Thermal Inertia Of Buildings Research Articles

Evaluating the impact of virtual energy storage under air conditioning and building coupling on the performance of a grid-connected distributed energy system

Energy storage technologies are vital in improving the operation performance of grid-connected distributed energy systems. The adjustability of indoor temperature and the thermal inertia of buildings can form an excellent virtual energy storage. However, there are few studies on the impact of this virtual energy storage on the operation performance of grid-connected distributed energy systems. Therefore, this study first calculates the equivalent thermal resistance and thermal capacitance of a building by using EnergyPlus and establishes the first-order thermodynamic load calculation model. Subsequently, the system dynamic optimal scheduling model, considering the virtual energy storage, was developed based on the first-order thermodynamic load calculation model. The Gurobi nonconvex solver is used to optimize the model, and the results are compared with the optimal scheduling results without considering the virtual energy storage. The results indicate that, guided by time-of-use electricity pricing, the virtual energy storage effectively reduces the air conditioning load during high and peak tariff periods while increasing it during valley tariff periods. This change in air conditioning load leads to an increase in grid power consumption during valley tariff periods. However, it also improves the system's operation efficiency and ultimately reduces the total grid power and gas consumption. The lower energy consumption makes the primary energy saving rate and carbon dioxide emission reduction rate of Scenario 2 greater than zero compared to Scenario 1 and increases with the increase of renewable energy penetration of the integrated power system. Furthermore, affected by the difference in the proportion of grid power consumption under time-of-use electricity pricing and the total energy consumption, the system's operating cost in Scenario 2 is less than in Scenario 1. The reduction rates in summer and winter typical days are 1.95 % and 6.48 %, respectively. Therefore, fully utilizing the virtual energy storage under air conditioning and building coupling can reduce the operating cost, primary energy consumption, and carbon dioxide emissions of grid-connected distributed energy systems.

Economic operation of an agent-based virtual storage aggregated residential electric-heating loads in multiple electricity markets

The increasing penetration of intermittent renewable energy sources leads to a higher demand for flexible balancing service in the power system. Consequently, demand-side management techniques, such as utilizing flexible resources from residential electric-heating loads, provide a promising solution. However, due to various uncertainties such as stochastic weather and market prices, it is challenging to develop an economically efficient strategy. Hence, this paper proposes an economic operation optimization approach for residential electric-heating loads to develop optimal bidding and scheduling strategy in the multi-stage market. First, the dynamic characteristics of aggregated electric-heating loads are modeled as the virtual energy storage systems (VESS) to quantify the flexibility potential arising from the combination of building thermal inertia, user tolerance and thermal energy storage (TES). Subsequently, the joint energy and balancing service market is introduced to exploit the operating flexibility of VESS. A multi-agent deep reinforcement learning (MADRL) optimization framework is proposed to address the multi-objective problem at different decision frequency, which involves achieving lower economic costs while maintaining indoor comfort. Finally, we combine the DRL with imitation learning (IL) by introducing the behavior cloning mechanism to mitigate training process instability. It reduces the computational costs and eliminates the need for redundant additional structure. Comparative simulation experiments demonstrate the efficiency of data-driven optimization approach, which provides an adaptive, economic cost-effective and general solution for multi-type aggregators to activate their flexibility in dynamic markets.

Energy performance analysis of alternative building retrofit interventions for the four climatic zones of Greece

Climatic conditions determine which retrofit strategy can result in a satisfactory improvement of a building's thermal performance. This study systematically investigates alternative retrofit actions, as standalone and in combination, and assesses their energy performance. The retrofit actions include i) insulation on the external walls, ii) the incorporation of phase change material (PCM) on the roof and the increase of the building thermal inertia, iii) a mechanical ventilation system with a summer period operation, and iv) a cool roof coating. The energy analysis concerns a typical two-story family building and the four climatic categories of Greece and is conducted with the INTEMA.building tool, a dynamic energy analysis software developed in Modelica language. The importance of the present study is that it serves as a guideline for the selection of the energetically optimum single retrofit or multiple-retrofit strategy regarding a building's location climatic data, and provides calculations concerning the annual heating and cooling loads, and the corresponding heat pump electricity consumption. The retrofit actions of external wall insulation and PCM roof addition are characterized as more effective in reducing a building's heating energy loads, while the mechanical ventilation system and cool roof coating are more effective in reducing a building's cooling loads. The quadruple retrofit strategy results in the maximum electricity savings for Heraklion (28.01%), Athens (21.15%), and Thessaloniki (16.54%), while the triple retrofit strategy which does not include the cool roof coating, results in the maximum electricity savings for Kastoria (10.79%). Lastly, an economic analysis of the energetically optimum renovation strategies is conducted.

