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This paper assesses the quality of the services provided for demand response by analysing the results of experimental work activating flexible sources in buildings, while evaluating the impacts on occupant comfort and extending the dataset through aggregation, to quantify the uncertainty for multiple systems. Power and energy flexibility is an integral part of the solution to address the challenge of grid balancing with increased renewable generation integration. However, the variability of the provided flexibility, as measured by the stability and consistency of load reduction or increase, may vary widely. To address this, the concept of quality of flexibility is introduced and analysed through the results of experiments conducted at a case study building to activate three sources of flexibility: heat pumps, Air Handling Unit fans and battery storage. The results show that fan data exhibits low uncertainty, suitable for ancillary services, whereas heat pumps¿ volatility is large. Standard error for heat pumps was within the quality threshold of 10 %, appropriate for energy services. Aggregation of multiple systems through the creation of a semi-synthetic dataset decreased the uncertainty for hourly energy services to as low as 2 %. For all cases, the impact on occupant comfort was not found to be significant. Funding was provided by the Sustainable Energy Authority of Ireland and the EU project ELSA (Energy Local Storage Advanced system) which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 646125. Flexibility research at the case study building was facilitated with grateful thanks to Geoff Watson of Zero Carbon peer-reviewed

© 2019 Elsevier B.V. This paper explores the concept of characterizing the as-built Heat Loss Coefficient (HLC)of buildings based on-board monitoring (OBM), via energy consumption and temperature sensors, and time series analysis. It is examined (1)how the coefficients of different Auto-Regressive with eXogenous inputs (ARX)models can be interpreted and (2)whether these conclusions are sensitive to the building envelope assembly or the applied indoor temperature profile. The paper presents a theoretical case study whereby detailed building energy simulations are used to accurately map the impact of physical phenomena on the characterization process. The simulation models and boundary conditions are composed to focus on the link between the estimated ARX-coefficients and the physical driving forces for transmission heat loss to the ground and the exterior environment. The results show how the various ARX model coefficients are linked to specific components of the HLC (e.g. heat transfer through the walls and roof or through the slab-on-ground floor)and to what extent they are affected by the selection of input variables. By monitoring the ground temperature, the transmission heat losses can rather accurately be assigned to either the slab-on-ground or the walls and roof. Without this measurement data, the uncertainty on the estimates increases (ranges of the 95% confidence interval of up to 35% of the mean estimate). Modeling the ground heat losses by a constant intercept term leads to underestimations of the reference HLC of up to 59%, whereas adding heat flux sensors to monitor the transmission heat losses to the ground to the measurement set-up allows to assess the transmission heat transfer coefficient to the exterior environment HLC e within 2%. ispartof: ENERGY AND BUILDINGS vol:195 pages:180-194 status: published

ispartof: pages:1-9 ispartof: BS'2019 pages:1-9 ispartof: Building Simulation Conference 2019 location:Rome date:2 Sep - 4 Sep 2019 status: published

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. ispartof: Energy And Buildings vol:208 pages:1-13 status: published

The past decade has seen the rapid development of sensor technologies. Monitoring data of the interior climate and energy consumption of in-use buildings, so-called on-board monitoring (OBM) data, offers the opportunity to identify as-built energy performance indicators, such as the heat loss coefficient (HLC) of the building envelope. To this end, it is important to advance the understanding of the impact of the OBM set-up and the applied data analysis method. This paper uses synthetic OBM data sets, generated from building energy simulations. The level of accuracy achieved with four data analysis methods for characterizing the HLC is investigated. The considered methods are the Average Method, the Energy Signature Method, Linear Regression and ARX modeling. Different cases, representing different building types, are considered in order to gain thorough insight into the physical interpretation of the results. By taking subsets of the original data sets, the sensitivity of the data analysis methods to the availability of specific data is assessed. This theoretical exercise illustrates how, under idealized monitoring circumstances, both linear regression and ARX models can accurately determine the HLC. The latter is able to assess the performance indicator within 5%. However, when subjected to practical limitations regarding the measurement of system inputs, such as unavailable solar or internal heat gains, the characterization results show large variations in accuracy and uncertainty.

