In the present energy scenario, buildings are playing more and more as energy prosumers. They can use and produce energy and also actively manage their energy demand. The energy flexibility quantifies their potential to adjust the energy demand on the basis of external requests. The objective of this paper is to propose a method for buildings energy flexibility labelling at design conditions in the same fashion as the energy performance label. The flexibility quantification is based on the calculation of four flexibility parameters, which contribute to the definition of the Flexibility Performance Indicator. In order to assess the Flexibility Performance Indicator, buildings dynamic simulations are necessary and the boundary conditions (i.e. demand response event, representative day, comfort constraints) to be considered during the evaluation are provided as part of the proposed methodology. The method was applied to different Italian buildings, which differ for geographic location and design specifications and, in particular, the effects of building structure, heating/cooling systems and energy storage systems were compared. Results show that the climatic conditions affect the flexibility performance, while the building feature more relevant is the thermal mass of the building envelope, more than that provided by the distribution system. A sensitivity analysis to evaluate how the results are influenced by the proposed boundary conditions was also performed. Their choice confirms to have a relevant impact on flexibility quantification, then their unique definition has a paramount importance within this methodology.

District cooling systems (DCSs) are networks able to distribute thermal energy, usually as chilled water, from a central source to industrial, commercial, and residential consumers, to be used for space cooling/dehumidification. As cooling demand will increase significantly in the next decades, DCSs can be seen as efficient solutions to improve sustainability. Although DCSs are considered so relevant for new city developments, there are still many technical, economic, and social issues to be overcome to let such systems to spread out. Thus, this paper aims to highlight the advantages and issues linked to the adoption of DCSs for building cooling when cold is recovered from a specific application. A case study based on liquified natural gas (LNG) cold energy recovery from the transport sector is presented. Starting from the estimation of the free cooling availability, a DCS design method is proposed and the potential energy saving is investigated. Results show that a DCS using the cold waste derived from LNG can provide a relevant amount of electricity saving (about 60%) for space cooling compared to traditional solutions, in which standard air conditioning systems are installed in every building.

The implementation of model predictive controls (MPCs) in buildings represents an important opportunity to reduce energy consumption and to apply demand side management strategies. In order to be effective, the MPC should be provided with an accurate model that is able to forecast the actual building energy demand. To this aim, in this paper, a data-driven model realized with an artificial neural network is compared to a physical-based resistance–capacitance (RC) network in an operative MPC. The MPC was designed to minimize the total cost for the thermal demand requirements by unlocking the energy flexibility in the building envelope, on the basis of price signals. Although both models allow energy cost savings (about 16% compared to a standard set-point control), a deterioration in the prediction performance is observed when the models actually operate in the controller (the root mean square error, RMSE, for the air zone prediction is about 1 °C). However, a difference in the on-time control actions is noted when the two models are compared. With a maximum deviation of 0.5 °C from the indoor set-point temperature, the physical-based model shows better performance in following the system dynamics, while the value rises to 1.8 °C in presence of the data-driven model for the analyzed case study. This result is mainly related to difficulties in properly training data-driven models for applications involving energy flexibility exploitation.

The building sector represents one of the most energy-consuming worldwide and a great part of its consumption is accounted for residential demand for space heating and cooling. Although it is necessary to promote the buildings energy efficiency, energy flexibility is also of paramount importance to optimize the balance between demand and supply. In fact, an energy flexible building is defined as able to change, in a planned manner, the shape of its energy demand curve, electrical and thermal, while the comfort of the end-users is still guaranteed. Objective of this work is to exploit the energy demand management ability of different buildings composing a cluster, when their aggregated demand derived from electric heating systems (i.e. heat pumps) is subject to demand response (DR) strategies. Users with different occupancy profile are considered. By supposing to be able to activate the energy flexibility of the single building with thermostatic load control, different scenarios of cluster composition are evaluated in order to provide guidelines to implement optimal strategies for energy flexibility exploitation without drawback effects connected to the event.

District heating (DH) is an alternative technology to Individual Heating (IH) for satisfying end-user’s needs. This paper assesses the competitiveness of a DH network in the center of Italy from energy, environmental, and economic points of view considering both thermal power plant and end-users’ sides. On the thermal power plant side, the energy analysis considers the Primary Energy Saving (PES) and the specific energy (Esp) of the fuel actually exploited in the thermal power plant compared to its Low Heating Value (LHV), while the environmental analysis considers the avoided CO2 and the economic analysis considers the Energy Efficiency Certificates (EECs). Results showed that the current thermal power plant configuration with two boilers and a Combined Heat and Power (CHP) unit reaches a yearly PES of 21.3% as well as 1099 tCO2 avoided. From the economic analysis of the thermal power plant side, 829 EECs with an economic return of 207,222€ are obtained, while from the end-users’ side the DH network is cheaper than IH in 84.7% of the cases. Further technologies are also studied to enhance the CHP unit flexibility.