Factsheet: Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems (PDF 0.31MB) 
May 2024

Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision 
September 2024
Active building energy management can facilitate the development of low-carbon buildings and support flexible operations of future smart cities, thanks to advancements in digitalization To fully leverage these benefits, it is essential to integrate diverse objectives and engage multiple stakeholders. However, a gap remains in comprehensive field insights into emission reduction, flexibility provision, and user impacts. This study examined how a real occupied building, with all its energy assets, could function as an emission-aware prosumer with flexible energy consumption. An existing building energy management system was enhanced by integrating a model predictive control strategy. The setup reduced equivalent carbon emissions from electricity imports and provided flexibility to the energy system. The experimental results indicate an emission reduction of 12.5% compared to a rule-based controller that maximized PV self-consumption. In addition, a minimal flexibility provision experiment was demonstrated with a locally emulated distribution system operator. The results suggest that flexibility was provided without the risk of rebound effects, as flexibility was quantified and communicated to the system operator in advance. This study demonstrates the feasibility of low-carbon buildings and their support for flexible energy systems, while also identifying and discussing practical scalability challenges.

A study on price responsive energy flexibility of an office building under cooling dominated climatic conditions 
August 2024
Flexibility in buildings is a low-cost alternative to support the electricity network with high penetration of variable generation. There is a limited understanding of energy flexible behaviour of the buildings in response to electricity market signals. In this paper, the price-responsive flexible behaviour of a commercial building with a cooling system has been studied. A first-order virtual battery model of the building and flexibility function-based approach have been used to understand the flexible behaviour of the building to varying price signals. The price signals delivered changes in building electricity demand profile, altering cooling temperature set points. The building structural thermal storage capacity was found to vary depending on demand changes. Low price signals provoke positive demand changes with respect to the baseline demand, taking the system to charging mode. Similarly, high price signals lead the system to discharging mode, reducing the instantaneous charging condition of the system. HVAC operating states, e.g., free cooling/economiser cooling and mechanical cooling, have been found to have a notable impact on electricity demand flexibility. The flexibility function shows that for a very low-price value, the cooling setpoint approaches the lowest comfort bound, and demand increases significantly. A deviation between the maximum and minimum electricity demand of 207 kW for the studied building system gives an estimated maximum energy flexibility capacity of 7248.2 kWh. The response of the building to variable price signals yields flexible demand, delivering cost savings of 13 %.

Stochastic occupancy modelling for spaces with irregular occupancy patterns using adaptive B-Spline-based inhomogeneous Markov Chains 
June 2024
This paper presents a discrete time, discrete state-space in-homogeneous Markov Chains model for stochastic occupancy modeling in spaces with irregular occupancy patterns. The goal of the model is to provide accurate predictions of occupancy numbers, enabling appropriate actions to be taken for HVAC system to maintain optimal indoor environment. The proposed Markov Chain model incorporates time in-homogeneity by coupling the time-varying model parameters using a Periodic B-Spline expansion with adaptive knots, which effectively captures patterns in occupancy activity. This method optimizes the distribution of knots based on specific occupancy characteristics observed in different types of rooms. To evaluate the effectiveness of the proposed method, six months of occupancy data collected from a meeting room are utilized. A comprehensive comparison is conducted between the proposed adaptive B-Spline method and other approaches, including the counting method and uniform B-Spline method. The comparison considers both model accuracy and complexity, using metrics such as the Akaike Information Criterion and Bayesian Information Criterion. Results indicate that the proposed model achieves more accurate predictions with fewer model parameters compared to other methods. These forecasts are particularly useful in optimizing the control of HVAC systems, where accurate predictions of future occupancy numbers are essential.

Data-driven predictive control for demand side management: Theoretical and experimental results 
January 2024
Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies.

Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation 
December 2023
Publisher: Energy and Buildings
This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.

Quantification of the energy flexibility of residential building clusters: Impact of long-term refurbishment strategies of the italian building stock 
October 2023
Publisher: Energy and Buildings
A major refurbishment of the building stock is necessary to achieve the objectives of the energy transition. In addition to decreasing the overall energy demand, the energy efficiency of buildings can create a non-negligible reserve of flexibility and resilience for the entire energy system. Long-term refurbishment strategies can have an impact on such potential of the building sector that is still not widely exploited.

In this work the objective is to quantify the influence of long-term refurbishment strategies, planned until 2050, on the energy flexibility reserve of the entire building stock. Reference clusters of residential buildings have been modelled to represent the current and future scenarios of the Italian building stock. Lumped parameter models representing archetypes of residential buildings are implemented to represent the Italian building stock. Current statistics on the composition of the building stock have been combined with European refurbishment targets to 2050 to define the current and future scenarios of the Italian building stock.

