Thermostatically Controlled Appliances Research Articles

Planning Smart Grid Functions in Residential Loads Using a Virtual Equivalent Battery Storage Unit

This article develops a method for planning smart grid functions (peak-demand management, direct load control, and demand response) for residential loads. The proposed planning method is developed based on modeling the thermal energy stored in thermostatically controlled appliances (TCAs) as energy charged into a virtual (fictitious) equivalent battery storage unit (VE-BSU) at the distribution transformer that feeds these TCAs. The thermal energy stored in TCAs can reduce their power demands during peak-demand times. These reductions in power demands of TCAs can be modeled as discharging energy from the VE-BSU. The energy charged and discharged by the VE-BSU can be used to plan smart grid functions to maximize storing thermal energy in TCAs during off-peak-demand times (short time horizons). This feature is due to the ability to store thermal energy in TCAs, and completely use it over short time intervals. The VE-BSU-based planning method is implemented and tested for residential loads fed by five different distribution transformers. Test results demonstrate the ability of the proposed method to plan effective and stable actions of smart grid functions, with minor sensitivity to the number of residential loads and/or seasonal variations in their power demands.

A multi-agent based optimization of residential and industrial demand response aggregators

Today’s power systems are subject to the high penetration of renewable power. Volatility and intermittency of the renewable power need to be compensated through alternative forms of flexibility. This paper proposes a novel agent-based structure to integrate the flexibility potential of industrial and residential demands. In this approach, a central demand response provider (DRP) is suggested to coordinate the responsive plans of industrial and residential demand response aggregators (IDRA, RDRA). The suggested IDRA integrates the flexibility potential of whole production lines for two energy-intensive heavy industries, i.e. cement manufacture and metal smelting. Besides, the RDRA uses the thermal and electrical storage capabilities of thermostatically-controlled appliances (TCAs) and electrical storage systems linked with roof-top photovoltaic (ESS-RPV) sites through home energy management systems (HEMS). The integrated flexibility is traded in the electricity market to maximize the profit of the market participants in a competitive environment, instead of subsidizing the responsive consumers by supportive regulations. Finally, the suggested structure is tested on the Danish sector of the Nordic Electricity Market to show applicability and proficiency of the proposed approach. The results show that the integrated flexibility can safeguard the future of power systems against the intermittent power.

A Two-Level Optimal Scheduling Strategy for Central Air-Conditioners Based on Metal Model with Comprehensive State-Queueing Control Models

Unlike some thermostatically controlled appliances (TCAs) with small capacities, Central Air-conditioner (CAC) has huge potential for demand response because of its large capacity. This paper presents a new CAC control strategy under multiple constraints. The CAC is modeled by three main modules: CAC central unit, water pumps, and temperature simulation of terminal users. The CAC’s power consumption is mainly determined by users’ load ratio. As the information and communication system have become the central nervous system of the smart grid, big data analysis is of great significance. Assuming that reliable two-way communication systems are preset, an integrated parameter priority list (IPPL) control strategy is used to control and monitor CAC. A new intelligent algorithm, Space Exploration and Unimodal Region Elimination (SEUMRE) algorithm, is introduced for solving the optimization problem of demand response targets generation under multiple constraints with the help of big data analysis. In this paper, influences and constrain factors, such as price and users’ comfortable levels are taken into account to satisfy the need of actual situation. Simulation results show that the proposed approach, when comparing with other typical optimization algorithms, yields better performances and efficiency.

A new demand response algorithm to maximize renewable energy usage for grid-connect microgrid

The uncertainties brought by wind and solar power make it challenging to utilize these clean energies efficiently in power grid. The traditional solution is to provide battery energy storage devices (BESS) to manage its fluctuant power energy. However, the high cost of BESS prevents it from being popularized. In this paper, the demand response (DR) technique is investigated to find an advisable way of using both wind power and solar power more efficiently. A new DR algorithm called weighting coefficient queuing (WCQ) algorithm is developed to manage activities of a kind of thermostatically controlled appliances (TCAs), e.g. air conditioner (AC). WCQ algorithm, composed of modified coloredpower algorithm and state-queuing (SQ) model, allows customers to set different colors as the response levels of their appliances. Such an algorithm is able to ensure both the comfort level and the fairness of customers. A microgrid with 1000 ACs is used to verify the effectiveness of the proposed algorithm.