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales.

Multi-agent game operation of regional integrated energy system based on carbon emission flow

In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision-making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon.

Thermal modeling for control applications of 60,000 homes in North America using smart thermostat data

As smart thermostats become increasingly available in residential buildings, there is an opportunity to use measured building data to calibrate models for community and district applications, instead of relying on high-fidelity simulations. This study used smart thermostat data from 60,000 houses in North America to create single-zone models. The model structure was defined with an automated forward selection procedure. 61% of the final models were classified as good fits and the structure of 80% of them was of 5th-order (5 thermal capacitances). An investigation of the 24-hour prediction error of the models showed that the ones classified as good fits are accurate enough for day-ahead predictions and Model Predictive Control (MPC) applications. An analysis of the model parameters suggested no strong correlation between them and the available metadata. The time constants of the houses were estimated, providing valuable information about the houses thermal inertia. Building models that can accurately capture and leverage building thermal inertia are ideal candidates for MPC and energy flexibility applications.

Interpretable Load Patterns of Building District Energy Systems using Attention-based LSTM

With the increasing demand for energy and focus on environmental sustainability, district energy systems (DESs) have emerged as a promising solution. To optimize DES operations and energy savings, accurate load forecasting is crucial. This study proposed an LSTM model with an attention mechanism for accurate heating load forecasting within a real DES. By introducing an attention mechanism, the heatmaps generated by weight distribution can reveal the load pattern’s periodicity and building thermal inertia. Research on single buildings and district systems has shown that load forecasting with district systems is more stable regarding forecasting accuracy and load pattern extraction capability under irregular external disturbances. The outcomes illustrate the effectiveness of the proposed framework in accurately predicting heating loads and extracting interpretable load patterns. This can assist building managers in enhancing operational strategies, resulting in energy conservation.

The flexibility of virtual energy storage based on the thermal inertia of buildings in renewable energy communities: A techno-economic analysis and comparison with the electric battery solution

The Renewable Energy Community (REC) concept has been introduced into the European decarbonization guidelines to promote the utilization of Renewable Energy Sources (RES) and to incentivize their self-consumption at the local level. This paper analyzes the flexible use of Heat Pumps (HP) for building heating in an REC context. The Power-to-Heat (P2H) energy conversion process of HP allows the flexibility of the thermal sector to be exploited within the electricity sector: in this way, it is possible to store energy in the form of heat inside the building mass and then use the stored energy to reduce the building heating demand in the hours following the accumulation of energy. This energy storage solution has been defined as building-based Virtual Energy Storage (VES). The flexibility enabled by VES has been used to optimize the self-consumption of an REC. The flexible VES solution was evaluated, from a technical and economic point of view, through a sensitivity analysis on the variation of the RES penetration, and the results were compared with those based on a more traditional centralized electric battery (EB) storage system. The results demonstrated that the VES solution is less flexible than electric batteries. Nevertheless, both flexible solutions (VES and EB) can significantly increase the REC self-consumption: the self-consumed energy increased by between 6% and 44% thanks to the exploitation of the VES flexibility, while the EB flexibility enabled an increase in the self-consumed energy of 19% to 63% according to the scenario analyzed. However, due to the high investment cost of EB, the VES configuration resulted to be the best solution from an economic point of view.

