Energy Flexible Buildings Research Articles

A semantic ontology for representing and quantifying energy flexibility of buildings

• EFOnt, a semantic ontology for building energy flexibility applications, is introduced. • EFOnt is designed to work with other building energy data tools as part of an ecosystem • EFOnt standardizes energy flexibility definition, characterization, and quantification. • One measurement-based and one simulation-based use case of EFOnt are given. • The future development and potential use cases of EFOnt are discussed. Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: ( 1 ) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and ( 2 ) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed.

Improving the building energy flexibility using PCM-enhanced envelopes

Pre-formed internal insulative panels with impregnated phase change materials (PCM) can significantly increase both the thermal resistance and thermal capacitance of existing or new building envelopes, thereby improving the overall energy performance of buildings. A further advantage is that such measures have the potential to enhance the energy flexibility of the building, thereby offering the possibility of participation in demand side management measures such as demand response programmes. The current literature on building envelope physics lacks research on energy flexibility and demand response, especially in the context of the building envelope integrated design with high latent heat materials such as PCM for demand response applications. The objective of the current study is to examine how the addition of PCM impregnated building envelopes affects both the thermal performance of the building envelope, as well as the wider building energy characteristics when subject to different demand response events. A reference building is utilised, which is a residential detached house with a floor area of 160 m 2 and a south-easterly facing aspect. Another contribution of this study is proposing new energy flexibility indicators taking into consideration envelope pre-cooling and pre-heating periods prior to the demand response event. Simulation results show that shorter envelope pre-cooling periods (0.5 hr) together with longer demand response periods (4 h) are preferable for all envelopes to achieve the maximum power curtailment for cooling. PCM-enhanced envelopes are shown to give best cooling demand shifting and energy flexibility efficiency. The MW PCM-1 and MW PCM-2 envelopes have the highest flexibility efficiency with a value of 244%. For heating, gypsum board enhanced with PCM retrofitted on the envelopes are shown to give an overall good performance in energy flexibility efficiency and in power curtailment compared to the other building envelopes in all durations of an energy flexibility event. For heating, the maximum energy flexibility efficiencies range from 250% for the LW Gypsum Board envelope to 356% for the LW PCM-2 envelope. • Energy flexibility is mapped for building envelopes with sensible and latent TES. • Building envelopes are optimised for different demand response events. • An energy flexibility sensitivity analysis algorithm is developed in EnergyPlus. • In buildings with only sensible TES short demand response events should be used. • Envelopes with PCM have better energy flexibility in long demand response events.

Improving energy flexibility and PV self-consumption for a tropical net zero energy office building

Building energy flexibility is crucial for improving the local consumption of renewable energy and enhancing building self-sufficiency. The abundant solar energy resource in the tropics presents a great opportunity to reduce carbon emission and achieve net-zero, but the building energy flexibility remains understudied in the region. Hence, this study proposed and implemented a practical control framework based on Model Predictive Control (MPC) that uncovers the energy flexibility potential of a tropical office building with hybrid cooling systems. Considering the impact of data availability on the actual control performance, MPC with alternative data usage configurations were also investigated in actual and virtual end-to-end experiments. It was first demonstrated that the proposed framework effectively regulated the building load. Compared with the baseline control, the PV self-consumption and the building self-sufficiency were respectively improved by 19.5% and 10.6%. Among the three data categories tested (internal disturbance, external disturbance, and system condition), accurate local weather conditions were shown to be the most critical for desirable control results. Moreover, the benefit of higher data granularity under different building characteristics was quantified in the simulation. Based on the systematic experiments, the relationships between the data availability and control performance were established. Accordingly, a data-centric framework was proposed to enhance the reproducibility and scalability of optimal control studies. Future research can be guided to facilitate large-scale real-world implementations.

