Thermostatically Controlled Loads Research Articles

Provision of multiple services with controllable loads as multi-area thermal energy storage

Power systems are experiencing a decrease of synchronous generation along with increased penetration of inverter based renewable generation leading to reduced system inertia and a need for flexible resources. Non-generating resources such as thermostatically controlled loads (TCLs) are flexible due to their thermal energy storage capacity. When aggregated, TCLs can arbitrage energy prices and provide reserves to the power system. We approach the operational flexibility of the TCLs by modeling a risk-averse aggregator that controls decentralized TCLs and aims to maximize its own profit. The high number and low power rating of residential TCLs makes it difficult to model and assess their flexibility potential on national level. Thus, we make use of a high-level thermal energy storage model for aggregations of TCLs to quantify their flexibility potential. We present a method to aggregate temperature, TCL parameters, and building stock data into a thermal battery equivalent. We propose a multi-period multi-market multi-zonal two-stage chance constrained rolling horizon optimization problem formulation for the risk-averse day-ahead self-scheduling problem of a price-taker TCL aggregator bidding in energy and reserve markets under uncertainty and recast the problem as a linear program. We perform several case studies in the Swedish power system based on a survey of single- and two-family dwellings with electric heating and assess the flexibility potential. Additionally, a sensitivity analysis provides insights regarding market design and policy implications.

High resolution synthetic residential energy use profiles for the United States

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.

An ensemble learning model for estimating the virtual energy storage capacity of aggregated air-conditioners

Renewable energy resources (RES) pose several challenges due to their natural intermittency when integrated into a distribution network. A smart energy storage system (SESS) alleviates these challenges, which is achieved by integrating thermostatically controlled loads (TCLs) such as air conditioners (ACs). Hence, this study proposes a virtual energy storage system (VESS) by modeling the ACs analogous to an electro-chemical battery. Besides, non-linearity in energy consumption patterns of ACs due to consumer behavior makes predictive energy analysis critical to deplete the discrepancy between supply and demand. Therefore, this study proposes an ensemble learning (EL) technique to predict the virtual energy storage (VES) capacity of ACs. The prediction stage relies on various machine learning (ML) methods, like support vector regression (SVR) and artificial neural network (ANN). Based on these ML techniques, this study proposes an SVRs-ANN-based EL model to predict the VES capacity during the discharging cycle. Each weak learner in the EL model is trained using a significant features subset with the aid of the k-fold cross-validation method to build a more generalized model for unseen data. The performance of the EL model is estimated using minute-based smart meter data set and empirically manifested that the EL model outperforms conventional approaches. The R2 and root mean squared error (RMSE) of the EL model reach 1 and 1.03×10−7, respectively, and results demonstrate lower RMSE values and higher performance than conventional methods. The results obtained have shown the robustness of the proposed EL model to operate ACs in energy-saving mode instead of load shifting. Although ACs are not controlled in the experiments, the experimental validation proved that the EL model accurately predicts the daily VES capacity of ACs without violating consumer satisfaction.

Aggregate Flexibility Capacity of TCLs With Cycling Constraints

Thermostatically Controlled Loads (TCLs), e.g., an air conditioner, typically maintain their temperature within a preset range using on/off actuation. These types of loads are inherently flexible: many different power consumption trajectories exist that can keep the temperature within range. Grid operators need tools for quantifying demand flexibility of a collection of TCLs in order to use them as a resource. However, computationally tractable characterization of flexibility capacity obtained so far has considered temperature constraints alone, ignoring their cycling/lock-out constraints: the length of time a TCL must stay in “on” stage before switching to “off,” or vice versa. In this work, we present a tractable characterization of the flexibility capacity of a collection of TCLs that incorporates not only temperature but also cycling and total energy consumption constraints. Unlike prior attempts at capacity characterizations incorporating cycling constraints, our results are independent of the algorithm used to coordinate the TCLs. The characterization leads to a set of convex constraints. A grid operator can use this characterization to compute a power consumption trajectory for an ensemble of TCLs that comes closest to what the operator needs to maintain demand-supply balance. Numerical results are provided to showcase the effectiveness of the proposed characterization.

