conditioning load from being over- or under-adjusted based on the frequency response interval, which is determined according to the outdoor temperature. Secondly, the response priority is arranged according to the holding time to ensure an orderly response of the air-conditioning load without communication. Finally, a simulation example of a thousand air-conditioning loads under different control strategy proves that the proposed method can achieve better frequency regulation effect.

To achieve the flexible operation of the active distribution network (ADN), an optimal control method for the ADN considering the soft open point (SOP) and thermostatically controlled loads under distributed photovoltaic (PV) access is proposed. First, the mathematical model of the SOP and thermostatically controlled loads are constructed based on the flexibility of the SOP and the thermal storage characteristic of thermostatically controlled loads, respectively. Then, the mathematical model of the ADN considering the SOP and thermostatically controlled loads are further constructed. The optimal control of the ADN is realized by adjusting the transmission power of the SOP and the switching state of the electric water heater. Finally, the optimal operation analysis of the SOP and thermostatically controlled loads under various control methods is carried out, and the impact of the SOP and thermostatically controlled loads on the economic and secure operation of the ADN is further investigated. The results show that the proposed method taps the demand response potential of thermostatically controlled loads while satisfying the water consumption habits of different customers, and utilizes the network reconfiguration with the SOP to further improve the security and economy of the ADN operation.

Abstract 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 temperature 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 distribution increases by 20% and the predicted percentage of dissatisfied is 48% lower than those without distribution.

As more and more controllable loads are connected to the power grid, air conditioning loads (ACLs) can be used as flexible loads to participate in load shaving and accommodate the fluctuation caused by renewable energies because of its characteristics of fast response, large potential adjustment power, cold storage and heat storage. In the actual aggregated process of ACLs, the changes of external conditions such as outdoor temperature, temperature of ACLs and number of ACLs will have a certain effect on the aggregated model. However, the time-varying characteristic of the aggregate ACLs′ external model (AAEM) is not be considered in the most of existing control methods. A data-driven-based AAEM and its corresponding load tracking control strategy are proposed. Based on the cost function of individual ACLs considering thermodynamic characteristics, the data-driven-based AAEM is established by using neural network. By this way, computational complexity of calculation AAEM is reduced. The load tracking control strategy considering the data-driven-based AAEM can effectively reduce the number of times of switching and the total cost. The simulation results verify the effectiveness of this method.

We consider the framework of convex high dimensional stochastic control problems, in which the controls are aggregated in the cost function. As first contribution, we introduce a modified problem, whose optimal control is under some reasonable assumptions an ε -optimal solution of the original problem. As second contribution, we present a decentralized algorithm whose convergence to the solution of the modified problem is established. Finally, we study the application of the developed tools in an engineering context, studying a coordination problem for large populations of domestic thermostatically controlled loads. [ABSTRACT FROM AUTHOR]

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 temperature 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 distribution increases by 20% and the predicted percentage of dissatisfied is 48% lower than those without distribution. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coordinated control of electric loads can provide valuable grid services, such as frequency regulation. However, due to the nonlinear characteristics of such load ensembles, it is important to systematically analyze their behavior and establish a thorough understanding of undesirable phenomena that can potentially arise. In this paper, we analyze the frequency response of an aggregate control scheme, with the goal of exploring controller performance limits. We show that rapid switching commands can induce oscillations in the power output due to the inherent lockout mechanism of the underlying devices. We demonstrate that highly detailed aggregate models are required to capture such phenomena. Such models enable deeper understanding of the control boundaries and therefore play an important role in avoiding the introduction of undesirable effects on the grid. • Frequency response analysis of a thermostatically controlled load aggregation. • Rapid switching commands can induce low-frequency power oscillations. • Oscillations are linked to the lockout mechanism protecting the compressor. • Highly detailed aggregate models are needed to capture the oscillations. • Case studies demonstrate the oscillation phenomenon and model performance. [ABSTRACT FROM AUTHOR]

