Thermostatically Controlled Loads Research Articles

Forecasting the price-response of a pool of buildings via homothetic inverse optimization

This paper focuses on the day-ahead forecasting of the aggregate power of a pool of smart buildings equipped with thermostatically-controlled loads. We first propose the modeling of the aggregate behavior of its power trajectory by using a geometric approach. Specifically, we assume that the aggregate power is a homothet of a prototype building, whose physical and technical parameters are chosen to be the mean of those in the pool. This allows us to preserve the building thermal dynamics of the pool. We then apply inverse optimization to estimate the homothetic parameters with bilevel programming. The lower level characterizes the price-response of the ensemble by a set of marginal utility curves and a homothet of the prototype building, which, in turn, are inferred in the upper-level problem. The upper level minimizes the mean absolute error over a training sample. This bilevel program is transformed into a regularized nonlinear problem that is initialized with the solution given by an efficient heuristic procedure. This heuristic consists in solving two linear programs and its solution is deemed a suitable proxy for the original bilevel problem. The results have been compared to state-of-the-art methodologies.

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.

A novel approach for sizing thermal and electrical energy storage systems for energy management of islanded residential microgrid

This paper proposes an energy management scheme (EMS) for islanded operation of a residential Microgrid (MG) using a Lithium-Ion battery (LIB) energy storage system (LIBESS) and distributed generators (DGs). The LIBESS handles primary frequency control and energy management during peak-load period while the dispatchable DGs supply the base load. During peak-load periods, the thermostatically controlled loads (TCLs) like air conditioners (ACs) make up a significant part of MG demand. A Thermal energy storages (TES) is quite similar to a TCL except that it can also store thermal energy for later use. Here, instead of using the ACs, it is proposed to use a TESS to provide the required thermal energy for keeping the inside temperatures of residences within the desired range. Instead of using the ACs, a pre-charged TES can be used to provide the needed cooling thermal energy during islanded operation that power generation capacity is insufficient. By not using the ACs, the total energy consumption of MG and the required LIB capacity for energy management decreases considerably. Various daily scenarios from available recorded data and randomly generated data are used to consider the uncertainties of the variations of the MG load profile, the ambient temperature and the renewable power generations. Also, the outage of the DG with the largest generation capacity is considered as the worst contingency that could ever happen during the islanded operation of MG. The LIBESS sizing results with and without the outage of the DG show that with the ACs being totally replaced with a TES, the overall cost of the TES and the LIBESS is decreased by 33% and 63% respectively than the case that the TES is not utilized.

Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling

District cooling systems (DCSs) belonging to multi-energy systems can be managed by model predictive controls (MPCs) designed to reduce the amount of electrical energy collected from the grid for backup cooling systems when there is a temporal mismatch between energy demand and availability. In this paper, a DCS recovering cold thermal energy from a liquid-to-compressed natural gas fuel station is used in an 8-user residential neighborhood to provide space cooling in summertime. In the residential neighborhood, there is a multi-energy system, including the DCS, photovoltaic panels, and backup systems based on variable-load air-to-water heat pumps. One user of the district was allowed to manage its energy demand with an MPC based on an artificial neural network (ANN). By integrating the ANN-based MPC routine in the building simulation environment and unlocking the energy flexibility of thermostatically controlled loads (TCLs) using variable setpoints, it was possible to reduce electrical energy consumption up to −71% with respect to a reference case with a rule-based control. This work highlights also the importance of the ANN training process for a proper representation of the TCL flexibility in the building model, which is not a trivial aspect to be taken into account in data driven models.

Energy Flexibility as Additional Energy Source in Multi-Energy Systems with District Cooling

The integration of multi-energy systems to meet the energy demand of buildings represents one of the most promising solutions for improving the energy performance of the sector. The energy flexibility provided by the building is paramount to allowing optimal management of the different available resources. The objective of this work is to highlight the effectiveness of exploiting building energy flexibility provided by thermostatically controlled loads (TCLs) in order to manage multi-energy systems (MES) through model predictive control (MPC), such that energy flexibility can be regarded as an additional energy source in MESs. Considering the growing demand for space cooling, a case study in which the MPC is used to satisfy the cooling demand of a reference building is tested. The multi-energy sources include electricity from the power grid and photovoltaic modules (both of which are used to feed a variable-load heat pump), and a district cooling network. To evaluate the varying contributions of energy flexibility in resource management, different objective functions—namely, the minimization of the withdrawal of energy from the grid, of the total energy cost and of the total primary energy consumption—are tested in the MPC. The results highlight that using energy flexibility as an additional energy source makes it possible to achieve improvements in the energy performance of an MES building based on the objective function implemented, i.e., a reduction of 53% for the use of electricity taken from the grid, a 43% cost reduction, and a 17% primary energy reduction. This paper also reflects on the impact that the individual optimization of a building with a multi-energy system could have on other users sharing the same energy sources.

Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation

Ensembles of thermostatically controlled loads (TCL) provide a significant demand response reserve for the system operator to balance power grids. However, this also results in the parasitic synchronization of individual devices within the ensemble leading to long post-demand-response oscillations in the integrated energy consumption of the ensemble. The synchronization is eventually destructed by fluctuations, thus leading to the (pre-demand response) steady state; however, this natural desynchronization, or relaxation to a statistically steady state, is too long. A resolution of this problem consists in measuring the ensemble’s instantaneous consumption and using it as a feedback to stochastic switching of the ensemble’s devices between on- and off-states. A simplified continuous-time model showed that carefully tuned nonlinear feedback results in a fast (super-) relaxation of the ensemble energy consumption. Since both state information and control signals are discrete, the actual TCL devices operation is space–time quantized, and this must be considered for realistic TCL ensemble modeling. Here, assuming that states are characterized by indoor temperature (quantifying comfort) and air conditioner regime (on, off), we construct a discrete model based on the probabilistic description of state transitions. We demonstrate that super-relaxation holds in such a more realistic setting, and that while it is stable against randomness in the stochastic matrix of the quantized model, it remains sensitive to the time discretization scheme. Aiming to achieve a balance between super-relaxation and customer’s comfort, we analyze the dependence of super-relaxation on details of the space–time quantization, and provide a simple analytical criterion to avoid undesirable oscillations in consumption.

Robust Distribution System Load Restoration With Time-Dependent Cold Load Pickup

Service restoration is one of the critical functions to enable the future self-healing distribution system. To restore the distribution system in a timely and reliable manner, the realistic system operating conditions need to be accurately characterized. In this paper, two main factors that have great impacts on distribution system restoration (DSR) in practice are investigated. First, cold load pickup (CLPU), generally caused by thermostatically controlled loads (TCLs), is a common phenomenon after an outage and shaped by the outage duration. However, the time-dependent behaviors of CLPU are rarely considered in literature. In this paper, the operating state evolution of TCLs after an outage is analyzed to characterize time-dependent CLPU. And the time-dependent CLPU is analytically embedded in DSR to accurately represent the actual behaviors of the restored loads. Second, it is difficult to predict loads that fluctuate during DSR due to the lack of real-time measurement data. Accordingly, a robust DSR based on the information gap decision theory (IGDT) is proposed to address this challenge, fully considering the uncertainty of CLPU. The proposed models are tested in IEEE 13-node and 123-node test feeders. Simulation results demonstrate that the time-dependent CLPU model and the uncertainty modeling of CLPU can accurately capture the actual behaviors of loads with TCLs after an outage, which greatly improves DSR decisions in practice.

Rolling horizon control architecture for distributed agents of thermostatically controlled loads enabling long-term grid-level ancillary services

This paper proposes novel modeling and control frameworks to evaluate the underlying capability of Thermostatically Controlled Loads (TCLs). The modeling architecture is based on distributed agents responsible for monitoring and controlling a small group of TCLs. Agents are mathematically formulated as a tracking problem in a mixed-integer linear programming format. Control actions are obtained by solving the agents sequentially in a rolling horizon control architecture. A step required to avoid the curse of dimensionality dictated by the number of devices and the duration of the planning horizon. The proposed architectures provide more practical evaluations in quantifying TCLs’ capability. In essence, individual devices are described by distinct parameters and different operating conditions imitating real-life devices found in actual distribution systems. In contrast to aggregate models developed in the literature, this work inherently allows devices’ heterogeneity, captures the outside temperature variations, and provides control access for both set-points and devices’ status.In this paper, the scope of TCLs evaluation is providing long-term services such as load-shifting and sustained load-reductions. Simulation results have shown that 1000 heterogeneous TCLs represented by air-conditioners are able to achieve both services. However, consequences on customers’ comfort are expected proportional to the extracted service. For instance, a sustained average power reduction of 319 KW causes an average set-point increase of 3.8 °F.