Distribution locational marginal price based hierarchical scheduling framework for grid flexibility from virtual energy storage systems

• VESS's are modeled using residential air-conditioners for DSM. • Local ancillary services TSO-DSO coordination scheme for cooperative management in grids. • Hierarchical framework for scheduling aggregated VESS. • Network constrained optimal VESS participation in ancillary service markets. • DSM based practical market model for SOs, maximizing overall market benefits. Maintaining generation and demand equilibrium under large scale renewable energy integration is challenging. Conventional flexibility providers such as grid-scale Energy Storage Systems (ESS) have potential but are costly solutions. With evolving smart grid scenario, aggregated demand response from Thermostatically Controlled Loads (TCLs) is utilized as Virtual Energy Storage Systems (VESS) and considered as innovative and cost efficient solution to provide fast response reserves. VESS units are often scheduled and controlled in a centralized manner considering limited distribution system constraints. However, accurate operational flexibility potential of diversified VESS units for various grid services needs to consider distribution network constraints. It can be fully utilized when Distribution System Operators (DSO) coordinate with Transmission System Operators (TSO) to transfer grid services from VESS of its area. Local ancillary service TSO-DSO coordination scheme ensures in this perspective significant flexibility of TSO and allows both TSO and DSO to access VESS flexibility. For such controlling of diversified VESS units where DSO is responsible for aggregate flexibility and local area balancing, a hierarchical scheduling framework is more practical. In this context, this paper proposes a two-stage hierarchical approach for optimal scheduling of TCLs as VESS to optimize energy and provide grid flexibility. At the lower stage, TCLs are scheduled based on Distribution Locational Marginal Price (DLMP) provided by DSO, while ensuring consumers’ comfort. At the upper level, DSO provides additional flexibility to TSO through optimal scheduling of aggregated VESS considering local ancillary services TSO-DSO coordination scheme. The simulation results indicate that VESS scheduling improves system flexibility by maintaining a generation-demand balance.

A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin

With the growing percentage of the intermittent renewable power generation, the energy system is under increasing pressure in balancing the supply and the demand. As a major part of urban energy consumptions, buildings can provide considerable regulation flexibility for the energy system by actively managing their energy demands. For the building energy flexibility (BEF) provided by thermostatically controlled loads (TCL), its dispatch performance is vulnerable to the building thermal parameter errors, and in some cases, occupants need to provide the critical information related to the indoor temperature state and the occupancy state to the energy management system outside buildings, which decreases the availability of the BEF and raises privacy concerns. For these issues in the BEF utilization, this paper proposes a data-model fusion dispatch strategy based on the digital twin (DT). The proposed strategy is capable of parameter fault tolerance and privacy protection by combining the model-free advantage of the data-driven method with the analytical optimization advantage of the model-driven method. Firstly, a DT-based BEF dispatch framework is proposed. Secondly, the building DT is established by combining the building thermal dynamics (BTD) data-driven model and the TCL operation mechanism model. And the building response deduction is carried out based on the DT. Finally, under the rolling optimization framework, the data-model fusion dispatch strategy is devised by uniting the DT deduction and the optimization constructed by the BTD mechanism model, in which the multi-dimensional modeling of the BTD is carried out from the state dimension and the energy dimension. The simulation results show that the optimization result can reach 98.4% of the ideal result under the scenario with 15% parameter random error, and 98.3% of the ideal result under the scenario with 15% random state noise injection.

Electricity market clearing considering demand response cost of thermostatically controlled loads

• According to the analysis the equivalent energy storage (EES) model of the aggregated thermostatically controlled loads (TCLs), non-monotonic characteristics of the TCLs’ cost curve is discovered. • Due to the non-monotonic characteristics of the marginal cost, the proposed non-monotonic step-wise bidding model by the TCLs aggregators can reflect the actual cost when participating in power system scheduling. • Based on the bidding model, a novel electricity market clearing method considering TCLs is proposed, so that accurate market clearing can be guaranteed. Among various kinds of flexible loads resources in power system, the thermostatically controlled loads are considered as the excellent candidates for the demand response programs due to the heating/cooling storage characteristics. Market-orientated demand-side management has become a new focus, and the participation of the thermostatically controlled loads in the electricity market requires an accurate cost model. In this paper, a cost model is established based on the response characteristics of the thermostatically controlled loads in day-ahead scheduling, and considering the impact of non-monotonic bidding for thermostatically controlled loads on market clearing is considered, then the augmented constraints of the thermostatically controlled loads bidding are introduced into the traditional clearing method. By this way, proper sequence of bid quantity of each segment and the economic revenue can be guaranteed. The numeral testing results verify the effectiveness of the proposed method.