Modern power systems require flexible demand-side resources to maintain the balance between electricity supply and demand. Building thermostatically controlled loads (TCLs) are great flexible assets, as they account for a significant portion of electricity consumption in buildings. In this regard, demand response (DR) programs have long been used by grid operators to enable the coordination of TCLs to reveal and utilize this demand flexibility. Existing efforts in DR are mostly model-based, which is not scalable because gathering the physical parameters and developing an accurate model for each participating load is impractical. To address this limitation, this paper proposes a data-driven, distributed hierarchical transactional control approach, which leverages concepts from machine learning, game theory, and model-free control. In this approach, the interactions between a distribution system operator (DSO) and load aggregators (LAs) in the upper level are designed as a Stackelberg game, where the DSO is the leader and the LAs are the followers. The DSO aims to maximize its profit and minimize the total demand peak-to-average ratio (PAR) of the system, whereas the LAs aim to minimize their electricity cost while maintaining the quality of service provided. In the lower level, the LAs control the individual loads of end users while tracking the optimal aggregated load profile passed from the upper level. The performance of this approach is evaluated using a case study with a single DSO, three LAs, and 300 TCLs. The results show that the proposed approach can achieve profit maximization and peak load reduction for the DSO, and cost reduction for the LAs while maintaining the comfort of the end users.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate control under combined parametric and additive uncertainties. The proposed SMPCap approach enables the quantification, and manipulation, of both the mean and covariance of the stochastic system states and inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared to the conventional deterministic MPC (DMPC) and the state-of-the-art SMPCa only accounting for additive uncertainties, at the cost of a maximum relative increase in energy use of 21.6% and 4.2%, respectively. By incorporating the SMPCap strategy in an integrated optimal control and design (IOCD) approach, its additional added value for obtaining a more appropriate, yet robust, heat supply system sizing is illustrated. Via simulations, size reductions up to 33.3% are shown to be achievable for a terraced single-family dwelling without increasing thermal discomfort compared to an IOCD approach incorporating DMPC. [ABSTRACT FROM AUTHOR]

We propose a multi-case, multi-period AC model to coordinate the procurement of grid services from an aggregation of connected distributed energy resources and microgrids. The proposed model aggregates the capabilities of all resources in the system to manage the network’s power flow and provide services to the power grid at the point of common coupling. The modeling approach enables incorporating a number of services (e.g., reserve, regulation, and reactive power capacity) with simple addition of service defining equations. We also propose a solution methodology for the resultant mixed-integer quadratically-constrained programming (MICQP) problem based on successive linear programming (SLP). The SLP is augmented with a fix-binary algorithm that promises efficient handling of discrete variables. The paper also provides a decentralized formulation that decomposes the problem into an aggregator problem and multiple small $\mu $ Gs’ problems. We integrate the SLP method with the analytical target cascading (ATC) algorithm to optimize the overall system with minimal shared information between the aggregator and the $\mu $ Gs. Case studies analyze the convergence of the centralized and the decentralized approaches, present the optimal power and service schedules of the tested system, and study the network conditions under service calls. [ABSTRACT FROM AUTHOR]

Among various kinds of flexible loads resources in power system, the thermostatically controlled loads (TCLs) are considered as the excellent candidates for the demand response (DR) programs due to the heat/cool storage characteristics. Effective operation and coordination of TCLs in DR programs requires an accurate model to capture the aggregate power. One appealing approach is the equivalent energy storage (EES) method, which describes the change of aggregate power offered by the TCLs as the dynamic of an energy storage model with limits on the output power and the energy capacity. In this paper, an EES model with the second-order equivalent thermal parameters (second-order ETP) model is established for interpretation of TCLs dynamic characteristics. Considering the internal and external heat exchange of the TCLs, the relationships between the heat exchange power and energy storage of the TCLs EES model are redefined. In order to exert the actual potential of TCLs in day-ahead scheduling, the time-varying charging-discharging power and energy storage constraints of the TCLs EES model are developed. By this way, more feasible scheduling results can be obtained. The performance of the proposed method is verified by the testing results.