The large penetration rate of renewable energy sources leads to challenges in planning and controlling the energy production, transmission, and distribution in power systems. A potential solution is found in a paradigm shift from traditional supply control to demand control. To address such changes, a first step lays in a formal and robust characterization of the energy flexibility on the demand side. The most common way to characterize the energy flexibility is by considering it as a static function at every time instant. The validity of this approach is questionable because energy-based systems are never at steady-state. Therefore, in this paper, a novel methodology to characterize the energy flexibility as a dynamic function is proposed, which is titled as the Flexibility Function. The Flexibility Function brings new possibilities for enabling the grid operators or other operators to control the demand through the use of penalty signals (e.g., price, CO2, etc.). For instance, CO2-based controllers can be used to accelerate the transition to a fossil-free society. Contrary to previous static approaches to quantify Energy Flexibility, the dynamic nature of the Flexibility Function enables a Flexibility Index, which describes to which extent a building is able to respond to the grid’s need for flexibility. In order to validate the proposed methodologies, a case study is presented, demonstrating how different Flexibility Functions enable the utilization of the flexibility in different types of buildings, which are integrated with renewable energies. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)

To assess the feasibility of district energy systems as well as to design them in an optimal way, districtenergy simulations are often deployed, requiring an accurate spatial and temporal quantification of thedistrict energy demand. Geographical information models and systems can provide input data toquantify the district energy demand, but the available levels of detail (LOD) of these data vary signifi-cantly between regions. Therefore, this work investigates the usability of LOD1 and LOD2 representationsas well as the impact of building geometry within district energy simulations, by quantifying the dif-ferences in geometrical and energy characteristics between five variants of LOD1 or LOD2 representa-tions. The most detailed LOD2 representation is thereby used as a reference. The results show that thesignificantly decreasing accuracy using LOD1 models may be compensated by assuming the roof shapefrom regional statistics. Also, aggregation of wall and roof components into a limited number of orien-tations significantly reduces si mulation time, while maintaining the accuracy. It is concluded thatgeographical information mo dels contain a significant amount of useful data, but the error that resultsfrom the deployed level of detail must be kept in mind when assessing the simulation results. ispartof: ENERGY vol:158 pages:1060-1069 ispartof: location:DENMARK, Copenhagen status: published

© 2018 International Building Performance Simulation Association (IBPSA). Building and district energy systems become increasingly complex, requiring accurate simulation and optimization of systems that combine building envelope, heating ventilation and air conditioning, electrical distribution grids and advanced controllers. Hence, it becomes more challenging for existing simulation tools to provide integrated solutions for these multi-physics problems. Moreover, common building simulation frameworks tightly integrate model equations and their solvers in the program code, which affects model transparency and hampers tool extensions. This is contrasted by equation-based tools such as Modelica, for which different solvers can be used. In this context, the Integrated District Energy Assessment by Simulation (IDEAS) library is developed. After a recent development shift towards more detailed, multi-zone models, this paper presents a comprehensive, well-documented, overview of the buildings part of IDEAS. This includes new computational aspects of the library, improved usability aspects, an updated intercomparison with BESTEST and a verification based on IEA EBC Annex 58. ispartof: Journal of Building Performance Simulation vol:11 issue:6 pages:669-688 status: published

An integrated approach using district energy simulations is essential to analyse energy efficient buildings and cities. Thereby, an accurate building representation in dimensions of space is considered as an essential boundary condition. Nonetheless, district energy simulations are often carried out using a limited set of archetype buildings. This paper analyses therefore the impact of a more accurate building representation in district energy models, for a given district. Three approaches to include building geometry with a different level of detail are extensively analysed through a comparison of geometrical properties, peak power, total energy use and overheating risk. The GIS-based approach is favoured for the design of district energy systems, as it enables a more accurate and automatic implementation of the spatial dimension of the dynamic energy simulation results.

