Energy Flexible Buildings Research Articles

Demand Response Potential of an Educational Building Heated by a Hybrid Ground Source Heat Pump System

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time.

Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

Economy and energy flexibility optimization of the photovoltaic heat pump system with thermal energy storage

Photovoltaic systems have been widely applied in building energy systems. However, with the sharp increase in demand of electricity in buildings during heating and cooling seasons, the uncertainty of photovoltaic generation further exacerbates the peak-valley difference in electricity use. To improve the economy and energy flexibility of buildings in hot summer and cold winter zones of China, a rule-based operation strategy was proposed for the photovoltaic heat pump with thermal energy storage system to optimize the size of the thermal energy storage system and system operation, with aims of minimizing the total annual cost of the system and maximizing the self-consumption rate of the photovoltaic generation. The effects of grid export power limits, grid import power limits, feed-in tariffs, and PV generation on the system operation optimization results were also investigated. The results indicated that by integrating the thermal energy storage system into the photovoltaic heat pump system, the self-consumption rate of the photovoltaic generation was reduced by 2.39 %, the total annual cost of the system was decreased by 6.61 %, and the payback period of the thermal energy storage system was 1.31 years. The optimum size of the thermal energy storage system and the self-consumption rate of the photovoltaic generation decreased with the increasing grid export power limits and grid import power limits. In contrast, higher grid export power limits, grid import power limits, feed-in tariffs, and PV generation all resulted in a lower total annual cost. This study provided a systematic design and operation optimization method for building energy systems.

A probabilistic model for real-time quantification of building energy flexibility

Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.

Model predictive controls for residential buildings with heat pumps: Experimentally validated archetypes to simplify the large-scale application

Exploiting the energy flexibility resulting from thermal loads management in buildings is one of the most promising solutions to contribute to the energy transition targets. To offer energy flexibility to the grid, the building should meet the requirements: (i) it needs electrically powered generation (e.g., through Heat Pumps) and (ii) advanced control techniques (e.g., Model Predictive Controls) must be implemented. In recent years, the scientific community has produced many studies demonstrating the potential of Model Predictive Controls combined with Heat Pumps to exploit energy flexibility in buildings. However, a large-scale deployment of such control techniques is still far off, both because of the not yet widespread use of Heat Pumps and the computational challenges involved in implementing them. The aim of this study is to contribute to the deployment of advanced control techniques for Heat Pumps systems in buildings by simplifying their implementation. At this aim, validated archetypes of Model Predictive Controls for Heat Pumps in residential buildings are proposed. The availability of archetypes can greatly facilitate the practical application of Model Predictive Control. In fact, they are adaptable to the characteristics of different buildings and Heat Pump installations and their structure is designed to have an acceptable trade-off between calculation time and prediction reliability. The archetypes cover three different types of heating systems: low temperature radiators without (i) and with (ii) integrated heat storage devices and (iii) underfloor heating system. All archetypes are applied to a real Heat Pump and validated through experimental campaigns. During the experimental test all the archetypes proved to be effective in controlling the real system and the results showed good reliability of the prediction model in the control (for all archetypes a Root Mean Square Error lower than 0.44 °C was obtained) and an optimizer success rate in Model Predictive Controls greater than 91 %. The archetypes proposed are provided as open-source tools that can be reused for similar cases to facilitate Model Predictive Controls implementation in heat pump systems.

Intermittent demand-controlled ventilation for energy flexibility and indoor air quality

Buildings play a crucial role in ensuring power grid stability by providing energy flexibility. Existing research mainly improves the building energy flexibility through energy storage, but requires additional equipment costs. This study proposes a new source of building energy flexibility by properly managing indoor air quality with intermittent demand-controlled ventilation (iDCV), which requires no additional equipment investment. This study reveals the mechanisms of enhancing energy flexibility and develops a nonlinear programming algorithm to optimize the dynamic operation of the iDCV. The robustness of the proposed iDCV in enhancing energy flexibility is verified under different application scenarios in different climate zones. The results show that compared with the existing method, the proposed iDCV enhances the energy flexibility with a peak load reduction of 17.1 %–36.1 % under the scenario of constant electricity price and with a flexibility factor enhancement of 9.5 %–80.8 % under the scenario of time-of-use electricity price. Besides the benefit to the power grid with the enhanced energy flexibility, the proposed iDCV also delivers benefits to building users with energy savings of 19.6 %–22.0 % and cost savings of 21.5 %–26.1 %. Therefore, the proposed iDCV provides dual benefits for both the power grid and building users and contributes to developing energy-flexible, low-carbon, and healthy buildings.