In order to make Thermostatically Controlled Loads (TCLs) better meet the scheduling requirements, a day-ahead scheduling of equivalent energy storage model that takes into account of the minimum-on-off time is established. By considering the minimum-on-off time, the charging and discharging power, as well as the energy storage are modified, and the relationship between heat exchange power and energy storage are developed. By this way, the equivalent energy storage model more accurately reflects the real thermodynamic characteristic of TCLs and enables TCLs to exert the actual potential to participate in the scheduling. Finally, the simulation results verify the feasibility of the proposed scheduling method.

A transition from generation on demand to consumption on demand is one of the solutions to overcome the many limitations associated with the higher penetration of renewable energy sources. Such a transition, however, requires a considerable amount of load flexibility in the demand side. Demand response (DR) programs can reveal and utilize this demand flexibility by enabling the participation of a large number of grid-interactive efficient buildings (GEB). Existing approaches on DR require significant modelling or training efforts, are computationally expensive, and do not guarantee the satisfaction of end users. To address these limitations, this paper proposes a scalable hierarchical model-free transactional control approach that incorporates elements of virtual battery, game theory, and model-free control (MFC) mechanisms. The proposed approach separates the control mechanism into upper and lower levels. The MFC modulates the flexible GEB in the lower level with guaranteed thermal comfort of end users, in response to the optimal pricing and power signals determined in the upper level using a Stackelberg game integrated with aggregate virtual battery constraints. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable even on small embedded devices. The effectiveness of this approach is demonstrated through a large-scale case study with 10,000 heterogenous GEB. The results show that the proposed approach can achieve peak load reduction and profit maximization for the distribution system operator, as well as cost reduction for end users while maintaining their comfort.

Integrating large amounts of volatile renewable power into the electricity grid requires ancillary services (ASs) from multiple providers including flexible demand. These should be comparable by uniform and efficiently evaluable performance criteria. The objective is to characterize the technical flexibility of aggregated building heating, ventilation, and air conditioning (HVAC) under different operating conditions. New bounds of flexible power and holding durations, accordingly pay‐back power and recovery times, and ramping rates are derived, using a new gray‐box model of stochastically actuated aggregations of thermostatically controlled loads (TCLs) that can serve as well for load control. New closed formulas of the expected switching temperatures are derived using survival processes and hazard functions. This ex‐ante characterization enables fast decision tools for AS feasibility testing and planning by demand aggregators, as it neither relies on simulation or optimization, nor on the identification and clustering of unit‐level parameters. The estimates are explored in a sensitivity study of urban‐level heat pump heating with respect to six key input factors. A case study using dynamic regulation signals from Pennsylvania–New Jersey–Maryland (PJM) demonstrates the benefit, in terms of tracking precision, of the refined energy measures over pure energy or power capacity bounds. [ABSTRACT FROM AUTHOR]

For thermostatically controlled loads (TCLs) to perform demand response services in real-time markets, recursive methods for parameter estimation are needed.  As the physical characteristics of a TCL change (e.g. the contents of a refrigerator or the occupancy of a conditioned room), it is necessary to update the parameters of the TCL model. Otherwise, the TCL will be incapable of accurately predicting its potential energy demand, thereby decreasing the reliability of a TCL aggregation to perform demand response. In this paper, we investigate the potential of an unscented Kalman filter (UKF) algorithm to identify a TCL model that is non-linear in the parameters. Experimental results demonstrate the parameter estimation of two residential refrigerators.

