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Optimized controls are particularly promising for flexible and efficient management of space heating and cooling systems in buildings. However, when controls are based on predictive models, their effectiveness is affected by the reliability of the models used. In this paper we propose a quantification analysis of some of the main uncertainty factors that can be observed in an optimal control really implemented in a building. A day-ahead optimal scheduling was applied to the heating system (composed of smart electric heaters with thermal storage) of a single room in an office building located in Osimo (Italy). The control algorithm is formulated to determine the charging periods of the heaters with the objective of minimizing the withdrawal of energy from the grid. The control takes into account the electricity produced by a photovoltaic plant and must maintain the internal air temperature close to an imposed setpoint.Firstly, the actual application of the control is shown during two selected days. Secondly, the analysis is extended to quantify the impact on the control performance of the prediction uncertainty of the input variables. The variable that has the greatest impact is the weather forecast and, specifically, the cloudiness index, which determines the solar gains. The different moment in time in which the weather forecast is predicted has proved to have a significant impact on the charging periods of the heaters (expected variation ranges from -50% to + 100%) and on the prediction of the indoor air temperature (variations observed up to 40%).

District heating (DH) network is a key infrastructure to decarbonize the heating sector through the centralized production of heat distributed to final users. The implementation of advanced control techniques is increasingly common in the field of energy optimization since they can provide a more efficient way of minimizing energy demand by appropriate scheduling of the control variables.The aim of this work is to present the application of two control strategies, i.e., Model Predictive Control (MPC) and Reinforcement Learning (RL), to a system based on a DH network supplied by a Combined Heat and Power plant (CHP-DH plant). The analyzed case study is a real CHP-DH plant operating in the small Italian town of Osimo (central Italy). The DH network currently connects more than 1200 users, generating peak heat demand of about 9.7 MWth. The heat generator is composed of a natural gas fueled internal combustion engine coupled with natural gas boilers.The work provides a comparison between the current control strategy (deduced from measured data) and the performance of the CHP-DH plant controlled with an MPC and an RL control. The results showed the effectiveness of the two controls in satisfying the thermal demand of the users, while minimizing the thermal losses towards the ground. Both MPC and RL allow to implement control strategies different from the current control in terms of supply temperature and flow rate circulating in the network. Referring to the winter months, in which the current operation of the system tends to prefer high supply temperatures, the advanced controls made it possible to reduce the thermal heat supply by reducing the thermal losses of about 3.9 % with the MPC and 6.54 % with the RL, corresponding to emission avoidances up to 23.3 tCO2 and 12.6 tCO2, respectively.The paper, as well as showing the application of the controls, contains a critical discussion of all the positive aspects and weaknesses found in the application of the MPC and the RL control to the case study.

The purpose of this paper is to identify and analyze the impact of flexibility enablers in cogeneration and district heating network (CHP-DHN) plants by means of a real case study located in central Italy. A wider definition of energy flexibility applicable to the entire energy supply chain (i.e. production, transport and usage) is used in this analysis. In particular the flexibility is intended as the capability of each part of the system to produce a variation in its load curve, while ensuring the required performance. In this sense energy efficiency technologies, the use of energy storage and advanced control techniques can be seen as flexibility enablers potentially available in each section of the energy system.The innovative contribution of this work is to propose flexibility strategies in compliance with the constraints imposed by both the managers and users. The study aims to show possible ways to activate flexibility services to be used with known instruments and to quantify their impact with a simulation-based approach. In particular, three different flexibility instruments are identified in different sections of the plant: (i) the use of a thermal energy storage (TES) in the generation side, (ii) the optimal management of the DHN supply temperature (energy distribution side) and (iii) the management of the thermostatically controlled loads (TCLs) of the final users (demand side) connected to the network. Through the implementation of simulation models calibrated with available measurements, the influence of these flexibility instruments on the energy/environmental performance is evaluated in comparison to the current configuration of the plant. Results confirm the great impact of the TES to increase the CHP working hours and, as a consequence, a primary energy saving increase is obtained in mid-season and in summer season. Whereas the optimal management of the water supply temperature in the DHN allows to obtain 1% fuel reduction in a typical winter week and 2% in a typical summer week. As far as the activation of the demand side flexibility is concerned, the effect of the management of TCLs on energy conservation is demonstrated: 1 °C reduction of the setpoint of all the residential users during a typical winter day produces a 7.3% reduction of the DHN thermal demand. However, its impact on the generation side (i.e. to reduce the electricity/thermal production of the CHP at specific times) is limited due to the characteristics of the considered CHP plant (the CHP engine is sized to cover only the thermal baseload and it scarcely affected by thermal demand variations). The analysis proposed helps to obtain valuable hints on unlocking the energy flexibility in CHP-DHN plants useful for a better management of such systems.