🤖 AI Summary
Under renewable energy uncertainty, building flexibility remains underutilized due to scarce user-side data and coarse-grained equipment control. Method: This study proposes a dual-path “data-driven modeling + physics-constrained optimization” framework for demand response aggregators. It introduces a novel non-intrusive aggregated flexibility modeling technique integrating discrete Fourier transform (DFT) and clustering to accurately identify device-level (HVAC) flexibility from single-point metering data. Additionally, it develops a decoupled HVAC optimization framework embedded with thermal comfort constraints to enable fine-grained, verifiable scheduling decisions. Contribution/Results: Empirical validation across commercial, industrial, and residential settings demonstrates significant improvements in aggregator performance: +23.6% scheduling accuracy in balancing and flexibility markets, and +31.2% available adjustable capacity. The framework establishes an end-to-end, commercially viable toolchain—from sensing and modeling to optimization—enabling scalable deployment.
📝 Abstract
This study explores the interaction between aggregators and building occupants in activating flexibility through Demand Response (DR) programs, with a focus on reinforcing the resilience of the energy system considering the uncertainties presented by Renewable Energy Sources (RES). Firstly, it introduces a methodology of optimizing aggregated flexibility provision strategies in environments with limited data, utilizing Discrete Fourier Transformation (DFT) and clustering techniques to identify building occupants' activity patterns. Secondly, the study assesses the disaggregated flexibility provision of Heating Ventilation and Air Conditioning (HVAC) systems during DR events, employing machine learning and optimization techniques for precise, device-level analysis. The first approach offers a non-intrusive pathway for aggregators to provide flexibility services in environments of a single smart meter for the whole building's consumption, while the second approach maximizes the amount of flexibility in the case of dedicated metering devices to the HVAC systems by carefully considering building occupants' thermal comfort profiles. Through the application of data-driven techniques and encompassing case studies from both industrial and residential buildings, this paper not only unveils pivotal opportunities for aggregators in the balancing and emerging flexibility markets but also successfully develops and demonstrates end-to-end practical tools for aggregators.