A fundamental requirement of the electric power system is to maintain a continuous and instantaneous balance between generation and load. The intermittency and uncertainty introduced by renewable energy generation requires the expansion of ancillary power system services to maintain such a balance. In this paper, we examine the potential of thermostatically controlled loads (TCLs), such as refrigerators and electric water heaters, to provide generation following services in real-time energy markets (1 to 5 minutes). To simulate the non-linear dynamics of hysteretic dead-band systems in a manner suitable for convex optimization, we introduce an alternative control trajectory representation of the TCLs and their discrete input signals. To perform distributed optimization across large populations of TCLs, we propose a variation of the alternating direction method of multipliers (ADMM) algorithm. Based on our simulation results, we have demonstrated the potential for controlling a population of TCLs within an error tolerance of 10 kW.

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

A novel hierarchical Energy Management System (EMS) framework is proposed for the incorporation of Thermostatically Controlled Loads (TCLs) in the provision of ancillary services, namely frequency and voltage control of a microgrid. The TCL population is aggregated as a function of the ambient temperature, voltage and frequency using a neural network-based approach. The proposed EMS comprises primary and secondary control levels. TCLs’ participation in the primary control is selected to be semi-autonomous in order to obtain a fast response with low communication burden. The secondary control is centralized and aims to find the optimal dispatch of regulating resources including TCL clusters in order to minimize frequency and voltage deviations as well as grid operation cost. The secondary control relies on a Trust-Region Power Flow (PF)-based multi-objective optimization. Simulation results demonstrate that the participation of TCLs in the proposed EMS does not compromise customer comfort while short cycling and the number of switching events are minimized. [ABSTRACT FROM AUTHOR]

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability, reduce costs, and support increased hosting of distributed renewable generation. This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling. We consider services delivered to (1) the high-voltage bulk power system through the coordinated action of many, distributed building loads working together, and (2) targeted support provided to the operation of low-voltage electric distribution grids. Although an exhaustive exploration is not possible, we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling (BEM) tools, suggesting pathways for future research and development. [ABSTRACT FROM AUTHOR]

Due to their heat/cool storage characteristics, thermostatically controlled loads (TCLs) play an important role in demand response programmers. However, 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 scheduling model. Finally, the proposed scheduling method is verified using the simulation results of a six-bus system.

Summary: The installation of an energy storage system (ESS) is vital for the Micrgorid (MG) islanded operation to ensure the maintenance of demand–supply balance. Lithium‐ion batteries (LIBs) are among the most commonly used ESS technologies for grid‐based applications, which is used in this paper to constantly maintain the demand–supply balance in a residential MG. For this purpose, a novel frequency‐based energy management scheme is proposed. It uses an LIB ESS to handle the primary frequency control and energy management during peak‐load period, while the dispatchable distributed generators like microturbine and fuel cell supply the base load. The air‐conditioning TCLs consume a significant part of the residential load profile, especially during mid‐summer. Unlike TCLs that instantaneously use their generated cooling thermal energy to control indoor temperatures, the thermal energy storage systems (TESSs) can also store the thermal energy and use it later. In this paper, the TESSs are used instead of TCLs to reduce the power consumption of a residential Microgrid (MG) in islanded mode. By doing this, the required LIBESSs capacity is decreased 70% considerably than the case the TCLs are used for controlling the indoor temperatures. Also, replacing the ACs and EHPs with TESSs results in a 63.7% reduction in the total cost of electrical and thermal storage. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This article derives a decentralized load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of variable length—desirable features for embedding in appliances and high-throughput simulations. Simulation results of large populations of heterogeneous appliances illustrate the accurate aggregate control of power consumption and high computational efficiency. Tracking accuracy is quantified as a function of population size and time step size, and correlations in the tracking error are investigated. The controller is shown to be robust to errors in model specification and to sudden perturbations in the form of random refrigerator door openings. [ABSTRACT FROM AUTHOR]

It can be foreseen that the modern electric power system tends to have an increasing penetration of renewable energy sources (RESs) and controllable loads under ancillary services programmes, which will cause the power system inertia reduction. For the thermostatically controlled loads (TCLs) (e.g. air conditioners) have large potentials for providing ancillary services for power systems and are more flexible and cost-efficient than traditional energy storage system, in this study, an approach of modelling power system virtual inertia (VI) by TCLs is proposed, while maintaining customers’ comfort level. Considering the TCLs’ heterogeneous characteristic and response uncertainty, a load tracking control strategy based on proportional–integral controller is proposed to preserve the performance of VI control strategy. Besides, the coefficient [inline-formula] is put forward for the first time to quantify and analyse the conversion efficiency between kinetic energy and thermal energy. Then, the method to design [inline-formula] based on the energy analysis is presented. Finally, some simulation results are provided to verify the effectiveness of the control scheme.

Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as thermostatically controlled loads (TCLs) can arbitrage energy prices and provide reserves due to their thermal energy storage capacity. The quantity of reserves depends not only on the aggregate power capacity, but also on information and communication technology, exogenous parameters, and system operator requirements. Specifically, the practical limitations origin from (i) communication constraints, (ii) ambient temperature, and (iii) the dispatch time of the activation signal. This study explores the impact of these parameters on the amount of reserves that an aggregator of TCLs can provide to the system operator based on centralised control of a TCL population. A decision support tool is proposed that can be used by aggregators to decide on maximum dispatchable reserve bids. The method can accommodate the specific control algorithm and TCL population of an aggregator and is based on offline computation. It constitutes a powerful reserve bid library to be used when optimisation tools become computationally intractable due to the increased number of decentralised flexible loads. [ABSTRACT FROM AUTHOR]

Demand response (DR) programs aim to engage distributed demand-side resources in providing ancillary services for electric power systems. Previously, aggregated thermostatically controlled loads (TCLs) have been demonstrated as a technically viable and economically valuable provider of such services that can effectively compete with conventional generation resources in reducing load peaks and smoothing demand fluctuations. Yet, to provide these services at scale, a large number of TCLs must be accurately aggregated and operated in sync. This paper describes a Markov Decision Process (MDP) that aggregates and models an ensemble of TCLs. Using the MDP framework, we propose to internalize the exogenous uncertain dynamics of TCLs by means of stochastic and distributionally robust optimization. First, under mild assumptions on the underlying uncertainty, we derive analytical stochastic and distributionally robust control policies for dispatching a given TCL ensemble. Second, we further relax these mild assumptions to allow for a more delicate treatment of uncertainty, which leads to distributionally robust MDP formulations with moment- and Wasserstein-based ambiguity sets that can be efficiently solved numerically. The case study compares the analytical and numerical control policies using a simulated ensemble of 1,000 air conditioners. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non-generating resources such as thermostatically controlled loads (TCLs) can arbitrage energy prices and provide balancing reserves when aggregated due to their thermal energy storage capacity. An aggregator that wants to quantify this flexibility in order to place optimal bids before gate closure, would be affected by market timing. The market timing can be quantified by parameters such as lead time and contract period. This paper explores the impact of market timing on TCL aggregate power consumption and reserve capacity bids and quantifies trade-offs between market timing and flexibility. We first optimize the power consumption and reserve capacity offers at given lead times and contract periods, varying from 24 hours ahead to real-time. We then introduce uncertainty in prices and TCL availability, formulate a two-stage chance-constrained optimization problem of a risk-averse aggregator, implement it on a rolling horizon basis, and evaluate how the trade-offs change. The results show that shorter lead times and contract periods positively impact TCL profit as well as flexibility if the prediction horizon is sufficiently long. The proposed method can be used by aggregators to decide on optimal bids and incentive payments to consumers to reward flexibility. [ABSTRACT FROM AUTHOR]

