Optimizing appliances usage for apartment buildings participation in demand response programs

This paper presents a bio-inspired optimisation framework that enables apartment buildings to participate more effectively in demand response programmes by coordinating the use of household appliances while preserving residents' comfort. Developed within the context of the DEDALUS project, the proposed approach addresses the complexity of managing multiple apartments with heterogeneous appliances, demonstrating how collective flexibility can support grid stability without requiring the participation of every household. The proposed methodology formulates appliance scheduling as a bi-level optimisation problem, combining a Genetic Algorithm with the Harris Hawks Optimisation (HHO) algorithm to identify both the minimum number of apartments required for demand response participation and the optimal operating schedules for their appliances. The framework incorporates user preferences, appliance flexibility and comfort constraints to generate recommendations that closely match the target consumption profile requested by the distribution system operator while minimising disruptions to residents' daily routines. The approach is validated using data collected from a residential apartment building, demonstrating its ability to closely approximate target energy consumption profiles while involving only a subset of apartments. The experimental results highlight the algorithm's convergence, scalability and effectiveness in balancing energy flexibility with occupant comfort, providing a practical decision-support tool for integrating multi-apartment residential buildings into future smart grid and demand response ecosystems.