Distributed Control of Clustered Populations of Thermostatic Loads in Multi-Area Systems: A Mean Field Game Approach

Thermostatically controlled loads (TCLs) can effectively support network operation through their intrinsic flexibility and play a pivotal role in delivering cost effective decarbonization. This paper proposes a scalable distributed solution for the operation of large populations of TCLs providing frequency response and performing energy arbitrage. Each TCL is described as a price-responsive rational agent that participates in an integrated energy/frequency response market and schedules its operation in order to minimize its energy costs and maximize the revenues from frequency response provision. A mean field game formulation is used to implement a compact description of the interactions between typical power system characteristics and TCLs flexibility properties. In order to accommodate the heterogeneity of the thermostatic loads into the mean field equations, the whole population of TCLs is clustered into smaller subsets of devices with similar properties, using k-means clustering techniques. This framework is applied to a multi-area power system to study the impact of network congestions and of spatial variation of flexible resources in grids with large penetration of renewable generation sources. Numerical simulations on relevant case studies allow to explicitly quantify the effect of these factors on the value of TCLs flexibility and on the overall efficiency of the power system.

Incorporating Production Task Scheduling in Energy Management of an Industrial Microgrid: A Regret-Based Stochastic Programming Approach

In this paper, a regret-based risk-averse stochastic production task and energy management (PTEM) model for industrial microgrid is proposed, which is applied to a battery manufacturing plant (BMP) microgrid. We incorporate production task scheduling with production constraints in the energy management of industrial microgrid. The general PTEM of industrial microgrids is analyzed and the production processes and energy requirements of a BMP multi-energy microgrid prosumer are presented, in which thermostatically controlled loads (TCLs) in buildings are considered. Then, the regret-based risk-averse PTEM model is formulated as a mixed-integer linear programming (MILP) problem based on stochastic programming considering uncertainties of day-ahead electricity market price and photovoltaic (PV) power output. Conditional value-at-risk (CVaR) is introduced for risk control and utilized for minimizing the expected regret. Numerical studies are carried out to validate the proposed model by comparing with risk-neutral approach and deterministic model based on real market prices from PJM electricity market. The results show that the proposed PTEM approach is effectively risk-averse. In addition, the impacts of total target production task and risk-control parameter on expected cost and regret are analyzed.

Stochastic and Distributionally Robust Load Ensemble Control

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.

Domain Randomization for Demand Response of an Electric Water Heater

Thermostatically Controlled Loads (TCLs) provide a source of demand flexibility, and are often considered a good source for Demand Response (DR) applications. Due to their heterogeneity, and as such a lack of dynamics models, Reinforcement Learning (RL) is often used to exploit this flexibility. Unfortunately, RL requires exploratory interaction with the TCL, resulting in a period of potential discomfort for the users. We present an approach to reduce this exploratory time by pre-training the RL-agent. Domain randomization is used to facilitate knowledge transfer. We evaluate the pre-training potential in a DR energy arbitrage scenario with an Electric Water Heater (EWH). Our experiments show that a priori knowledge about EWH dynamics can be used to initialize and improve the control policy. In our experiments, pre-training attributes to 8.8% additional cost savings, compared to starting from scratch.

Analysis of synchronization in load ensembles

While coordinated control of a large population of electric loads can provide important services to the electric grid, situations have been observed where control of load ensembles may lead to highly nonlinear behavior such as synchronization, sustained oscillations and bifurcations. Synchronization of thermostatically controlled loads (TCLs) is undesirable since it can lead to increased short-cycling, sudden changes in power demand and network voltage issues. In this paper, we investigate the synchronizing tendency of TCLs under control strategies where updates are broadcast periodically for coordinating TCLs. We study the problem using a hybrid dynamical systems framework to model both the continuous and discrete dynamics of load ensembles. Analysis of eigenmodes of the underlying discrete-time system provide insights into synchronizing tendencies and rate of convergence to the synchronized state. Simulations are provided to illustrate the theory.

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

Aggregation and Optimal Management of TCLs for Frequency and Voltage Control of a Microgrid

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.

