• Energy Research

In the evolving energy sector, where buildings are recognized as dynamic components of energy networks and smart grids, the implementation of new regulations and guidelines is crucial to optimize the interaction between buildings and the grid. The imperative to reduce building energy consumption facilitates the promotion of new technologies that rely on renewable energy generation and innovative materials. As technology has progressed, the multitude of energy vectors involved has made controlling energy systems increasingly challenging. This poses a barrier to the widespread adoption and implementation of cutting-edge technologies. In this framework, this paper explores an energy-efficient solution using an integrated photovoltaic/thermal collector and an active phase-change material storage system. The study optimizes the integration of technologies through a resistance capacitance model, assessing the impact on thermal comfort, energy savings and costs. A novel cascade methodology, combining particle swarm optimization search with model predictive control, is designed to select the optimal mode of operation for the proposed technologies. The economic feasibility of the proposed system is analyzed across different tariff structures, whereas the interaction with the grid is evaluated using energy flexibility key performance indicators. The energy and economic performance, as well as the flexibility of the system are assessed through a proof-of-concept conducted in an office building scenario. The results demonstrate an increase in energy efficiency, with savings ranging from 9% to 28% compared to a suitable baseline scenario, and a significant energy shift from on-peak to off-peak periods, potentially accounting for up to 46% of the total building load. This energy flexibility enables the grid to receive reduced demand during morning hours.

Abstract This paper presents an experimental and numerical study of predictive control of a hybrid ventilation system in an institutional building with emphasis on thermal comfort. A 17–story high institutional building with a large amount of exposed structural concrete is used as a case study to test different control strategies. A model of the entrance region of a typical floor is developed, calibrated from full–scale tests, and validated on a different set of measurements. It is used to estimate the heat removed from the concrete floor and the impact on thermal comfort. The system is currently operating based on heuristic control, with an exterior air temperature setpoint above which the air is allowed into the corridors, which are buffer spaces. With input measured data from 12 typical days, the model is used to compare this strategy to reactive and predictive control strategies. A control strategy based on data of the previous hour (reactive) is expected to quintuple the energy savings potential, but reduce thermal comfort. A predictive control strategy satisfies thermal comfort, but also increases energy savings potential. Lowering thermal comfort criteria during the night in order to precool the building further increases energy savings potential. A prediction horizon of 1 h and a control horizon of 15 min provide satisfactory thermal comfort, while keeping the computational time and forecast uncertainty to reasonable levels for a typical floor. This result provides the basis for the development of an integrated predictive control strategy for the whole building.