Data-Driven Modeling of Aggregate Flexibility Under Uncertain and Non-Convex Device Models

Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into wholesale energy markets. To ease market clearing, system operators allow aggregators to submit bidding models of simple prespecified polytopic shapes. Aggregators need to carefully design and commit to a polytope that best captures their energy flexibility along a day-ahead scheduling horizon. This work puts forth a model-informed data-based optimal flexibility design for aggregators, which deals with the time-coupled, uncertain, and non-convex models of individual loads. The proposed solution first generates efficiently a labeled dataset of (in)-feasible aggregation schedules. The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset. The ellipsoid is subsequently inner approximated by a polytope. Using Farkas’ lemma, the obtained polytope is finally inner approximated by the polytopic shape dictated by the market. Numerical tests show the effectiveness of the proposed flexibility design framework for designing the feasible sets of small- and large-sized aggregators coordinating solar photovoltaics, thermostatically-controlled loads, batteries, and electric vehicles. The tests further demonstrate that it is crucial for the aggregator to consider time-coupling and uncertainties in optimal flexibility design.

Frequency Regulation With Thermostatically Controlled Loads: Aggregation of Dynamics and Synchronization

Thermostatically controlled loads (TCLs) can provide ancillary services to the power network by aiding existing frequency-control mechanisms. TCLs are, however, characterized by an intrinsic limit cycle behavior, which raises the risk that these could synchronize when coupled with the frequency dynamics of the power grid, i.e., simultaneously switch, inducing persistent and possibly catastrophic power oscillations. To address this problem, schemes with a randomized response time in their control policy have been proposed in the literature. However, such schemes introduce delays in the response of TCLs to frequency feedback that may limit their ability to provide fast support at urgencies. In this article, we present a deterministic control mechanism for TCLs such that those switch when prescribed frequency thresholds are exceeded in order to provide ancillary services to the power network. For the considered scheme, we provide analytic conditions, which ensure that synchronization is avoided. In particular, we show that as the number of loads tends to infinity, there exist arbitrarily long time intervals where the frequency deviations are arbitrarily small. Our analytical results are verified with simulations on the Northeast Power Coordinating Council 140-bus system, which demonstrate that the proposed scheme offers improved frequency response compared with existing implementations.

Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads

Thermostatically controlled loads (TCLs) are viewed as a promising demand-side resource for smoothing out the intermittency of renewable energy. However, it is difficult to dispatch these dispersed flexibility resources to serve the microgrid without establishing the aggregation model of TCLs. Motivated by this need, this paper presents a distributed and real-time economic dispatch strategy based on the alternating direction multiplier method, that allows TCLs and distributed generations to work in coordination to maintain the power balance of islanded microgrids under fluctuating renewable energy sources, while considering the occupant comfort and the fair utilization. A semi-online parameter identification method that does not involve occupant privacy is proposed for obtaining parameters of inverter-based TCLs. This method is based on recursive least squares and designed with a changing forgetting factor to avoid data saturation. Also, a virtual battery model considering parameter heterogeneity is established for TCLs, and the scheduling capability of the TCLs cluster under different ambient temperatures is determined using deep neural networks. Besides, this paper introduces a fast distributed consensus algorithm without changing the communication structure to improve the convergence speed of the distributed algorithm. The effectiveness of the proposed strategy is validated on an islanded microgrid with 100 TCLs.

A bottom-up intra-hour proactive scheduling of thermal appliances for household peak avoiding based on model predictive control

Residential-level peak shaving is beneficial for the supply–demand balance of buildings with a limited power capacity of renewable energy sources like building-integrated photovoltaics. Thermostatically controlled loads (TCLs) are recognized as flexible resources for peak shaving. In practice, most thermal appliances are controlled by local on–off controllers without any communication and coordination. Multiple appliances may operate simultaneously and produce load peaks. Existing studies assume the existence of an electricity market and time-varying tariffs, which are not always available. This paper developed a model predictive control (MPC) based bottom-up proactive method to leverage the thermal inertia of residential thermal appliances for avoiding household peak load without relying on time-varying tariffs. First, we used a physics-based modeling approach to model the thermal dynamics of several residential thermal devices. Then, we designed a centralized MPC controller to minimize the appliance concurrent operations considering occupant comfort zones and appliance rate powers. Consequently, the house peak load is reduced by minimizing concurrent operations. Simulations over homogenous and heterogenous appliance set validated that the MPC-based proactive scheduling method can effectively reduce household peak load without compromising user comfort too much. Finally, we discussed the impact of control interval, appliance rated powers, and penalty factors on peak avoiding and the computational feasibility of the MPC controller. We concluded that the MPC-based proactive scheduling is feasible and effective for a limited number of thermal appliances with short horizons (i.e., six-step). Replacing a mixed-integer nonlinear program solver with a mixed-integer quadratic program solver could significantly reduce the computational burden. However, the application of the MPC controller to multiple households at the system level is still impractical. Further research efforts are emphasized, like mining and forecasting occupant demand and appliance usage patterns in households and designing scalable control strategies for system-level peak shaving.