Short-term cooling and heating loads forecasting of building district energy system based on data-driven models

Accurate forecasting of cooling and heating loads is critical for optimizing the energy usage of devices and planning for energy storage in building district energy systems (BDESs). Data-driven models offering high prediction accuracy and efficiency are receiving significant attention for addressing short-term load prediction problems. However, load forecasting for district building levels, as well as interpretable studies of deep learning models, are still the minority of existed research. To satisfy the cooling and heating loads forecasting of an actual BDES, a framework that includes raw data acquisition, data processing, model development, and evaluation is developed and analyzed. The performance of a multi-input-multi-output prediction strategy evaluated using five data-driven models, namely extreme gradient boost (XGB), long short-term memory (LSTM), gated recurrent units (GRU), and attention-added LSTM/GRU, is investigated. The results show that XGB performs the best, with CV-RMSE values of 14.51% and 11.95% for the one-hour-ahead forecasting of cooling and heating loads, respectively. In terms of 24-hour ahead prediction, the most accurate condition is “48–24” attention-added LSTM in both cooling and heating loads forecasting, where the CV-RMSE values recorded are 26.61% and 27.58%, respectively. The accuracy and interpretability of the models improved after an attention mechanism is introduced. The most significant enhancement in the prediction accuracy is reflected by a reduction in the CV-RMSE by 2.01%. The visualized heat maps of attention weights indicate the mechanism by which the models learn and extract the load patterns, where LSTM and GRU indicate different priorities. Further analysis of the attention weight distribution reveals the effects of building thermal inertia and load periodicity.

Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction

Accurate supply temperature prediction plays a vital role in achieving meticulous management of heating station. However, there are relatively few studies on ultra-short-term supply temperature prediction at present. This paper comprehensively applied 4 feature selection methods and 3 prediction algorithms to estimate hourly secondary supply temperature. Taking a floor radiant heating system as the case, the correlation analysis (CA) based on the back propagation neural network (BPNN) model and the support vector regression (SVR) model shows that outdoor temperature, supply and return temperatures are the main input feature categories. This paper novelty proposed the categorical principal component analysis (CPCA) method, compared with the traditional principal component analysis (PCA), this method can reduce the root mean square error (RMSE) of BPNN model and SVR model by an average of 18.6% and 19.7%, respectively. The comparison of 4 historical input lengths for the long and short-term memory (LSTM) model shows that historical 12 h can fully consider the influence of building thermal inertia and heating system thermal delay for floor radiant. Further comprehensive comparison shows that the BPNN model based on correlation analysis has the best performance.

Quantification and economic analysis of virtual energy storage caused by thermal inertia in buildings

The virtual energy storage caused by the thermal inertia of the building is the property and can participate in the demand response. However, the quantification of this virtual energy storage part is not clear. To determine the energy flexibility potential of building virtual energy storage, quantitative indicators such as charging and discharging time, and charging and discharging energy are proposed. The building model was created using the building energy simulation software EnergyPlus, and the quantification of each performance indicator of the building’s virtual energy storage was developed. Among them, the optimal charging time for this building model is 30 minutes, and the optimal discharging time is 60 minutes. Under the optimal virtual energy storage operation strategy, the charging energy is 57 kWh and the discharging energy is 84 kWh. Compared with the reference strategy, the building virtual energy storage operation strategy saves 48.76 yuan in single-day cooling electricity cost, with a 3% savings rate. The results show that building virtual energy storage has certain energy flexibility without additional investment and can generate certain economic benefits at the same time.

Improving room temperature stability and operation efficiency using a model predictive control method for a district heating station

