• Energy Research

Abstract Low order grey-box models are suitable to be used in predictive controls. In real buildings in which the measured quantities are few the reliability of these models is crucial for the control performance. In this paper an identification procedure is analyzed to investigate the accuracy of different order grey-box models for short-term thermal behavior prediction in a real building, part of a living smart district. The building has a low number of zones and a single indoor temperature measuring point. The models are identified on the data acquired in 31 days during the winter 2015. The second order model shows the best performance with a root-mean-square error (RMSE) less than 0.5°C for a prediction horizon of 1-hour and a RMSE less than 1 °C for a prediction horizon of 3-hours.

Thermally activated buildings systems (TABS) are systems that integrate heating/cooling devices in the building structure, so that the building elements act as thermal storage and have an active role in the energy supply and demand management. Although TABS are well known systems, there are still open questions in their realization, mainly concerning appropriate control strategies which are influenced by the large thermal inertia. The purpose of this paper is to analyze the influence of demand side management control strategies on the performance of a thermally activated building system applied in a commercial building. The goal is to estimate the potential of TABS for load shifting requested by the electricity grid. The analysis is performed by means of a sample case: first the existing TABS control strategy and then the possible implementation of DSM mechanisms are analyzed. In particular three different demand side management mechanisms are evaluated: (i) a peak shaving strategy, (ii) a random request of switching on/off the system and (iii) a night load shifting strategy. The simulation results show high potential of TABS within the DSM framework, since TABS allow load control while scarcely affect thermal comfort.

Abstract In this paper, two innovative small-scale solar Organic Rankine Cycle (ORC) trigeneration plants are investigated and compared using a simulation analysis. In particular, the first plant (Plant 1) consists of a 146 m2 Compound Parabolic Collectors (CPC) solar field, a 3 m3 diathermic oil storage tank, a 3.5 kWe ORC plant and a 17 kWc absorption chiller, while the second plant (Plant 2) consists of a Linear Fresnel Reflectors (LFR) solar field of equal reflecting area, a phase change material storage tank equipped with reversible heat pipes, a 3.2 kWe ORC unit and the same 17 kWc absorption chiller as the former. The dynamic performance of the considered plants has been assessed for two Italian locations representative of the European Mediterranean area, Napoli and Messina, having a similar global radiation but a significantly different ratio of direct normal irradiance to diffuse irradiance. The comparison between the two different solar ORC trigeneration systems has revealed the great influence of the solar radiation on the effectiveness of such systems even for locations at similar latitudes. The energy production has been analysed both on a monthly and daily basis. Results have shown that while the performance of Plant 1 is not so sensitive to location and radiation conditions, Plant 2 is greatly affected by these parameters. Moreover, the higher condensing temperatures necessary in summer to supply the absorption chiller significantly limit the electrical efficiency of the solar CPC ORC. On the contrary, the LFR technology allows the achievement of higher temperatures and conversion efficiencies in summer, thus resulting especially suitable for solar cooling applications. In conclusion, this study has highlighted the importance of adequate technology selection with different radiation conditions in order to better exploit the potential of trigenerative solar ORC systems.

Thermal processes represent a significant fraction of industrial energy consumptions, and they rely mainly on fossil fuels. Thanks to technological innovation, highly efficient devices such as high-temperature heat pumps are becoming a promising solution for the electrification of industrial heat. These technologies allow for recovering waste heat sources and upgrading them at temperatures up to 200 °C. Moreover, the coupling of these devices with thermal storage units can unlock the flexibility potential deriving from the industrial sector electrification by means of Demand-Side Management strategies. The aim of this paper is to quantify the impact on the energy system due to the integration of industrial high-temperature heat pumps and thermal storage units by means of a detailed demand–supply model. To do that, the industrial heat demand is investigated through a set of thermal process archetypes. High-temperature heat pumps and thermal storage units for industrial use are included in the open-source unit commitment and optimal dispatch model Dispa-SET used for the representation of the energy system. The case study analyzed is Belgium, and the analysis is performed for different renewable penetration scenarios in 2040 and 2050. The results demonstrate the importance of a proper sizing of the heat pump and thermal storage capacity. Furthermore, it is obtained that the electrification of the thermal demand of industrial processes improves the environmental impact (84% reduction in CO2 emissions), but the positive effect of the energy flexibility provided by the heat pumps is appreciated only in the presence of a very high penetration of renewable energy sources.

Abstract This paper presents the comparison of three data driven models for short-term thermal behaviour prediction in a real building, part of a living smart district connected to a thermal network. The case study building is representative of most of the buildings of the tertiary sector (e.g. offices and schools) built in Italy in the 60s–70s of the 20th century. The considered building models are: three lumped element grey-box models of first, second and third order, an AutoRegressive model with eXogenous inputs (ARX) and a Nonlinear AutoRegressive network with eXogenous inputs (NARX). The models identification is performed by means of real measured data. Nevertheless the quantity and quality of the available input data, all the data driven models show good accuracy in predicting short-term behaviour of the real building both in winter and summer. Among the grey-box models, the third order one shows the best performance with a Root-Mean-Square Error (RMSE) in winter less than 0.5 °C for a prediction horizon of 1 h and a RMSE less than 1 °C for a prediction horizon of 3 h. The ARX model shows a maximum RMSE less than 0.5 °C for a prediction horizon of 1 h and a RMSE less than 0.8 °C for a prediction horizon of 3 h. The NARX network shows a maximum RMSE less than 0.5 °C for a prediction horizon of 1 h and a RMSE less than 0.9 °C for a prediction horizon of 3 h. In summer the RMSE is always lower than 0.4 °C for all the models with a 3-h prediction horizon. Other than typical control applications, the paper demonstrates that all the data driven models investigated can also be proposed as a powerful tool to detect some typologies of occupant bad behaviours and to predict the short-term flexibility of the building for demand response (DR) applications since they allow a good estimation of the building “thermal flywheel”.

Abstract 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 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.

The energy transition to a decarbonized energy scenario leads toward distributed energy resources in which end users can both generate and consume renewable electricity. As a result, several challenges arise in terms of decentralized energy resource management and grid reliability. With microgrids, the cooperation of distributed energy resources is improved, and with peer-to-peer energy exchange and demand response programs, better energy allocation and flexible management of consumption loads according to the needs of supply systems are achieved. However, effective peer-to-peer energy allocation and flexible demand management in microgrids require the development of market structures, pricing mechanisms, and demand response strategies enabled by a reliable communication system. In this field, blockchain offers a decentralized communication tool for energy transactions that can provide transparency, security, and immutability. Therefore, this paper provides a comprehensive review of key factors for peer-to-peer energy trading and flexible energy demand management in blockchain-enabled microgrids. The goal is to provide guidelines on the basic components that are useful in ensuring efficient operation of microgrids. Finally, using a holistic view of technology adoption as a tool for peer-to-peer communication in microgrids, this paper reviews projects aimed at implementing blockchain in energy trading and flexible demand management.