Data-driven energy management in residential areas leveraging demand response

A distributed data-driven coordinated design is proposed to achieve efficient energy management of a residential grid, where controllable distributed resources such as electric vehicles (EVs) and thermostatically controlled loads (TCLs) are adjusted by balancing the end-use electricity cost, charging preference, and thermal comfort. The motivation for the control pattern is to minimize the total system cost by directly utilizing the measured input–output data instead of intractable model identification and state estimation. Firstly, we formulate a data based optimization problem with persistently exciting data sets and show the equivalence with the model-based problem. To protect the privacy of each consumer, we design a distributed pattern by the gradient of the augmented Lagrangian such that TCLs and EVs implement demand response individually. Moreover, the proposed algorithm is enhanced by a receding control scheme to tackle the uncertainties in the predictions. Standard test systems are used to illustrate the proposed design and demonstrate its effectiveness and benefits in the residential community.

Energy priority control strategy and reward allocation mechanism for thermostatically controlled loads based on leaky storage model

The aggregated thermostatically controlled loads (TCLs) can be used as energy storage to provide various ancillary services for smart grid. This paper presents an energy priority control method and a reward allocation mechanism, which aims to avoid TCLs always concentrating on the temperature boundaries and encourage users to improve adjustment capacity. Firstly, the energy to be charged and the residual energy are determined based on leaky storage model. Then when loads need to be turned on, the loads in the off state will be sorted in descending order according to the energy to be charged. Conversely, when loads need to be turned off, the loads in the on state will be sorted in descending order according to the residual energy. In order to encourage users to increase the temperature adjustment range, the energy provision index (EPI) is defined based on the energy capacity of loads. The simulation results show that this method can improve the tracking accuracy from 4.34% to 0.13% and reduce the switching frequency from 7.1 times per hour to 5.7 times per hour. In addition, with the increase of EPI, the reward of each load will increase, which indicates that this mechanism can improve the regulation ability.

Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

The load profiles of most commercial and industrial consumers are characterized by brief periods of very high power consumption followed by intervals of lower demand. To encourage such consumers to flatten their load profiles, power utilities around the world often levy a monthly demand charge (DC) on the peak demand measured over brief intervals. In this work, we consider the joint optimization of energy costs (EC) and the instantaneous peak power of a multi-unit building which uses a hydronic heating, ventilation and air-conditioning (HVAC) system and responds to a demand response (DR) program. Despite the apparent non-convexity of the control framework, we show how it may be transformed into a convex optimization problem. Next, we study the power demand patterns resulting from our proposed strategy for thermostatically controlled loads (TCLs), and evaluate the strategy’s performance for various climate zones in the US under both typical and atypical weather conditions. The results show that, depending on the ambient conditions and the tariff structure, our strategy can result in utility bill savings of up to nearly 19% compared to the baseline. The results also indicate that our power control strategy can significantly reduce the instantaneous peak power consumption in commercial TCLs.

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side.

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.

A Data-Driven Management Strategy of Electric Vehicles and Thermostatically Controlled Loads Based on Modified Generative Adversarial Network

As part of the transportation system electrification and decarbonization, electric vehicles (EVs) have been experiencing a steady growth in some countries in recent years. However, the uncoordinated charging of EVs brings negative effects to the power grid. Therefore, a new intelligent energy management strategy needs to be involved in the transportation electrification progress to address the variability of flexible loads and energy storage in the active distribution network. In this article, we have proposed a coordinated dispatch strategy of EVs and thermostatically controlled loads (TCLs) based on a modified generative adversarial network (GAN). TCLs are utilized to complement the limits of EVs’ driving behaviors. EVs are modeled as battery energy storage systems (BESSs), and TCLs are modeled as virtual energy storage systems (VESSs). Machine learning is integrated into a bilevel optimization problem to determine the steady-state power dispatch and the energy storage control of the VESS. The proposed method is verified on the IEEE 33-bus system. Based on the simulation results, it can be concluded that the proposed data-driven method can outperform the conventional model-based method in terms of accuracy. Also, the modified GAN helps the training process to be less affected by the missing data. Comparative studies are conducted to show that the coordinated dispatch strategy of EVs and TCLs can maintain the voltage stability in the distribution system by compensating for the drawbacks of each other.