Automatic generation of multi-zone RC models using smart thermostat data from homes

An automated methodology that generates sufficiently accurate building models is essential for both the adoption of advanced control strategies like Model Predictive Control, and the estimation of building energy flexibility required for building-grid interaction. This paper presents such a methodology for the generation of multi-zone resistance-capacitance (RC) thermal network models of residential buildings. The models are calibrated using smart thermostat data and predict the indoor air temperature up to 24 h ahead. The methodology starts with a very simple model and iteratively adds one parameter at a time; the parameter that increases the quality of the model the most, as measured with the Bayesian Information Criterion. When the quality of the model cannot be improved any further, the procedure is reversed to delete any redundant parameters. The algorithm concludes when neither adding nor removing a parameter increases the model quality. The algorithm is applied on measured data from an experimental facility, a bungalow house in Québec, Canada. The resulting model can accurately predict all 9 zone temperatures 24 h in advance with an RMSE of 0.44 °C. An examination of the estimated parameters shows that they reflect the layout of the house, previously unknown to the methodology.

Dynamic thermal perception under whole-body cyclical conditions: Thermal overshoot and thermal habituation

The emerging “smart grid” paradigm with associated demand-management programs, such as demand-response (DR), calls for enhanced building energy flexibility. This can be achieved by time-shifting or -shaving building heating and cooling peak loads through the implementation of heating and cooling set-point temperature modulations. However, designing and controlling comfortable DR-induced set-point variations remains a challenge as the psycho-physiology of dynamic thermal perception is still poorly understood. In this paper, we explored the physiological and subjective responses of 29 male and 35 female adults to whole-body warm and cool cyclical thermal conditions and focused on studying the phenomena of “thermal overshoot” and “thermal habituation”. We observed that females responded to cooling with a higher rate of cooling of skin temperature and, correspondingly, stronger thermal overshoot responses compared to males. These perceptual differences were explained in terms of skin temperature differences since the relationship between thermal sensation and skin temperature was independent of sex. We tested whether other factors influence the interindividual variability of the sensory response and found that the thermal overshoot response weakens as participants' body mass index increases and their age decreases. Thermal habituation was found to modify skin temperature only after cool exposures. Given that the studied rates of change of the air temperatures are those that can be typically found in buildings during DR events, our results can have implications on the design and control of female-proofed set-point fluctuations, as designing for an average occupant might result in thermally unacceptable conditions for females.

Classification of Household Room Air Conditioner User Groups by Running Time in the Hot Summer and Cold Winter Zone of China

Household room air conditioners (RACs) are widely used in residential buildings to maintain an indoor thermal climate in China’s hot summer and cold winter (HSCW) zone. The aggregate utilization of RACs in a region has a great impact on regional energy demand in both the heating and cooling seasons. Classifying household RAC users and identifying their RAC usage demands will contribute to better balanced regional energy management for building energy flexibility. In this study, a data-driven method was proposed to classify the household RAC user groups at the regional level, using running time as an indicator. The results showed that RAC users could be classified into four groups with different RAC usage demands. The Lower Class was determined by the absolute poverty line with the Gini coefficient. In addition, the Upper Class was distinguished through the determination of the scaling region in power-law distribution. At the same time, the similarities and differences between different classes in monthly and hourly periods and the flexibility potential were discussed. The rigid demand was observed in the monthly periods of June, July and August and during the hourly periods of 21:00–22:00 in both the bedroom and living-room.

Ten questions concerning energy flexibility in buildings

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings. • Energy flexibility characterization methodologies are needed at the aggregated level. • There is a trend toward decentralized and distributed architectures to harness energy flexibility. • Buildings with/within multicarrier energy systems offer higher levels of flexibility. • Multidisciplinary approaches are needed to address various aspects of the topic. • Supportive policies are key to enable opportunities and incentivize stakeholders.