Special Issue on Grid-Interactive Efficient Buildings—Part 1

We are pleased to present starting with this volume of ASME Journal of Engineering for Sustainable Buildings and Cities (JESBC), a series of peer-reviewed papers related to some of ongoing research projects and applications of Grid-interactive Efficient Buildings (GEBs). As part of a new program initiated by the Building Technology Office (BTO) with the US Department of Energy (DOE), GEBs are characterized as highly energy-efficient buildings that are equipped with smart technologies and distributed energy resources that can be dynamically controlled to meet grid needs and minimize electricity system costs, while meeting occupants’ comfort and productivity requirements. In simple terms, DOE defines GEBs as efficient, connected, smart, and flexible buildings. The GEB technologies touch on the main topical areas of JESBC aimed at fostering sustainable buildings and cities by providing results of evaluation studies and demonstration projects. Indeed, one of the main goals for JESBC is to disseminate solutions to enhance the built environment energy efficiencies, low carbon services, inter-connectivity services, resilient multi-services, and healthy and comfortable living spaces.Buildings are responsible for the highest energy consumption among all the sectors globally and in the US where 40% of the national primary energy is attributed to the built environment. Moreover, electricity generation is shifting from fuels to distributed energy resources including photovoltaic (PV) systems in a global effort to reduce carbon emissions and transition to carbon-free energy. The shift to distributed and renewable energy resources results in an increase in non-dispatchable and intermittent power supply causing challenges especially for the grid operators to balance between the supply and demand. GEBs and associated technologies and operations offer options for utilities to maintain the desired energy supply-demand balance by adjusting the loads of buildings and communities to match continuously variable power supply resources. Through a series of reviews, DOE has outlined the potential benefits of GEBs as well as the challenges and the research gaps [1] for various building energy systems including building envelope elements [2], lighting and electrical systems [3], and heating, ventilating, and air conditioning (HVAC) equipment [4], as well as whole-building controls, sensors, modeling, and analytics [5].The GEB special issue JESBC covers a wide range of topics to enhance the sustainability, resiliency, and flexibility buildings energy systems including innovative technologies to enhance energy efficiency for various building components, building integrated renewable energy technologies, and advanced optimized control strategies in order to design and operate smart grid-interactive efficient buildings. In particular, the GEB special issue covering part of the JESBC August volume and entire November volume includes some of the latest analyses for a suite of technologies and applications for grid-connected buildings and communities. Some of technologies included in the special issue are dynamic insulation systems for building envelopes, thermostatically controlled loads (TCLs), fault detection and diagnostic (FDD) tools, building integrated fuel cells, and rooftop PV systems. These technologies and other proven energy efficiency strategies have been applied to design and operate high energy performance buildings including net-zero energy and carbon-neutral buildings and communities. The first paper included in this issue is entitled “Load Communication in Populations of Thermostatically Controlled Loads.” The authors in this paper introduce and evaluate a promising feedback-based method to modify each thermostat behavior based on the actions of neighboring TCLs to reduce unwanted oscillatory effects when applied to demand response events.In the second paper entitled “Bringing Automated Fault Detection and Diagnostic tools for HVAC&R into the Mainstream,” the authors perform an independent assessment of ten commercially available FDD tools installed to detect faults for roof top units (RTUs) in ten different sites. The sites include different building types ranging from an office, a restaurant, and a school. The paper reports on the first phase to evaluate the performance of the implemented FDD tools using an independent monitoring system based on comprehensive set of sensors used to verify the validity of the identified faults.The November volume of JESBC will be fully dedicated for additional studies and evaluations targeting GEB technologies and applications. So stay tuned!

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.

Quantifying flexibility of load aggregations: impact of communication constraints on reserve capacity

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.

Aggregate Power Control of Heterogeneous TCL Populations Governed by Fokker–Planck Equations

This paper addresses the modeling and control of heterogeneous populations of thermostatically controlled loads (TCLs) operated by model predictive control (MPC) schemes at level of each TCL. It is shown that the dynamics of such TCLs populations can be described by a pair of Fokker-Planck equations coupled via the actions in the domain. The technique of input-output feedback linearization is used in the design of aggregate power control, which leads to a nonlinear system in closed loop. Well-posedness analysis is carried out to validate the developed control scheme. The closed-loop stability of the system is assessed by a rigorous analysis. A simulation study is contacted, and the obtained results confirm the validity and the effectiveness of the proposed approach.

Power system virtual inertia implemented by thermostatically controlled loads

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 γ 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 k v i based on the energy analysis is presented. Finally, some simulation results are provided to verify the effectiveness of the control scheme.