Editorial: Energy-efficient and energy-flexible buildings towards net-zero carbon emission

Editorial: Energy-efficient and energy-flexible buildings towards net-zero carbon emission

Aggregation of building predictive energy flexibility in smart microgrid

Numerous demand-side resources possess significant potential to offer substantial flexible reserves and ancillary services, thereby enhancing the economic and technical performance of power systems within the demand response domain. A novel approach to aggregating the energy flexibility of buildings based on the virtual battery model is proposed in the smart microgrid, employing predictive control techniques while ensuring consumer privacy. The individual approximation process is implemented to extract the flexible capacity of local resources while protecting information privacy. Subsequently, the aggregator collects the flexibility information to develop an optimization strategy for maximum flexibility aggregation at the upper level, enabling the broader exploitation of global flexibility. The feasibility of power disaggregation is guaranteed according to the aggregation procedure. Furthermore, the comprehensive framework accommodates temporal preferences for flexibility requests from the aggregator. Numerical simulations are conducted to perform a comparative analysis with existing state-of-the-art methods. The results demonstrate that the proposed approach exhibits improved adaptability in characterizing the aggregate flexibility from buildings, effectively managing the heterogeneity of load characteristics and the variability in the collected flexibility information. Microgrids can realize cost savings and achieve dynamic flexibility utilization. This methodology can be extended and applied to diverse types of flexible resources.

Two‐stage demand‐side management in energy flexible residential buildings

AbstractIn this study, an optimisation model is developed for two‐stage energy management of a residential building to minimise energy cost under monthly power‐based tariffs for peak demand and time‐variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimizes energy costs during the second stage. The second stage's optimisation problem is solved in a rolling time window, facilitating real‐time operation of flexible energy sources in the building. This includes optimal charging and discharging of the battery energy system, electric vehicle battery charging, heating system operation, and determining the optimal start times for washing machines and dishwashers, all close to real‐time. The proposed approach enables users to predict and manage peak demand in daily operation, staying below the predetermined value through a close to real‐time energy management system. The effectiveness of this two‐stage approach in demand‐side management for residential buildings is demonstrated through a realistic case study.

Methods for Coordinating Optimization of Urban Building Clusters and District Energy Systems

In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits.

Comparison of time-invariant and adaptive linear grey-box models for model predictive control of residential buildings

Model predictive control (MPC) is a promising optimal control technique for activating building energy flexibility using its thermal mass. The performance of the MPC controller is directly related to the accuracy of the model prediction. Grey-box models, based on physical laws and calibrated on measurement data, are commonly used to represent the building thermal dynamics in MPC. Most research works use Linear Time-Invariant (LTI) grey-box models even though weather conditions vary significantly throughout the heating season. This is critical as inaccurate model prediction can lead to a lower performance of the MPC controller. This study introduces two adaptive MPC schemes to overcome this limitation of LTI models. The first one, called the Partially Adaptive MPC, only updates the effective window area of the prediction model. The second one, called the Fully Adaptive MPC, updates all the parameters of the grey-box model. The adaptive MPC performance is compared with MPC using LTI models in two different tests. The simulation-based results show that MPC based on LTI performs well if the control model is trained during a period with similar weather conditions as the period when the MPC will be applied. The Partially Adaptive MPC is unable to deliver satisfactory prediction performance due to the limited number of parameters that are updated. The Fully Adaptive MPC has the best performance compared to the other MPCs, especially as it avoids thermal comfort violations.

Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

Comparison of the parameters of a flat solar collector with a tubular collector to ensure energy flexibility in smart buildings