Demand Flexibility of Residential Buildings: Definitions, Flexible Loads, and Quantification Methods

This paper reviews recent research on the demand flexibility of residential buildings in regard to definitions, flexible loads, and quantification methods. A systematic distinction of the terminology is made, including the demand flexibility, operation flexibility, and energy flexibility of buildings. A comprehensive definition of building demand flexibility is proposed based on an analysis of the existing definitions. Moreover, the flexibility capabilities and operation characteristics of the main residential flexible loads are summarized and compared. Models and evaluation indicators to quantify the flexibility of these flexible loads are reviewed and summarized. Current research gaps and challenges are identified and analyzed as well. The results indicate that previous studies have focused on the flexibility of central air conditioning, electric water heaters, wet appliances, refrigerators, and lighting, where the proportion of studies focusing on each of these subjects is 36.7%, 25.7%, 14.7%, 9.2%, and 8.3%, respectively. These flexible loads are different in running modes, usage frequencies, seasons, and capabilities for shedding, shifting, and modulation, while their response characteristics are not yet clear. Furthermore, recommendations are given for the application of white-, black-, and grey-box models for modeling flexible loads in different situations. Numerous static flexibility evaluation indicators that are based on the aspects of power, temporality, energy, efficiency, economics, and the environment have been proposed in previous publications, but a consensus and standardized evaluation framework is lacking. This review can help readers better understand building demand flexibility and learn about the characteristics of different residential flexible loads, while also providing suggestions for future research on the modeling techniques and evaluation metrics of residential building demand flexibility.

Characterizing energy flexibility of buildings with electric vehicles and shiftable appliances on single building level and aggregated level

Residential energy flexibility is considered one of the efficient concepts to alleviate the ever-increasing concerns of better balancing supply and demand. A positive assumption that all buildings have the same energy flexibility potential, is not applicable in a realistic situation especially when direct load control is not applied for each. This paper proposes a novel approach to characterize the energy flexibility of shiftable appliances and EVs (as two main sources of energy flexibility) protecting consumers’ data privacy and considering the usage behavior. First, an xg-boost regression is utilized to non-intrusively extract the consumption of appliances. Then, the uncertainty of the power values and the operation time of each appliance is computed based on the extracted consumption patterns. Finally, a price-based DR model is used to determine their energy flexibility and prioritize them according to the change in their operating time before and after optimization. Case studies are conducted and results prove that the proposed method is computationally cost-effective and outperforms other methods in terms of accuracy, customer privacy and comfort. Moreover, the results show that the proposed model can significantly decrease the flattening signal up to 3% for each residential building and up to 25% in the residential aggregated level.

Energy flexibility and viability enhancement for an ocean-energy-supported zero-emission office building with respect to both existing and advanced utility business models with dynamic responsive incentives

The foreseeable large-scale deployment of intermittent renewable energy systems in the future and fluctuations in energy markets can severely affect the operation and stability of smart grids, creating significant uncertainty and instability in the electricity supply and demand. It is believed that the mismatch between energy demand of a building and instantaneous renewable energy generation can be reduced by controlling energy use through the energy flexibility of the respective building. Enhancing the energy flexibility of a zero-emission building can satisfy the demands of the energy network around the building and contribute to the resilience of the energy system. Meanwhile, in an electricity tariff model with a distinction between peak and off-peak periods, enhancing energy flexibility in buildings can reduce the operating costs of electricity consumption, with benefits to the economic performance of the building itself. In this study, a simulated hypothetical zero-emission office building near the coast with a floating photovoltaic system and tidal stream generator system in Hong Kong from previous study were used in a case study to investigate the impact of energy flexibility control with stationary batteries as the source of energy flexibility. Two flexibility control strategies were designed to demonstrate their impact on the economic performance of the system. After incorporating the “Peak Demand Management” (PDM) programme in Hong Kong, variations in the economic performance of the system were demonstrated through simulations. Considering the incentive provided by the PDM programme, the possibility of achieving a neutral economic performance at different percentages of renewable energy generation over a 20-year life cycle without the feed-in tariff was investigated. Furthermore, two possible modifications for business models and the PDM programme are proposed. The simulation results indicate that both suggestions can significantly improve the economic performance of the system.