In China, the regulation of a district heating (DH) station is mainly based on weather compensation control. This control method leads to large room temperature fluctuations and high energy consumption as the regulation of supply water temperature is solely based on outdoor meteorological parameters, which the influence of pipe delay, thermal inertia of buildings, and indoor temperature were not considered. In this study, a model predictive control (MPC) method was adopted to achieve high room temperature stability and high flexibility in DH station regulation. A dynamic model of the entire DH station, which considers the thermal inertia of buildings and delay of pipe, was established and verified using actual operation data. The performance of the MPC method was then evaluated and compared with that of the conventional control method (CCM) using the dynamic system model. When thermal comfort was considered as the single objective of the MPC, the optimal control step of the DH station was 8 h. The room temperature stability improved significantly as the room temperature fluctuation amplitude reduced from 2.64 °C to 0.35 °C. When thermal comfort and energy consumption were combined by the objective function, the MPC method could reduce the system energy consumption by 7.4 %. The MPC method could help improve the stability of the pipe network and had a strong anti-interference ability. In practical applications, during the two years after renovation, energy consumption was reduced by 5.9 % and 7.9 %, respectively. Further, the thermal comfort rate of households was improved by 0.27 % and 0.03 %, with relative incomes of 0.72 yuan/m2 and 1.04 yuan/m2, respectively.

Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

Optimal control of a fourth generation district heating network using an integrated non-linear model predictive controller

District heating (DH) can help increasing the share of renewable and residual energy sources in the heating sector. Usually a rule-based controller (RBC) is used for controlling these networks while more advanced control strategies like model predictive control (MPC) can support the transition to a decarbonised heating sector. In this paper, an integrated non-linear MPC approach for the control of DH networks is presented. The main novelties are the consideration of the system as a whole (including the heat demand side) and the inclusion of important non-linearities and all types of flexibility in the controller model of the MPC. A simulation-based methodology and dedicated solver are used in which the MPC is applied to an existing fourth generation DH network. The results of a three-day simulation in both a winter and spring period show that the MPC outperforms the currently used RBC: the thermal discomfort is lower in the winter period and comparable in the spring period while the electrical energy use of the DH system is reduced by 3% and 17%, respectively. The MPC achieves these results by lowering the network temperatures, using its anticipatory ability, and exploiting the flexibility of the building thermal inertia.

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Owing to potential regulation capacities from flexible resources in energy coupling, storage, and consumption links, central energy stations (CESs) can provide additional support to power distribution network (PDN) in case of power disruption. However, existing research has not explicitly revealed the emergency response of PDN with leveraging multiple CESs. This paper proposes a decentralized self-healing strategy of PDN to minimize the entire load loss, in which multi-area CESs' potentials including thermal storage and building thermal inertia, as well as the flexible topology of PDN, are reasonably exploited for service recovery. For sake of privacy preservation, the co-optimization of PDN and CESs is realized in a decentralized manner using adaptive alternating direction method of multipliers (ADMM). Furtherly, bilateral risk management with conditional value-at-risk (CVaR) for PDN and risk constraints for CESs is integrated to deal with uncertainties from outage duration. Case studies are conducted on a modified IEEE 33-bus PDN with multiple CESs. Numerical results illustrate that the proposed strategy can fully utilize the potentials of multi-area CESs for coordinated load restoration. The effectiveness of the performance and behaviors' adaptation against random risks is also validated.

Integrated energy system operation considering building thermal inertia and hydrogen storage systems

(1) An IES operation optimization system considering building thermal inertia and hydrogen energy storage systems is formed. (2) A hybrid particle swarm optimization-cuckoo search based on the beetle antennae search strategy (BPSO-CS) is proposed.

Novel effective room temperature-based predictive feedback control method for large-scale district heating substation

• A predictive feedback control strategy based on effective room temperature is proposed. • The feasibility of the method is verified using an existing district heating substation. • The proposed method is flexible and performs well in terms of anti-interference. • The proposed method can reduce thermal energy consumption by 6.1%. Conventional district heating (DH) substations in China use a feed-forward adjustment method that predicts the water supply temperature based on the outdoor temperature. This method does not consider the delay of the pipe network and the thermal inertia of the building. Most importantly, the lack of closed-loop feedback on the indoor temperature results in high energy consumption and large fluctuations in room temperature. In this study, a predictive feedback system based on effective temperature control has been proposed to improve the quality of district heating. First, a dynamic simulation control model of a DH substation was created based on measured data. Then, through dynamic simulation analysis, the influence of the heating network delay on the room temperature was observed to be smaller than the influence of the building's thermal inertia, which confirmed that predictive feedback control based on effective temperature could be achieved. Finally, the new control method was compared to two conventional control methods. Based on the comparison with the two conventional controls, refining the timespan of the feed-forward control was found to be ineffective for improving room temperature stability owing to thermal inertia. The new control method demonstrated high control flexibility and strong anti-interference, and the indoor temperature fluctuation decreased from ±1.1 °C to ±0.3 °C compared with conventional control methods. In this case, under the premise that thermal comfort is not affected, the room temperature setting can be reduced by 0.5 °C, resulting in an energy saving of 6.1%, with a payback return period of less than a year. This method only requires installing room temperature sensing equipment and linking the feedback control algorithm to the original control system and is therefore easy to achieve.