This study examines the application and implementation of energy-efficient measures and strategies for use in buildings aimed at achieving the goal of virtually zero energy consumption by buildings. In particular, the aim of the study is to introduce integrated solar devices into the building, functioning as a functional component of the structure for heat storage, in order to increase the flexibility of the structure while maintaining comfortable conditions inside. This article discusses an experimental comparison of flat and tubular solar collectors in the southern and northern regions of Kazakhstan. Compared to glass (S = 3), a tubular solar collector has a coefficient of thermal conductivity that is two times lower for single-layer elements and 2.5 times lower for double-layer elements. This means that less heat is lost. A flat solar collector was also developed, a heat-insulating translucent double-glazed window with reduced pressure, and the coolant is made of thin-walled corrugated stainless-steel pipe. In this collector, there is a constant circulation of heat, which increases the efficiency of heat transfer by eliminating additional partitions between the panel and thermal insulation. A calculation was carried out that allowed us to determine the productivity and useful gain for two collectors. The annual increase in the useful energy of flat solar collectors is about 12780000 kJ/hour and 20100000 kJ/hour for tubular solar collectors; therefore, the annual increase in the useful energy of evacuated tubular collectors is 20% more than that of flat plate collectors in the northern region.

Energy profiles and electricity flexibility potential in apartment buildings with electric vehicles – A Norwegian case study

Energy flexibility in buildings has the potential to reduce the grid burden of neighbourhoods, yet its practical implementation remains limited. This paper presents a data-based case study from Norway, examining the electricity flexibility potential of electric vehicles, within the context of apartment building loads and PV generation. The results highlight the significant electricity flexibility potential in apartment buildings with EVs, where EV charging can be shifted in time by means of a shared energy management system. Energy profiles are presented, showing how EV charging can increase the average electricity use in apartments by a factor of 1.5 and the power use by a factor of 3.5 to 8.6. Furthermore, the study demonstrates how electricity flexibility KPIs of optimised EV charging in apartment buildings are affected by different energy tariffs, PV generation, V2G technology, and the location of the billing meters. The simulated scenarios showed a maximum reduction of peak loads of 45 %, while a maximum of 38 % of the EV charging was covered by PV generation. The study confirms that residential EV charging emerges as a viable frontrunner in the practical realization of end-user flexibility, paving the way for effective solutions in real-life applications.

Energy, economic and environmental benefits of Demand Response for improving building energy flexibility

The increasing penetration of intermittent renewable sources in power generation at local and building-level poses growing issues in balancing generation and demand. To avoid imbalances, it is therefore necessary to ensure adequate levels of flexibility in the building energy system. This can be done both on the generation side, through the coupling of different energy carriers (cogeneration, power-to-heat solutions) and/or the integration of storage systems, and on the demand side, through smart “demand response” programs. This paper considers a tourist facility located in central Germany as a case study to evaluate the energy, economic and environmental benefits that can be obtained from the application of appropriate demand response strategies. The electrical demand data of the facility are monitored at both aggregate and individual load levels and made available by means of a cloud platform. The facility includes two stationary combined heat and power internal combustion engines powered by natural gas and a photovoltaic system. The results show how, thanks to appropriate load management, it is possible, on the one hand, to increase the self-consumption of PV-generated energy and, on the other hand, to keep more constant the load of the engines, which can therefore operate with better efficiencies. This results in both a reduction in energy expenses and a decrease in carbon dioxide emissions attributable to the building.

Model predictive control for demand response in all-electric school buildings

This paper presents predictive control strategies for all-electric school buildings in cold regions to activate energy flexibility based on changes in electricity prices. A fully electric school building near Montreal, Canada, is used as a case study. This study investigates three scenarios: 1) Reference case with a proportional–integral controller and flat rate electricity price, 2) Model predictive control with flat rate electricity price, and 3) Model predictive control with dynamic electricity price. These scenarios are modelled using the resistance-capacitance thermal networks model, and energy performance is determined and compared over a typical heating season. The proposed approach takes into account the physical parameters of the building, weather predictions, and thermal comfort constraints to maintain optimal energy consumption. A building energy flexibility index is used to quantify the building energy flexibility with a focus on peak demand reduction when the electricity prices are higher than usual. The results show that the MPC strategy can reduce peak power demand by up to 100% and minimize the cost of electricity during demand response events while maintaining acceptable comfort conditions.

Enhancing energy flexibility of building clusters via supervisory room temperature control: Quantification and evaluation of benefits

The electric grid faces increasing challenges in the context of electrification and renewable energy integration, gradually evolving into a smart grid. Buildings could facilitate this energy transition by providing flexibility services to the grid. Existing research on building energy flexibility has mostly focused on single buildings due to the difficulty of modelling buildings at the urban scale. This paper adopts an archetype-based approach to model generic building clusters, targeting various building types, compositions, and cluster sizes. We applied this approach to a case study cluster of 54 buildings. With the cluster model, we then characterized its operational flexibility associated with two rule-based room temperature controls. Simulation results show that the building cluster achieves 22–27% peak demand reduction on average against its regular operation throughout a series of flexibility events. We also evaluated the financial benefits of the flexibility service in an incentive program from a Canadian utility; an economic return of over $40,000 per winter was obtained for the building cluster. This work demonstrates the operational energy flexibility value in building clusters, benefiting not only the grid operator but also building owners.