Design for energy flexibility in smart buildings through solar based and thermal storage systems: Modelling, simulation and control for the system optimization

The present study investigates the use and implementation of energy efficient measures and strategies for building applications, toward the Nearly Zero Energy Buildings target. Specifically, objective of the study is to implement building integrated photovoltaic thermal devices coupled with a phase change materials heat exchanger acting as an active thermal storage building component, with the aim to add flexibility to the building while still maintaining indoor comfort conditions. To show the potentials of the novel configuration proposed in this paper, a multi-zone grey-box model is developed and validated to capture the thermal dynamics of a building, and a control strategy applied to the whole system is developed for energy management purpose. The whole simulation model, including thermophysical properties of the building-system and the control features, is implemented in a MATLAB environment. To assess the model and application potentials toward the optimal design and operation of the proposed system for energy efficiency and flexibility goals, a suitable case study analysis is conducted. Thus, a sensitivity analysis, using an evolutionary algorithm, is performed by considering economic and energy objective functions which focuses on the reduction of the building energy demand, load variability and economic aspects. In this regard, the optimal design configuration is underlined in a way that the operation of the components can be maximized to provide flexibility to the building: in average working conditions one single layer of PCM can provide around 186.3 Wh/K per unit of temperature and width. A rule-based management strategy is proposed to prove the possibility to shift and shave the energy peaks during high energy request periods, demand response events. Finally, by considering an approximate economic calculation, the simple payback, taking into account only the positive effects on the winter management, is around 13.5 years.

Evaluation of methods for determining energy flexibility of buildings

The high rate of penetration of renewable energy sources leads to challenges in planning and controlling the production, transmission and distribution of energy. A possible solution lies within the change from traditional supply side management to demand side management. Buildings are good candidates for implementing a demand response model since they account for around 39% of global final energy use and are stably connected to all infrastructure networks. As a result, employing buildings as "players" in energy networks is considered now more than ever compelling. Recently, significant improvement has been denoted in the thermal efficiency of the building shell and the energy efficiency of the HVAC systems in new and renovated buildings. However, despite the reduction in energy demand regarding the space conditioning, buildings continue to be passive end users of the energy system. In order to ensure that they are capable of providing the necessary energy flexibility to balance intermittent energy production, a first step is to establish a formal, standard, and robust method of characterizing the energy flexibility provided on the demand side. Buildings can supply flexibility in a variety of ways, but there is currently no fixed and consistent method for quantifying the amount of flexibility a building can provide to future energy systems. In this paper, an overview of the literature on building energy flexibility will be offered, as well as an introduction to the concept of building energy flexibility and the methodologies used to define and evaluate it.

Load pattern recognition based optimization method for energy flexibility in office buildings

Air conditioning systems are generally considered to have the greatest flexibility potential in buildings that can be flexibly regulated with thermal storage to reduce the interaction with the power grid and increase demand response benefits. In previous studies, the flexibility of air-conditioning systems was reflected through time-of-use tariffs. However, a strategy that only factors the tariffs incurs a greater operational energy consumption. In this study, a flexibility factor was established and incorporated into the multi-objective optimization process, together with the operational energy consumption, as two optimization objectives. After obtaining typical load patterns using a two-step clustering method, for multi-objective decision-making in the day-ahead operation, the entropy-grey technique for order preference by similarity to an ideal solution method is used. Considering an office building as a case study, we found that the optimized flexibility factor can reach 0.31 and 0.99 during a week of operation in winter and summer, on average, respectively, and achieved a cumulative energy-saving effect of 17.98% and 35.49%. In addition, the two-step clustering method can better demonstrate the flexibility factor than the single-step clustering.