Purpose The purpose of this paper is to standardised four-step flexibility assessment methodology for evaluating the available electrical load reduction or increase a building can provide in response to a signal from an aggregator or grid operator. Design/methodology/approach The four steps in the methodology consist of Step 1: systems, loads, storage and generation identification; Step 2: flexibility characterisation; Step 3: scenario modelling; and Step 4: key performance indicator (KPI) label. Findings A detailed case study for one building, validated through on-site experiments, verified the feasibility and accuracy of the approach. Research limitations/implications The results were benchmarked against available demonstration studies but could benefit from the future development of standardised benchmarks. Practical implications The ease of implementation enables building operators to quickly and cost effectively evaluate the flexibility of their building. By clearly defining the flexibility range, the KPI label enables contract negotiation between stakeholders for demand side services. It may also be applicable as a smart readiness indicator. Social implications The novel KPI label has the capability to operationalise the concept of building flexibility to a wider spectrum of society, enabling smart grid demand response roll-out to residential and small commercial customers. Originality/value This paper fulfils an identified need for an early stage flexibility assessment which explicitly includes source selection that can be implemented in an offline manner without the need for extensive real-time data acquisition, ICT platforms or additional metre and sensor installations.

Several studies have demonstrated the capability of data-driven modelling based on on-site measurements to characterise the thermal performance of building envelopes. Currently, such methods include steady-state and dynamic heating experiments and have mainly been applied to scale models and unoccupied test buildings. Nonetheless, it is proposed to upscale these concepts to characterise the thermal performance of in-use buildings based on on-board monitoring (OBM) devices which gather long-term operational data (e.g., room temperatures, gas and electricity consumption…). It remains, however, to be proven whether in-use data could be a cost-effective, practical and reliable alternative for the dedicated tests whose more intrusive measurements require on-site inspections. Furthermore, it is presently unclear what the optimal experimental design of the OBM would be and which data analysis methods would be adequate. This paper presents a first step in bridging this knowledge gap, by using on-board monitoring data to characterise the overall heat loss coefficient (HLC) [W/K] of an occupied, well-insulated single-family house in the UK. With the aid of a detailed building physical framework and specifically selected data subsets a sensitivity analysis is carried out to analyse the impact of the measurement set-up, the duration of the measurement campaign and the applied data analysis method. Although the exact HLC of the building is unknown and no absolute errors could hence be calculated, this paper provides a new understanding of the decisions that have to be made during the process from design of experiment to data analysis. It is demonstrated that such judgements can lead to differences in the mean HLC estimate of up to 89.5%.

Renewable energy integration in power systems and increasing electrification of energy demand create new challenges to which energy flexibility can provide effective solutions. Trough an innovative use of cumulative energy consumption curves, which represent the maximum and minimum energy limits, as well as the associated flexible energy consumption, this paper presents a methodology to characterise and use the energy flexibility provided by water pumping and storage systems (WPSS) in order to achieve specific objectives at different levels of power systems. The methodology is applied to a case study considering a real WPSS where energy flexibility is used to reduce electricity costs and support the operation of the power system during a wind generation curtailment event. Collected results show that savings around 16% can be achieved while reducing pumping cycles by 57%. Furthermore, the WPSS operation can be modified according to the needs of the power system using the available energy flexibility.

As demand response and energy flexibility are often suggested as key principles to facilitate high levels of renewable energy sources into energy markets, different studies evaluated the potential impact of energy flexibility in buildings. Nonetheless, due to differences in definition and quantification methodologies for energy flexibility, comparing results between such studies is difficult. With a review and applied evaluation of existing definitions and quantification methodologies this paper aims at assessing the applicability, benefits and drawbacks of each quantification methodology. The conducted review shows that energy flexibility definitions found in the literature have their particularities despite sharing the same principle that energy flexibility is the ability to adapt the energy profile without jeopardizing technical and comfort constraints. The survey of quantification methodologies reveals two main approaches to quantify energy flexibility. A first approach quantifies energy flexibility indirectly using past data and assuming a specific energy system and/or energy market context. The second approach directly predicts the energy flexibility that a building can offer to the energy system in a bottom-up manner. While applications for both approaches were identified, this paper focuses on the latter. By applying methodologies that follow this second approach to a common case study, three common properties of energy flexibility were observed: i) the temporal flexibility; ii) the amplitude of power modulation; iii) and the associated cost.

