• Energy Research

Highlights•Common focus points in existing definitions of energy flexible buildings have been identified.•Quantification methods for the prediction of the available energy flexibility of buildings are reviewed.•Comparison of methods on a thermal case study shows significant overlap among indicators.•Time, power and cost are identified as main recurring characteristics.•Optimal control methods are found more appropriate with increasing system complexity.

Network flexibility is the use of the thermal capacity of water that is contained in the district heating network pipes to store energy and shift the heat load in time. Through optimal control, this network flexibility can aid in applications such as peak shaving and operational heat pump optimisation. Yet, optimal control requires perfect predictions and complete knowledge of the system characteristics. In reality, this is not the case and uncertainties exist. To obtain insight into the importance of these uncertainties, this paper studies the influence of imperfect knowledge of building parameters on the optimal network flexibility activation and its performance. It is found that for the optimisation of heat pump operation, building parameter uncertainties do not present large risks. For peak shaving, a more robust result can be achieved by activating more network flexibility than may be required.

Abstract This study investigates the use of thermal mass within a smart sustainable district (SSD) to facilitate the renewables’ integration. The aim is to pinpoint the most suitable archetypes in a SSD and the most optimal energy flow to each of them; moreover, to establish a relation between the suitability to activate thermal mass and the properties of the archetypes, namely Heat Loss Coefficient (HLC), Structural mass (SM) and time constant (TC). First a dummy residential district is defined, then using construction and envelope properties, archetypes are defined. Based on the archetypes, the district is clustered and then it is modelled using Artificial Neural Networks (ANN). The energy optimization and renewable integration is casted as an Optimal Control Problem (OCP). Solving this OCP ensures thermal comfort within the building clusters and the renewable energy is stored optimally through the thermal mass. Finally, conclusions are drawn on which clusters of buildings offer the most opportunities to store the renewables through their thermal mass within a district and which criteria best reflect on that. The results signify that HLC and TC are more suitable criteria.

Abstract To assess the impact of implementing energy efficiency and renewable energy measures, urban building energy models are emerging. In these models, due to the lack of data, the natural variability of the existing building stock is often highly underestimated and uncertainty on the simulated energy use arises. Therefore, this work proposes a probabilistic building characterization method to model the variability of the existing residential building stock. The method estimates realistic distributions of five input variables: U-values of the floor, external walls, windows and roof as well as window-to-wall ratio, based on known data (location, geometry and construction year). First, quantile regression has been implemented to generate the uncorrelated distributions based on the Flemish energy performance certificates database. The accuracy of the marginal distributions is good, as the empirical coverage on the 50%, 80%, 90% and 98% prediction interval deviates 0.6% at most. However, it is needed to include the correlations between these variables. Hence, three main methods to build multivariate distributions from marginal distributions and to draw correlated samples are implemented and extensively compared. The Gaussian copula method is put forward as the preferred method. Considering the mean-maximum discrepancy (MMD), this method performs eight times better than the uncorrelated case (MMD of 0.0027 versus 0.0228).

Abstract The use of structural thermal storage is often suggested as a key technology to improve the penetration of renewable energy sources and mitigate potential production and distribution capacity issues. Therefore, a quantitative assessment of the energy flexibility provided by structural thermal energy storage is a prerequisite to instigate a large scale deployment of thermal mass as active storage technologies in an active demand response (ADR) context. In the first part of the work, a generic, simulation-based and dynamic quantification method is presented for the characterization of the ADR potential, or energy flexibility, of structural thermal energy storage. The quantification method is based on three ADR characteristics – i.e. available storage capacity, storage efficiency and power-shifting capability – which can be used to quantify the ADR potential in both design and operation. In the second part of the work, the methodology is applied to quantify the ADR characteristics for the structural thermal energy storage capacity for the different typologies of the Belgian residential building stock. Thereby an in-depth analysis demonstrates the relation between the building properties and its energy flexibility as well as the dependence of the energy flexibility on the dynamic boundary conditions.

Abstract The integration of buildings in a Smart Grid, enabling demand-side management and thermal storage, requires robust reduced-order building models that allow for the development and evaluation of demand-side management control strategies. To develop such models for existing buildings, with often unknown the thermal properties, data-driven system identification methods are proposed. In this paper, system identification is carried out to identify suitable reduced-order models. Therefore, grey-box models of increasing complexity are identified on results from simulations with a detailed physical model, deployed in the integrated district energy assessment simulation (IDEAS) package in Modelica. Firstly, the robustness of identified grey-box models for day-ahead predictions and simulations of the thermal response of a dwelling, as well as the physical interpretation of the identified parameters, are analyzed. The influence of the identification dataset is quantified, comparing the added value of dedicated identification experiments against identification on data from in use buildings. Secondly, the influence of the data used for identification on model performance and the reliability of the parameter estimates is quantified. Both alternative measurements and the influence of noise on the data are considered.

Abstract In the debate on predicting household energy savings, the temperature takeback – an increased indoor temperature after an energy efficient retrofit – is often blamed for offsetting part of the potential energy savings. Mostly, it is attributed to inhabitants grading up their heating behaviour to the lower energy cost after retrofit. However, even if inhabitants do not change their heating pattern, the indoor temperature will still rise after retrofit due to physical processes: warmer unheated zones and less temperature drop between two heating periods. This paper uses building energy simulation tools to assess the extent of these physical processes in the overall temperature rise. An existing terraced house is modelled and fictitious renovation measures are imposed, keeping the heating patterns unchanged. For the case analysed, a heating season mean indoor temperature rise of about 1 °C is found, being in the same order of magnitude as empirically detected temperature changes. This suggests that the remaining behavioural aspect of the temperature takeback might be smaller than generally assumed. In addition, the comparison is made with a calculation method based on the EPBD regulation that does not take into account the physical temperature rise. The latter method overestimates the potential energy savings by about 6%.

Abstract Within the context of amongst others urban energy planning and energy system design, urban and district energy simulations have gained interest to quantify the energy use of existing districts. To reduce calculation time and in absence of adequate detailed building level data, urban energy simulations often deploy reductive modelling approaches based on a limited set of buildings, or archetype buildings. This may lead to significant modelling errors when the archetype buildings are not tailored to the studied location. This paper explores a building clustering approach that harvests available local building information, e.g. geospatial data, to generate a tailored set of archetype buildings. Focussed on simulating the annual heat demand or peak heat demand, this paper evaluates if clustering on building properties can be an alternative to clustering on the energy key performance indicators of interest to define the tailored archetypes. As consumption data on a building level is often not available, such an approach would eliminate the need to simulate the energy use for all buildings. The results show that indeed clustering on the properties is a viable alternative with robust results for both annual energy use and peak energy demand and a comparable accuracy compared to clustering on the targeted performance indicators.