Since the topic of quantifying the energy flexibility of clusters of buildings is still rather open, this study proposes an analysis based on a combination of different indicators derived from the literature and proposed ad hoc by the authors. They include flexibility curves, that correlate the demand of the cluster to the penalty signal (e.g., a price signal), and flexibility indicators for the comparison between the scenarios with and without activation of energy flexibility.

The results quantify the impact of Italian building stock refurbishment strategies on flexibility reserve and efficiency targets. It has been estimated that the maximum electrical power shiftable (both upward and downward) by activating the energy flexibility of the whole building stock can reach 17.9 GWe in 2050. While in terms of energy, the following amounts of average daily shiftable energies have been obtained: from -34.4 to + 13.6 GWhe in 2030, from -75.4 to + 16.2 GWhe in 2040 and up to -113.5 to + 45.8 GWhe in 2050, that represent around 2% of the present Italian electricity demand.

A multi-agent system based coordinated multi-objective optimal load scheduling strategy using marginal emission factors for building cluster demand response 
February 2023
Publisher: Energy and Buildings
Building cluster load management to harness energy flexibility and reduce both electricity cost and carbon emissions is an important but inadequately addressed issue in the context of carbon neutrality. This study develops a multi-agent system (MAS) based coordinated optimal load scheduling strategy for building cluster load management in response to dynamic electricity price and marginal emission factor (MEF) simultaneously. The strategy effectively solves the multi-objective optimization problem of conflicts, i.e., minimizing the electricity cost, carbon emissions and peak load while maintaining a good level of users’ satisfaction with electricity use quantified by a utility function. Case study on a campus building cluster is carried out to test the strategy developed. Three demand response (DR) schemes are designed for the building cluster, i.e., price-based DR, MEF-based DR, and the price and MEF hybrid-based DR which implements the optimal scheduling strategy developed. In addition, two real scenarios with opposite correlations between dynamic electricity price and MEF, i.e., positively correlated (scenario 1) and negatively correlated (scenario 2), are extracted from German electricity market. The electricity costs, carbon emissions, peak loads, and utility of the three DR schemes in the two scenarios are critically compared. The results show that the price-based DR may result in the rise of carbon emissions, and the MEF-based DR may lead to higher electricity cost, depending on the correlation between dynamic electricity price and MEF. The optimal strategy developed can achieve a compromise between the conflicting objectives in both scenarios. Under the extremely disadvantageous situation like scenario 2, where the trends of the price and MEF are completely opposite, the price-based DR results in an increase of carbon emission of 2.78%, and the MEF-based DR leads to an increase of electricity cost of 2.63%. The hybrid-based DR can reduce the peak power by 5.54% without increasing electricity cost and carbon emissions in scenario 2. This research provides an effective optimal load scheduling strategy as well as the application guideline for building cluster DR management towards decarbonization and economic benefit.

Ten questions concerning energy flexibility in buildings 
August 2022
Author(s): Rongling Li, Andrew J. Satchwell, Donal Finn, Toke Haunstrup Christensen, Michaël Kummert, Jérôme Le Dréau, Rui Amaral Lopes, Henrik Madsen, Jaume Salom, Gregor Henze, Kim Wittchen
Publisher: Building and Environment
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.

Demand-side flexibility in a residential district: What are the main sources of uncertainty? 
January 2022
With the increasing share of intermittent renewable energy sources in the energy mix, demand-side flexibility is likely to play a key role in the future. For buildings, flexibility is defined as the ability to shift their energy consumption away from “peak periods” i.e. high-demand periods of the electrical network. In France, these episodes occur mainly during the wintertime due to the significant demand for space heating. To achieve flexibility objectives, we explore an indirect control strategy at district scale by adjusting the dwelling thermostat during peak periods. The study is conducted over 337 dwellings in order to better predict the load curve by taking advantage of the aggregation effect. Three main research questions are addressed in relation to the assessment of flexibility potential: (i) the effect of aggregation, (ii) the identification of the most influencing factors, including occupant behavior, and (iii) the quantification of uncertainties. Using an urban building energy modeling tool populated with various national data sources (building envelope, energy class of equipment, etc), we perform a sensitivity analysis on 22 parameters representing the geometry, the appliances, the building characteristics, the occupants, and the grid. The output indicator is the average power shifted during the flexibility (or demand response) event. From this analysis, 7 parameters appear as being the most influential. A regression analysis on these parameters is performed, depending on both the duration of the event and the typology of district. The results show that the duration of the flexibility event and the occupant pre-selected temperature change are the most influential parameters. It results to approximately ± 90 W of uncertainty on an average potential of 290 W of shiftable power per household in a recent district. Furthermore, the occupants are highlighted as making a significant contribution to flexibility. Finally, we observed that the thermal properties investigated with the study of an old fabric district play a key role. Low thermal performance means high heating consumption and increased flexibility potential, but a similar relative uncertainty.