Characterization of building energy performance indicators such as the Heat Loss Coefficient (HLC) based on in-situ measurement data calls for thorough building physical insight, a well- designed measurement set-up to collect sufficient, qualitative data and adequate data analysis methods. On-board monitoring may be an alternative for dedicated experiments to perform the data collection task. This paper analyses the sensitivity of the end-result of the characterization, the HLC estimate, to flaws in the monitoring data set. More specifically, the impact of not installing submeters to disentangle the gas consumption for space heating and the production of domestic hot water is evaluated. Hereto, multiple gas decomposition methods are applied on a case study monitoring data set, after which the HLC is assessed. The results show deviations up to 33% for the mean estimate. Nevertheless, the 95% confidence intervals largely overlap.

Heat pumps are widely recognized as a key technology to reduce CO2 emissions in the residential building sector, especially when the electricity-generation system is to decarbonize by means of large-scale introduction of renewable electric power generation sources. If heat pumps would be installed in large numbers in the future, the question arises whether all building types show equal benefits and thus should be given the same priority for deployment. This paper aims at answering this question by determining the CO2-abatement cost of installing a heat pump instead of a condensing gas boiler for residential space heating and domestic hot-water production. The electricity system, as well as the building types, are based on a possible future Belgian setting in 2030 with high RES penetration at the electricity-generation side. The added value of this work compared to the current scientific literature lies in the integrated approach, taking both the electricity-generation system and a bottom up building stock model into account. Furthermore, this paper analyzes the possible benefits of active demand response in this framework. The results show that the main drivers for determining the CO2-abatement cost are the renovation level of the building and the type of heat pump installed. For thoroughly insulated buildings, an air-coupled heat pump combined with floor heating is the most economic heating system in terms of CO2-abatement cost. Finally, performing active demand response shows clear benefits in reducing costs. Substantial peak shaving can be achieved, making peak capacity at the electricity generation side superfluous, hence lowering the overall CO2-abatement cost.

Both a well-designed on-board monitoring campaign and an adequate data-driven statistical modeling method are required to accurately characterize the building’s overall heat transfer coefficient (HTC). In this paper, we reflect on the latter by means of a theoretical deduction of the heat balance equation and case studies on simulation data. We demonstrate the impact of using air temperatures as a proxy for equivalent temperatures and neglecting the intercept when characterizing the HTC using a linear regression method on measurement data. ispartof: pages:969-974 ispartof: Energy Procedia vol:132 pages:969-974 ispartof: Nordic Symposium on Building Physics location:Trondheim date:11 Jun - 14 Jun 2017 status: published

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. ispartof: Applied Energy vol:198 pages:192-202 status: published

The increasing global energy demand, the foreseen reduction of available fossil fuels and the increasing evidence off global warming during the last decades have generated a high interest in renewable energy sources. However, renewable energy sources, such as wind and solar power, have an intrinsic variability that can seriously affect the stability of the energy system if they account for a high percentage of the total generation. The Energy Flexibility of buildings is commonly suggested as part of the solution to alleviate some of the upcoming challenges in the future demand-respond energy systems (electrical, district heating and gas grids). Buildings can supply flexibility services in different ways, e.g. utilization of thermal mass, adjustability of HVAC system use (e.g. heating/cooling/ventilation), charging of electric vehicles, and shifting of plug-loads. However, there is currently no overview or insight into how much Energy Flexibility different building may be able to offer to the future energy systems in the sense of avoiding excess energy production, increase the stability of the energy networks, minimize congestion problems, enhance the efficiency and cost effectiveness of the future energy networks. Therefore, there is a need for increasing knowledge on and demonstration of the Energy Flexibility buildings can provide to energy networks. At the same time, there is a need for identifying critical aspects and possible solutions to manage this Energy Flexibility, while maintaining the comfort of the occupants and minimizing the use of non-renewable energy. In this context, the IEA (International Energy Agency) EBC (Energy in Buildings and Communities program) Annex 67: “Energy Flexible Buildings” was started in 2015. The article presents the background and the work plan of IEA EBC Annex 67 as well as already obtained results. Annex 67 is a corporation between participants from 16 countries: Austria, Belgium, Canada, China, Denmark, Finland, France, Germany, Ireland, Italy, The Netherlands, Norway, Portugal, Spain, Switzerland and UK. ispartof: Energy and Buildings vol:155 pages:25-34 status: published

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.