Multi-quantile recurrent neural network for feeder-level probabilistic energy disaggregation considering roof-top solar energy

The purpose of feeder-level energy disaggregation is to decouple the net load measured at the feeder-head into various components. This technology is vital for power system utilities since increased visibility of controllable loads enables the realization of demand-side management strategies. However, energy disaggregation at the feeder level is difficult to realize since the high penetration of embedded generation masks the actual demand and different loads are highly aggregated. In this paper, the solar energy at the grid supply point is separated from the net load at first via either an unsupervised upscaling method or the supervised gradient boosting regression tree (GBRT) method. To deal with the uncertainty of the load components, the probabilistic energy disaggregation models based on multi-quantile recurrent neural network model (multi-quantile long short-term memory (MQ-LSTM) model and multi-quantile gated recurrent unit (MQ-GRU) model) are proposed to disaggregate the demand load into thermostatically controlled loads (TCLs), non-thermostatically controlled loads (non-TCLs), and non-controllable loads. A variety of relevant information, including feeder measurements, meteorological measurements, calendar information, is adopted as the input features of the model. Instead of providing point prediction, the probabilistic model estimates the conditional quantiles and provides prediction intervals. A comprehensive case study is implemented to compare the proposed model with other state-of-the-art models (multi-quantile convolutional neural network (MQ-CNN), quantile gradient boosting regression tree (Q-GBRT), Quantile Light gradient boosting machine (Q-LGB)) from training time, reliability, sharpness, and overall performance aspects. The result shows that the MQ-LSTM can estimate reliable and sharp Prediction Intervals for target load components. And it shows the best performance among all algorithms with the shortest training time. Finally, a transfer learning algorithm is proposed to overcome the difficulty to obtain enough training data, and the model is pre-trained via synthetic data generated from a public database and then tested on the local dataset. The result confirms that the proposed energy disaggregation model is transferable and can be applied to other feeders easily.

Demand response strategy for TCLs considering consumer utility

With the higher share of renewable energy, the flexibility of the power system needs to be improved. In recent years, the loads on the demand side can provide flexibility with the development of demand response technology. Thermostatically controlled loads (TCLs), as a kind of large-scale flexible load, have been a significant demand response resource. In this paper, a demand response strategy for TCLs considering consumer utility function is proposed. Firstly, a utility function is modeled to estimate the utility of TCLs. Then a model for estimating the potential of TCLs is built. On this foundation, the power system operators (PSOs) sort TCLs by consumer utility. TCLs with lower consumer utility will be called first. The study case adopts realistic data of an eastern city in China, which verifies the effectiveness of the proposed strategy.

The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution

With the urbanization and the decarbonization of the heating sector, thermostatically controlled loads (TCLs) with a rising energy consumption proportion, have become important demand response (DR) resources, which can provide considerable regulation flexibility for transition to renewable energy. Depending on the measurement or the forecast, TCLs dispatch methods established from the static perspective, failed to sufficiently consider the impact of DR chain reactions and the varying characteristics of TCLs response flexibility, which lead to the deterioration of the reliability and the availability of its dispatch potential. To address these issues, this paper proposes an interactive dispatch strategy based on the source–load collaborative evolution. Firstly, in light of system dynamics, a response evolution dynamic model is established by dividing of TCLs response process into multiple subsystems, which constitutes a comprehensive picture of TCLs response dynamics. Secondly, TCLs response flexibility is evaluated from the current and the evolution perspectives, which can enhance the reliability and stability of TCLs response by considering the potential response flexibility variations caused by the chain reaction of responses. Thirdly, an interactive dispatch strategy is devised by synthesizing of the source–load collaborative evolution dynamics from the dimensions of the elaborate state and the equivalent energy, which not only improves the dispatch performance, but also reduces the computational complexity. Based on the established strategy, TCLs response flexibility can be guided explicitly and quantitatively, like the energy storage in the dispatch, which improves the controllability and feasibility of TCLs response in DR service. Finally, the validity of the proposed strategy is verified via the comparison of it with the methods based on the static response mode.

A new method for energy management of residential microgrid for sizing electrical and thermal storage systems

This paper proposes a frequency-based energy management scheme (FEMS) 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. Considering that the thermostatically controlled loads (TCLs) make up a major part of MG load profile especially during peak-load period in summer. Unlike TCLs like ACs and EHPs that instantaneously use their generated cooling thermal energy, the Thermal Energy storages (TESs) can also store it and use it later. In this paper the Thermal Energy Storages (TESs) are used instead of TCLs like ACs and EHPs to supply the cooling thermal energy for maintaining the indoor temperatures of residences within comfort limits. The TESs can be charged to provide cooling thermal energy during in islanded operation with insufficient power generation capacity. By doing this, the total energy consumption of MG and the required LIB capacity for energy management decreases considerably. The results shows that with TCLs being totally replaced with TESs, the required LIB capacity is decreased 70 % than the case TCLs are not replaced with TESs.