Demand-Response-Oriented Load Aggregation Scheduling Optimization Strategy for Inverter Air Conditioner

In recent years, the peak–valley differences in urban power loads have been increasing. It is difficult to maintain the real-time balance of a power system by relying solely on the generation-side resources. As a typical flexible load, an air conditioning load can balance the supply and demand of a power grid by adjusting power using the thermal inertia of buildings. From the perspective of a load aggregator, this study models and aggregates the dispatch of a single inverter air conditioner distributed in a region to determine the adjustment potential of an air conditioning cluster. Then, according to the demand response capacity requirements, an optimal strategy for the aggregate dispatch of an inverter air conditioner considering incentive compensation measures is proposed with the objective of maximizing the load quotient economic benefit. The sensitivity analysis of the compensation factor for temperature rise is also performed. The results show that 3000 inverter air conditioners in the load quotient dispatch area participate in the demand response for 4 h, with a load reduction of 1.267 MW and a net income of RMB 14,435.97. Secondly, an increase in the temperature rise compensation factor will reduce the cost of temperature rise compensation by the loader to the user, but it will also reduce the load reduction and the net income of the loader. This study has practical significance for load aggregators to formulate compensation strategies and improve the economic benefits of participating in demand response.

A Bi-level gaming programming for regional integrated energy system considering the users’ reliability incentive

In the context of the regional integrated energy system, as renewable energy increases and energy coupling gets higher, the operator has more resilience and flexibility to support reliability auxiliary service. To address energy reliability resource allocation, a novel method that user reliability incentive mechanism considering user reliability demand is proposed to help optimal decision making in a market vision. Thus, users’ demand for reliability differentiation in time and space dimensions will become prominent. Accordingly, we propose a master-followers bi-level gaming model with a reliability incentive mechanism. The reliability model is also innovated in the reliability constraint; the reliability reserve margin is adjusted in real-time considering the building thermal inertia and user's status. Considering user's reliability incentive, operator dispatches economically in the upper level and obtain the reliability index. With reliability improvement delivered by the upper level, users chase their maximum energy benefits by adjusting load responds on HVAC. This is considered by building thermal inertia and formulate reliability incentives based on improvement. A case study verifies the superiority of the proposed model on the participant's economy, users’ reliability, the renewable energy accommodation capacity, and differential reliability pricing. The sensitivity for external energy price and renewable energy permeability are also analyzed.

A Case Study of Mapping the Heating Storage Capacity in a Multifamily Building within a District Heating Network in Mid-Sweden

The building sector accounts for a third of the total energy use in Sweden, and district heating provides half of the heating needs. The peak demand loads within a district heating network occur both regularly and irregularly and impose a burden on the energy company to fulfill the demand, often by using more expensive and less environmentally friendly resources (e.g., fossil fuels) instead of the waste heat from industry or biofuels. Heat storage during hours of less demand and prior to colder periods can be used for load management and sustainable planning of energy supply, as well as reduction of total greenhouse gas emissions. Thus, heat supply to the building can be lowered temporarily during the peak power period to utilize the stored thermal energy within the building thermal inertia. The use of indoor temperature decay and the delivery of heating power to a multifamily building are studied here, and heating storage capacity and thermal inertia are calculated. During the performed decay test, the energy supply was estimated to be reduced by 61% for 5 h, which resulted in only a 0.3 °C temperature decay. Therefore, the suggested method can shave eventual peaks in supplied heat with minimal influence on the thermal comfort.