Frequency Control Ancillary Service Provided by Efficient Power Plants Integrated in Queuing-Controlled Domestic Water Heaters

Frequency is an important parameter of a power system. It is of great significance to maintain its stability, especially in the current development scenario of large-scale interconnected power systems. Thermostatically controlled appliances (TCAs) are good controllable resources for demand response owing to their rapid response capabilities and relatively wide controllable ranges. In this study, domestic water heaters, which have wider deadbands compared with other typical TCAs, such as heat pumps, are used as frequency regulation resources. The main contribution of this paper is that it proposes a queuing-controlled strategy with lock-on and off constraints for controlling an efficient power plant consisting of water heaters (EPP-WH). The queuing-controlled strategy enables TCAs to provide frequency regulation ancillary service for the normal operation of the power system. The thermal dynamic process of the water heater and the formation of the EPP-WH are first discussed. Based on the developed model, a series of strategies are proposed, including load shedding calculation, top layer optimization, and improved temperature priority list (TPL) strategy with lock-on and off constraints. Finally, typical case studies are discussed to illustrate the frequency regulation effects and the effects of two characteristic parameters—users’ willingness and lock time limits. Reasonable targets are generated based on various consideration from top layer optimization module. The results indicate that using the model and proposed strategies, the EPP-WH has good frequency regulation performance.

Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid

Thermostatically controlled appliances (TCAs) have great thermal storage capability and are therefore excellent demand response (DR) resources to solve the problem of power fluctuation caused by renewable energy. Traditional centralized management is affected by communication quality severely and thus usually has poor real-time control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation (OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.

Model Predictive Control for Thermostatically Controlled Appliances Providing Balancing Service

This paper studies the modeling and control methods for the provision of ancillary services through aggregating thermostatically controlled appliances (TCAs). A model predictive control (MPC) scheme is presented. Minimum ON/OFF time for protecting the TCA unit is explicitly described in the MPC scheme. A novel method of converting time-integrated interdependent logic conditions, which are for constraining the lockout effect, into inequalities is proposed in this paper. A case study is conducted with realistic data to validate the feasibility of the modeling and control approaches. Parametric studies in terms of temperature deadband width, ambient temperature, and lockout time are conducted to investigate the service quality in different circumstances. The test results reveal that a population of diversified TCAs can become a major source of providing ancillary service. The proposed approach can be specialized to deal with the pure ON/OFF control.

Dynamic Frequency Regulation Method Based On Thermostatically Controlled Appliances in the Power System

This paper presents a dynamic frequency regulation strategy which uses residential thermostatically controlled appliances (TCA) to alleviate frequency deviations caused by high penetration of renewable energy sources in the power system. Heating, ventilating and air-conditioning (HVAC) load is used as an example of TCA load. With the control strategy, HVAC units respectively collect frequency information locally, based on which their on/off status is dynamically regulated. The simulation is implemented with 1000 HVAC units in a British Grid model. The results demonstrate that the strategy effectively alleviate the frequency deviations caused by renewable energy sources. A frequency response ability assessment method based on a dynamic load-sorting strategy is also presented.

Impacts of optimization interval on home energy scheduling for thermostatically controlled appliances

Home energy management systems (HEMS) have attracted much attention in recent years for energy efficiency and cost savings. Home energy scheduling is an important function of the HEMS, especially for thermostatically controlled appliances (TCAs). Optimization interval is a basic parameter in home energy scheduling, a topic that has been seldom studied before. This paper studies the impacts of optimization interval on home energy scheduling taking into consideration four scheduling strategies for TCAs. A tracking strategy is developed to arrive at a suboptimal solution while avoiding unacceptable solving time. The impact mechanism of optimization interval is analyzed. The optimization interval takes into account the scheduling ability of HEMS, flexibility of TCAs, the feasibility of scheduling, scheduling performances, user experiences, and model accuracy. The flexibility of TCAs, which depends on optimization interval, is defined and modeled. Two time division methods are proposed, namely, consistent interval division method (CIDM) and inconsistent interval division method (IIDM). Numerical simulation is carried out to verify the analysis. The results show that optimization interval impacts the flexibility, feasibility, and performance of scheduling. The proposed tracking strategy is seen as an effective method for HEMS.

Improving the Centralized Control of Thermostatically Controlled Appliances by Obtaining the Right Information

This letter presents a performance enhancement for a previously developed direct load control scheme that controls (toggles on/off) thermostatically controlled appliances (TCAs) through two-way communication infrastructure for providing load balancing services. In addition to device operating temperature and on/off status (TCA states), two more pieces of information, rates of temperature increase and decrease (RTI/RTD), are calculated at each TCA and communicated to the central controller. By communicating the RTI/RTD to the central controller, the same or even better control performance can be achieved at 2-3 times longer sampling periods, showing significant reduction of the communication needs from each TCA to the central controller.

Voltage stability enhancement using thermostatically controlled appliances as a comfort-constrained virtual generator

Conventional direct load‐shedding for achieving static voltage stability lacks considerations on both customer comfort and energy efficiency, resulting in higher cost and emission. A novel security‐based, optimal load‐shedding strategy considering customer comfort settings is presented in this paper. A temperature priority list method is used to model the virtual generator (VG) consisting of thermostatically controlled appliances (TCAs). To illustrate the control process and performance evaluation of the proposed load‐shedding scheme, a modified IEEE 6‐bus test system is used. Three heat, ventilation, and air‐conditioning (HVAC) groups are numerically simulated to respond to optimal load shedding signals. Reduced responsive load population, variations of temperature dead‐bands, and different outdoor temperature profiles are modeled to evaluate the capacity variations of the VG and its control performance. The results demonstrate that TCAs can provide satisfactory voltage stability.