Air conditioners (ACs) are an important resource for demand-response (DR) services in electricity networks and have motivated much research activity in recent years. Existing approaches to model and control aggregate AC demand largely focus on ensembles of fixed-speed (on/off) compressor technology. However, AC markets are dominated by variable-speed compressor technology, which is rapidly becoming widespread. This paper presents a mean-field, first-principle modelling formalism for the analysis and control of aggregate demand of heterogeneously distributed ensembles of variable-output compressor ACs (VOCACs). This formalism posits that the model structure of an individual VOCAC fitted with the mean values of the VOCAC parameters distributed in the ensemble can accurately represent the aggregate DR of the ensemble, in complete contrast with models for fixed-speed AC ensembles. The proposition is numerically validated in two prevalent DR applications of model predictive control (MPC): shaving peak demand and firming photovoltaic generation capacity. The proposed representative model is embedded in the MPC designs to predict the aggregate demand of a heterogeneously distributed ensemble of 1,000 VOCACs by centralised manipulation of temperature setpoints. The model is extended to enable DR by constraining power consumption, which is illustrated in an MPC implementation of the Australian/New Zealand Standard AS/NZS-4755 DR modes. [ABSTRACT FROM AUTHOR]

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A virtual battery model is a simple and general method to quantify aggregate flexibility from thermostatically controlled loads (TCLs), enabling grid operators to effectively coordinate a large number of flexible building loads with supply-side resources in power systems. Lockout controls are designed to avoid wear and tear resulting from short-cycling of hardware. The lock on/off time could significantly affect aggregate flexibility from TCLs to provide ancillary services and may even fail control algorithms designed without considering the lock time constraints. This paper focuses on flexibility estimation and control design for TCLs with lock time constraints to provide frequency regulation service. We first investigate the potential impacts of lock time on TCLs’ aggregate flexibility and control performance. Both control-dependent and control-independent power bounds are then derived, based on previous TCL switching operations and regulation signals, respectively. While the control-dependent method provides aggregate flexibility for a given control method, the control-independent method calculates the theoretical maximum of power bounds. Finally, two control algorithms are proposed to better distribute flexibility over time and thereby improve signal tracking performance. The proposed methods are illustrated and validated through simulations. [ABSTRACT FROM AUTHOR]

This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user’s information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model. [ABSTRACT FROM AUTHOR]

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators. [ABSTRACT FROM AUTHOR]

In this paper, thermostatically controlled loads (TCLs), e.g., air conditioners, are utilized to mitigate the photovoltaic and load variations in a building-microgrid community, so that the whole community can provide ancillary services, e.g., fulfilling the power dispatch order from the main grid. First, a distributed control scheme with a two-layer hierarchical communication graph is proposed. The control gains are selected based on a linear quadratic regulator approach to achieve optimal control performance. Then, an initial value updating method is developed so that the dispatch order and local regulation signals are converted into the initial conditions for distributed control. With the proposed control scheme, the building-microgrid community can provide ancillary services while fairly sharing power and customers’ comfort requirement among all participated TCLs. The effectiveness of this control scheme is validated on a building-microgrid community with a variety of test cases. [ABSTRACT FROM AUTHOR]

Building energy management (BEM) systems can shift the heating, ventilation, and air conditioning (HVAC) demand of buildings in summer using pre-cooling. The physical and operational constraints of buildings directly impact the operation of low voltage networks (LVNs). There is a gap in the literature on the extent of the electricity consumption and the voltage profiles of LVNs are influenced by different wall constructions of BEM-enabled buildings. In this paper, a joint electrical and thermodynamic model is used to control residential BEMs, taking into account the constraints of the LVN and the buildings. As a case study, the electricity demand of an IEEE LVN with 55 residential buildings are modeled using four common wall constructions: 1) timber clad; 2) brick veneer; 3) reverse brick veneer; and 4) double brick walls. The results show the buildings with higher access to thermal inertia from inside provide enough thermal storage capacity to shift the entire peak-time cooling demand to off-peak periods. Furthermore, the location of a building in LVN affects its HVAC load shifting, with houses farthest from the transformer starting pre-cooling earlier to avoid exceeding the voltage limit. This paper shows the advantage of the joint electrical and thermodynamic model in capturing both the dynamics of HVAC demand for buildings and the electrical dynamics of LVN. [ABSTRACT FROM AUTHOR]