• Energy Research

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 as...

Abstract 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.

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.

Abstract 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.

Abstract 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.

Abstract 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.

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.