Performance evaluation of controlling thermostatically controlled appliances as virtual generators using comfort‐constrained state‐queueing models

This study presents a detailed performance evaluation of comfort-constrained state-queueing model for integration of thermostatically controlled appliances (TCAs) as virtual generators (VGs). The configuration of VG models is presented. The heating mode of heating, ventilating and air-conditioning (HVAC) unit – heat pump is used as an example TCA load to demonstrate VG operation. First, an estimated baseline of the aggregated HVAC loads is obtained based on outdoor temperature forecasts using thermal dynamic models. Then, the control method of HVAC units for intra-hour load balancing is proposed. The effects of different comfort-constraints, outdoor temperature profiles and thermal deadbands are modelled to evaluate the performance of TCAs as VGs. The technical functionality of the TCA-VG is described and demonstrated by the modelling results.

Parameter Selection for a Centralized Thermostatically Controlled Appliances Load Controller Used for Intra-Hour Load Balancing

This paper presents a novel dynamic parameter selection process to optimize the performance of a centralized load controller designed to provide intra-hour load balancing services using thermostatically controlled appliances (TCAs). An optimal set of control parameters for the controller are selected by exhaustive simulations of control variables such as the sampling time of the forecaster, the magnitude of the load balancing signal, and the temperature deadband. The effects of TCA lock-off times, ambient temperatures, heat gains, and two-way communication delays on the controller design are also modeled. Customer comfort, device life cycles, and control errors are used as metrics to evaluate the performance. The results demonstrate that the optimized controller offers satisfactory performance considering all the operational uncertainties.

Passive energy storage using distributed electric loads with thermal storage

This paper presents the concept of controlling distributed electric loads with thermal energy storage as a passive electric energy storage system (PEESS). Examples of such loads include different types of thermostatically controlled appliances (TCAs) such as hot water heaters, air conditioners, and refrigerators. Each TCA can be viewed as a thermal cell that stores electricity as thermal energy. A centralized control mechanism can be used to control the timing of each thermal cell to consume electric energy so that the aggregated electricity consumption of the thermal cells will vary against a baseline consumption. Thus, when the aggregated consumption is higher than the baseline, the PEESS is charging; otherwise, the PEESS is discharging. The overall performance of a PEESS will be equivalent to that of a battery energy storage device. This paper presents the configuration and formulates the control of a PEESS. The modeling results demonstrate the feasibility of implementing the PEESS.

Design Considerations of a Centralized Load Controller Using Thermostatically Controlled Appliances for Continuous Regulation Reserves

This paper presents design considerations for a centralized load controller to control thermostatically controlled appliances (TCAs) for continuous regulation reserves (CRRs). The controller logics for setting up the baseline load, generating priority lists, issuing dispatch commands, and tuning the simplified forecaster model using measurement data are described. To study the impacts of different control parameter settings on control performance and device lifetimes, a system consisting of 1000 heating, ventilating, and air-conditioning (HVAC) units in their heating modes is modeled to provide a CRR 24 hours a day. Four cases are modeled to evaluate the impact of forecasting errors, minimum HVAC turn-off times, response delays, and consumer overrides. The results demonstrate that a centralized TCA load controller can provide robust, good quality CRRs with reduced communication needs for the two-way communication network and inexpensive load control devices. Most importantly, because the controller precisely controls the aggregated HVAC load shapes while maintaining load diversity, the controllable and measurable load services that it provides can be used for many other demand response applications, such as peak shaving, load shifting, and arbitrage.

Modeling Uncertainties in Aggregated Thermostatically Controlled Loads Using a State Queueing Model

To study the impacts of price responsive demand on the electric power system requires better load models. This paper discusses the modeling of uncertainties in aggregated thermostatically controlled loads using a state queueing (SQ) model. The cycling times of thermostatically controlled appliances (TCAs) vary with the TCA types and sizes, as well as the ambient temperatures. The random consumption of consumers, which shortens or prolongs a specific TCA cycling period, introduces another degree of uncertainty. By modifying the state transition matrix, these random factors can be taken into account in a discrete SQ model. The impacts of considering load diversity in the SQ model while simulating TCA setpoint response are also studied.

A State-Queueing Model of Thermostatically Controlled Appliances

This paper develops a state-queueing model to analyze the price response of aggregated loads consisting of thermostatically controlled appliances (TCAs). Assuming a perfectly diversified load before the price response, we show that TCA setpoint changes in response to the market price will result in a redistribution of TCAs in on/off states and therefore change the probabilities for a unit to reside in each state. A randomly distributed load can be partially synchronized and the aggregated diversity lost. The loss of the load diversity can then create unexpected dynamics in the aggregated load profile. Raising issues such as restoring load diversity and damping the peak loads are also addressed in this paper.