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The present study analyses the performance of a heating system controlled by a model predictive control strategy, where the impact of different combinations of thermal energy storage tank volumes and installed PV power capacities are analysed. The novelty of the paper lies in studying both economic and energy impacts of each equipment combination in different locations, buildings, and indoor occupancy schedules. The payback period, the reduction of the electricity grid consumption, and the behaviour of the coefficient of performance of the heat pump are studied in detail for all cases. Results point out that from an economic point of view, to invest in a thermal energy storage tank provides shorter payback periods in comparison to scenarios with PV panels, due to the high price of the solar elements. However, the energy performance analysis highlighted that the use of PV panels contributes to achieve up to 34%, 54%, and 90% of reduction of the electricity grid consumption in Helsinki, Strasbourg, and Athens, respectively. Finally, it is worth noting that the increase of the thermal energy storage volume improves the coefficient of performance of the heat pump. [Display omitted] • An analysis of the MPC behaviour at the energetic and economic level is presented. • The MPC strategy is implemented in different occupancy schedules and locations. • The aim of the MPC strategy is to reduce the final energy cost in all scenarios. • Different combinations of PV panels and TES tanks were assessed. • Combining TES, PV, and MPC contributed to enhancing the heat pump performance. [ABSTRACT FROM AUTHOR]

• "A Hierarchical Distributed Predictive Control Approach for Microgrids Energy Management". • Optimization of flexible buildings in smart microgrids. • An innovative distributed consensus-based MPC architecture with coordination. • Several experiments in a distributed energy laboratory facility. • Feasibility of realtime optimization proved. This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages and renewables. The overall goal is to provide a comprehensive and innovative framework to maximize the overall benefit, still accounting for possible requests to change the load profile coming from the grid and leaving every single building or user to balance between servicing those requests and satisfying his own comfort levels. The user involvement in the decision-making process is granted by a management and control solution exploiting an innovative distributed model predictive control approach with coordination. In addition, also a hierarchical structure is proposed, to integrate the distributed MPC user-side with the microgrid control, also implemented with an MPC technique. The proposed overall approach has been implemented and tested in several experiments in the laboratory facility for distributed energy systems (Smart RUE) at NTUA, Athens, Greece. Simulation analysis and results complement the testing, showing the accuracy and the potential of the method, also in the perspective of implementation. [ABSTRACT FROM AUTHOR]