A novel multi-level predictive management strategy to optimize phase-change energy storage and building-integrated renewable technologies operation under dynamic tariffs

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.