E-DYCE - Dynamic approach to the dynamic energy certification of buildings

The energy performance certification (EPC) scheme, introduced in the European Union approximately 20 years ago, has become the focus of the upcoming revision of the Energy Performance of Buildings Directive (EPBD). Despite its widespread use, the current EPC scheme has several shortcomings that need to be addressed. The Energy flexible DYnamic building Certification (E-DYCE) project has developed a dynamic approach to address these issues. The methodology includes a dynamic assessment of a building’s energy needs and comfort conditions under standard and different from standard conditions of building use to support Performance Gap (PG) analyses. The E-DYCE approach includes a dynamic building performance simulation with comfort and energy-related key-performance indicators (KPIs) measured and calculated according to E-DYCE DEPC methodology. These KPIs can inform end-users about indoor environmental quality conditions decisive for building energy performance, aid building managers in detecting dysfunctions resulting in PG, and include energy performance indexes for heating, cooling, lighting, domestic hot water, and more. Overall, the E-DYCE approach offers dynamic, reliable, and customer-tailored information and optimization possibilities to end-users while potentially resolving known shortcomings of the existing EPC schemes.

Design optimization of energy systems for zero energy buildings based on grid-friendly interaction with smart grid

Zero-energy buildings usually achieve annual net-zero energy balance by generating electricity using on-site renewable energies and interconnecting with the power grid. However, the impact of energy mismatch problem in zero-energy buildings design on the grid is often overlooked, which potentially compromises grid's stability. To address this issue, this paper presents an optimization design method based on "grid-friendly interaction" which optimizes the energy system of zero-energy buildings to ensure better alignment between the building's electricity purchase/sale and grid demand. In this study, an indicator of "grid-friendly interaction", which represents the ratio of the net building friendly interaction power to the total annual interaction power, is firstly established to guide buildings in improving their interaction with the grid. Next, the energy supply, demand, and storage schemes of renewable energy systems in the zero-energy building are iteratively adjusted to achieve the best match with grid's expectations. This optimization design method was applied to a practical case in Hong Kong. Results show that, compared to the benchmark building, the optimal system design scheme increases grid friendliness by 83 %, reduces carbon dioxide emissions by 77 %, but also incurs a total cost increase of 180 %. The optimization design method proposed in this paper can effectively mitigate the impact of zero-energy buildings on the power grid, fostering enhanced interplay between the building structures and the grid. The study significantly contributes to the advancement of exploring the potential for energy flexibility in zero-energy buildings, which is of great significance for the safe and stable operation of future grids. The purpose of this study is to explore the energy flexibility potential of zero-energy buildings, which is of great significance for the safe and stable operation of future grids.

Potential evaluation of energy flexibility and energy-saving of PCM-integrated office building walls

Building energy flexibility is important for reducing peak-to-valley differences in air-conditioner loads. Integrating phase change material into office building envelopes and precooling can effectively reduce peak load, but precooling may increase total energy consumption. Therefore, it is imperative to investigate its energy flexibility and energy consumption features. In present work, a heat transfer model of phase change material (PCM)-integrated office building walls was established firstly and verified by experiments. Then, the energy flexibility and energy-saving potential of the PCM-integrated wall under different precooling strategies were studied, and the effects of various PCM parameters on energy flexibility and energy-saving potential were evaluated. Finally, the influence of different peak-to-valley electricity tariff differences on electricity costs was analyzed. The results show that the optimized precooling strategy and peak-to-valley electricity tariff difference make the energy flexibility index of the PCM-integrated wall up to 69.7% at a total load reduction of 1.3% and save the electricity cost by 51%. The PCM location and melting point have the most significant effect on the energy flexibility and energy-saving potential of the PCM-integrated wall under precooling operating conditions. This study guides the selection of energy flexibility and energy-saving precooling strategy and PCM parameters for PCM-integrated office building walls and the formulation of time-of-use tariff policies.