A niche technique overlooked in the Danish district heating sector? Exploring socio-technical perspectives of short-term thermal energy storage for building energy flexibility

This research explores socio-technical perspectives of the demand-side management strategy of using the built environment for short term thermal energy storage. Here conceptualised as a niche innovation within the Danish socio-technical district heating landscapes, the research explores potentials and limitations of this building energy flexibility strategy from the perspective of district heating sector professionals, actors at the centre of the low-carbon energy transitions. Results of the mixed-methods abductive research enquiry suggest that this energy flexibility strategy facilitates (I) solving local network congestion challenges in smaller parts of existing networks and (II) reduces needed network capacity in new heat supply areas. Sector professionals assess this (III) energy flexibility strategy as most practicable in large-scale/commercial buildings and industries. Challenges include hardware balancing, service and maintenance, and the sometimes counterproductive incentive structures among stakeholders involved. Research evidence suggests that business models appealing to environmental values and priorities may incentivise sustainable heat-use behaviours more than economic benefit alone among some groups of end users. Building energy flexibility and demand-side management strategies may become integral to future ‘smart’ energy systems throughout the world. However, their successful implementation necessitates understanding the local socio-technical dynamics involved. Multidisciplinary research approaches as the one taken here facilitate these necessary insights.

Fifty shades of grey: Automated stochastic model identification of building heat dynamics

To reach the carbon emission reduction targets set by the European Union, the building sector has embraced multiple strategies such as building retrofit, demand side management, model predictive control and building load forecasting. All of which require knowledge of the building dynamics in order to effectively perform. However, the scaling-up of building modelling approaches is still, as of today, a recurrent challenge in the field. The heterogeneous building stock makes it tedious to tailor interpretable approaches in a scalable way. This work puts forward an automated and scalable method for stochastic model identification of building heat dynamics, implemented on a set of 247 Dutch residential buildings. From established models and selection approach, automation extensions were proposed along with a novel residual auto-correlation indicator, i.e., normalized Cumulated Periodogram Boundary Excess Sum (nCPBES), to classify obtained model fits. Out of the available building stock, 93 building heat dynamics models were identified as good fits, 95 were classified as close and 59 were designed as poor. The identified model parameters were leveraged to estimate thermal characteristics of the buildings to support building energy benchmarking, in particular, building envelope insulation performance. To encourage the dissimination of the work and assure reproducibility, the entire code base can be found on Github along with an example data set of 3 anonymized buildings. The presented method takes an important step towards the automation of building modeling approaches in the sector. It allows the development of applications at large-scale, enhancing building performance benchmarks, boosting city-scale building stock scenario modeling and assisting end-use load identifications as well as building energy flexibility potential estimation.

An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems

A key issue in energy flexibility assessment is the lack of a scalable practicable approach to quantify and characterise the flexibility of individual residential buildings from an integrated energy system perspective without the need to use complex simulation models. In this study, this problem is addressed by explicitly quantifying the flexibility of multicomponent thermal and electrical systems commonly found in residential buildings based on an ensemble learning framework that consists of four algorithms, namely, random forests, multilayer perceptron neural network, support vector machine, and extreme gradient boosting. The day-ahead and hour-ahead prediction models developed are periodically updated considering dynamic feature selection based on residential occupancy patterns. The proposed methodology utilises synthetic data obtained from a calibrated white-box model of an all-electric residential building for two indicative occupancy profiles. The energy systems evaluated include a heat pump, a photovoltaic system, and a battery unit. The daily flexibility mappings are acquired by applying hourly independent, and consecutive demand response actions for each energy system considered, using suitable energy flexibility indicators. The results show that the ensemble models developed for each target variable outperform each of the constituent machine learning algorithms. Moreover, the storage capacity resulting from harnessing the heat pump downward flexibility demonstrates accurate accuracy with a coefficient of determination equal to 0.979 and 0.968 for day-ahead predictions and 0.998 and 0.978 for day ahead predictions for the two occupancy profiles considered, respectively. This framework can be used by electricity aggregators to evaluate a building portfolio in an end-user-tailored manner or optimally exploit its energy flexibility considering multi-step predictions to shift electricity usage to off-peak times or times of excess onsite renewable energy generation.

