Empowering Aggregators with Practical Data-Driven Tools: Harnessing Aggregated and Disaggregated Flexibility for Demand Response

TL;DR

The paper tackles the challenge of enabling robust Demand Response participation by aggregators under renewable energy uncertainty by delivering an integrated toolkit that combines aggregated and disaggregated flexibility strategies. It advances a data-driven approach using Discrete Fourier Transform ($DFT$) and DBSCAN to identify building-level patterns and baselines for aggregated DR, and develops a two-model HVAC framework plus a Virtual Power Plant (VPP) optimization to unlock device-level flexibility while preserving occupant comfort. Two real-world case studies—industrial buildings with single-meter data and a residential HVAC-driven VPP—demonstrate end-to-end applicability, supported by web apps that assist aggregators in decision-making and DR event management. The work contributes practical methods and tools that can enhance grid resilience, facilitate participation in balancing markets, and foster consumer engagement in DR programs through transparent, privacy-conscious, and comfort-aware strategies.

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.

Figures (7)

• Figure 1: Hourly energy consumption of each day during the month of January for Building 1.
• Figure 2: Clustering of hourly energy consumption using the DBSCAN algorithm.
• Figure 3: Energy consumption baselines for assessing flexibility potential.
• Figure 4: Interface of the web application showing the available VPP configuration options for aggregators.
• Figure 5: 3-hour ahead flexibility prediction for HVAC system of apartment 4.