A rule-based predictive control framework for market and grid-oriented operation in thermally activated buildings

The integration of renewable energy into power grids introduces instability due to generation intermittency, necessitating demand-side flexibility strategies. Building-grid interaction, particularly for thermally activated building systems, offers a solution by dynamically adjusting power demand. These buildings exploit thermal mass of their structural elements, such as concrete slabs with embedded water pipes, to shift thermal loads. This study investigates an all-electric office building with concrete core activation, employing a rule-based control strategy and introducing a rule-based predictive control framework to optimize market-oriented and grid-oriented operations. A co-simulation environment integrating a building performance simulation tool with numerical optimization is developed. Results show that the rule-based predictive control improves cost savings by 2.2% compared to rule-based control while ensuring grid support, achieving a load matching grid index of 88.6% during congestion events. The proposed approach enables sequential demand flexibility, balancing economic efficiency and grid stability while offering an alternative to advanced control algorithms.