Optimal Control Strategies for Demand Response in Buildings under Penetration of Renewable Energy

The penetration rates of intermittent renewable energies such as wind and solar energy have been increasing in power grids, often leading to a massive peak-to-valley difference in the net load demand, known as a “duck curve”. The power demand and supply should remain balanced in real-time, however, traditional power plants generally cannot output a large range of variable loads to balance the demand and supply, resulting in the overgeneration of solar and wind energy in the grid. Meanwhile, the power generation hours of the plant are forced to be curtailed, leading to a decrease in energy efficiency. Building demand response (DR) is considered as a promising technology for the collaborative control of energy supply and demand. Conventionally, building control approaches usually consider the minimization of total energy consumption as the optimization objective function; relatively few control methods have considered the balance of energy supply and demand under high renewable energy penetration. Thus, this paper proposes an innovative DR control approach that considers the energy flexibility of buildings. First, based on an energy flexibility quantification framework, the energy flexibility capacity of a typical office building is quantified; second, according to energy flexibility and a predictive net load demand curve of the grid, two DR control strategies are designed: rule-based and prediction-based DR control strategies. These two proposed control strategies are validated based on scenarios of heating, ventilation, and air conditioning (HVAC) systems with and without an energy storage tank. The results show that 24–55% of the building’s total load can be shifted from the peak load time to the valley load time, and that the duration is over 2 h, owing to the utilization of energy flexibility and the implementation of the proposed DR controls. The findings of this work are beneficial for smoothing the net load demand curve of a grid and improving the ability of a grid to adopt renewable energies.

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

Investigation and evaluation of building energy flexibility with energy storage system in hot summer and cold winter zones

The dramatically increasing energy demand of building air-conditioning in hot summer and cold winter zones fluctuates greatly, especially during the period of cooling and heating in summer and winter, which exerts strict operation burden and challenge on power-grid system stability. In order to realize building energy flexibility, this paper proposes a type of energy utilization system including electricity chiller, gas-boiler, electricity heater, cold and thermal energy storage tank (case II), on the basis of electricity chiller and gas-boiler for cooling and heating demand (case I), which is commonly used as cold and heat source in hot summer and cold winter zones. The total cost of the whole life period and annual carbon emission of energy systems are served as objective function is optimized. Furthermore, the system capacity confirmation and load demand management control are analyzed and discussed, the characteristics of energy-flexibility with and without energy storage are presented, the load coefficient is introduced to evaluate the impact of case II on building energy flexibility. Compared with case I, with the deployment of cold and thermal energy storage, the cost-effectiveness of case II is 16.5% and carbon emission is reduced by 30.9% compared with case I .In case II, the load coefficient of electricity chiller and electricity heater is 83.34% and 30.65% respectively, which means that energy storage system enables to promote building energy flexibility improvement and grid-friendliness enhancement. This paper provides methods and theoretical guidance for the planning-operation of building energy system and building energy flexibility in hot summer and cold winter zones.

Sociotechnical imaginaries of resident roles: Insights from future workshops with Danish district heating professionals

Technical research indicates that buildings can be used for short-term heat storage to peak-shave daily profiles in district heating. Increasing the energy flexibility of buildings implies new relations between demand and production – newly defined roles between residents and heating professionals. Heating professionals' understanding of residents is an important precondition for how these roles can be changed. To investigate this empirically, this paper reports from three future workshops where professionals from Danish district heating utilities, municipalities, energy companies and housing organisations discuss possibilities for including residents more actively in flexibility generation and more generally in district heating systems operation. The analysis focuses on the importance professionals ascribe to residents compared to other actors, the vocabulary professionals use to describe residents, and, most importantly, the rationales professionals ascribe to residents. Inspired by the notion of sociotechnical imaginaries, our analysis shows how the professionals imagine residents and ascribe rationales of autonomy, economy, comfort, involvement and sustainability to them. The professionals emphasise the first three rationales in particular. The conclusion points to the negative consequences such imaginaries may have for the future development of flexible district heating and proposes experimenting with various collaborative forms for engaging residents and professionals.