This paper will focus on the energy performance of the public building sector in Flanders (Belgium) by analysing the recently published EPC database. The main aim of this paper is to firstly have a qualitive and quantitative overview of the current energy performance of different building categories in the Flemish public building sector. The non-residential building types (office, educational, healthcare etc.) will be categorized. Within each category, a set of typologically representative non-residential buildings will be further identified, while a data driven cluster analysis will be carried out in order to define these non-residential archetypes. Moreover, the less energy efficient building sets will be identified by benchmarking the energy performance within the defined building categories, and the relative potential energy saving targets and pathways will be quantified and evaluated in order to provide policy support in improving energy efficiency of the poorly performing public buildings.

Flexibility in Buildings is an integral part of the solution to address the electrical grid’s challenges of grid balancing and renewable generation hosting capacity. One of the main barriers to greater participation of commercial and residential buildings in demand response schemes is the complexity and cost associated with assessing the flexibility of buildings. To overcome these barriers, an early stage flexibility assessment methodology was developed to provide stakeholders with actionable information in a concise and relevant way, so they can effectively evaluate the flexibility of their building and negotiate with aggregators for demand response participation. This paper validates the early stage flexibility assessment methodology in multiple buildings, demonstrating its ease of implementation and scalability. The validation was conducted at five pilot sites, in different geographical regions, activating a range of flexible sources through experiments on site.

Grey-box modelling is a system identification technique that combines parameter estimation methods to quantify the model parameters and physical knowledge to define the model structure. Consequently, the potential of grey-box modelling lies, in addition to the application of reduced-order models in e.g. control strategies or district simulations, in the characterization of the thermal properties of buildings. Nevertheless, the quality of the obtained model is governed by the dynamic information that is available in the training data. Thereby, the required accuracy of the models, and thus the requirements of the training data, differ from application to application. This paper analyses how the significant difference in dynamic behavior of a slow floor heating system compared to a highly responsive radiator heating system results in a variation of the time constants of the building that are excited by the system and therefore identifiable in the system identification process. The performance of grey-box models for prediction and simulations are contrasted for cases with radiators and floor heating and the physical interpretability of the model parameters is demonstrated. publisher: Elsevier articletitle: Impact of the Heat Emission System on the Identification of Grey-box Models for Residential Buildings journaltitle: Energy Procedia articlelink: http://dx.doi.org/10.1016/j.egypro.2015.11.740 content_type: article copyright: Copyright © 2015 The Authors. Published by Elsevier Ltd. ispartof: pages:3300-3305 ispartof: Proceedings of 6th International Building Physics Conference vol:78 pages:3300-3305 ispartof: International Building Physics Conference location:Turin date:14 Jun - 17 Jun 2015 status: published

In order to avoid grid instability and decreasing production efficiencies of large power plants due to a widespread integration of renewable electricity production, demand-side management (DSM) is proposed as a solution to overcome the possible mismatch between demand and supply. This research evaluates the potential to improve the balance between the electricity use for heating and local electricity production of a nearly zero energy building (nZEB), by active use of structural thermal storage capacity of the building. To quantify the DSM potential of structural thermal storage, the cover factors and peak electricity demand of a single family dwelling equipped with a photovoltaic (PV) system are chosen. Detailed representations of the PV system and the dwelling itself, heated by an air-water heat pump, are implemented in the modeling environment of Modelica and simulated for the heating-dominated climate of Belgium. The influence of the insulation level and the embedded thermal mass of the construction on the DSM potential is evaluated. The impact of the heat emission system is estimated by comparing a floor heating system with a radiator emission system. Results show that although the influence on the cover factors is limited, the use of the structural storage capacity for demand-side management shows strong potential to shift the peak electricity use for heating to off-peak hours. Furthermore, it is shown that not only the availability of the thermal mass, but also the interaction between the heating system and the thermal mass is of significant importance. publisher: Elsevier articletitle: Potential of structural thermal mass for demand-side management in dwellings journaltitle: Building and Environment articlelink: http://dx.doi.org/10.1016/j.buildenv.2013.03.010 content_type: article copyright: Copyright © 2013 Elsevier Ltd. All rights reserved. ispartof: Building and Environment vol:64 pages:187-199 status: published