Aggregated Prosumer Flexibility: An Integrated Approach from Data to Effective Demand Response Program
| dc.contributor.author | Yuan, Rui | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The global transition to 100% renewable-powered electricity grids faces a fundamental challenge: managing the inherent variability of renewable generation. Although Demand Response (DR) has been recognised as a crucial solution since the early 2000s, its true potential remains largely unrealised in daily grid operations. The recent proliferation of behind-the-meter (BTM) technologies, including residential batteries, electric vehicles, and rooftop solar systems, has transformed everyday electricity consumers into “prosumers” with significant potential for providing grid flexibility. However, effective implementation of DR faces multiple interconnected challenges. These include the absence of accurate, interpretable and dynamic identification methods for BTM applicants, limited access to reliable, diverse datasets representing modern prosumer behaviour; inadequate mechanisms for accessing highresolution consumer data in real time due to privacy concerns and lack of stakeholder motivation; and the limitations of conventional price-based control approaches in effectively mobilising BTM flexibility. Despite numerous global research initiatives and trials, most existing DR programs still rely on traditional direct load control methods, failing to capitalise on advanced technologies and the expanding prosumer resource base. To overcome these challenges, this research makes several novel contributions. First, we develop and validate a shape-based methodology for identifying prosumer types using low-resolution smart meter data in an interpretable manner, achieving superior accuracy with lower time and space complexity compared to existing and intuitive approaches. We extend this framework to create a dynamic, memory-efficient solution that enables real-time user classification. Building on these advances, we introduce an AI-based approach incorporating domain knowledge to synthesise large-scale residential user data, demonstrated using Danish consumer data, but adaptable to other regions globally. Second, we propose a Codebook and shape-based technique to address the limitations of low-resolution, offline BTM data while preserving consumer privacy. This costeffective solution enables greater access to valuable consumption data without compromising security. Third, we introduce a new flexibility metric for fair quantification of prosumer contributions and develop an innovative DR approach that outperforms conventional price-based methods while avoiding the drawbacks of direct load control. The practical applicability of our frameworks is validated through industrial collaboration with Watts A/S, a Danish smart grid service provider. The results demonstrate significant improvements in the accuracy of the prosumer classification, the quality of data synthesis, and the effectiveness of the DR program. This thesis establishes a comprehensive framework for harnessing BTM flexibility while addressing critical challenges in data security, system operator trust, Demand-Side Management (DSM), and user fairness, key elements for enabling the transition to renewable-powered smart grids. | |
| dc.description.dissertation | Thesis (Ph.D.) -- University of Adelaide, School of ENTER SCHOOL, YEAR | en |
| dc.identifier.uri | https://hdl.handle.net/2440/146219 | |
| dc.language.iso | en | |
| dc.provenance | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals | en |
| dc.subject | Demond response | |
| dc.subject | demand side management | |
| dc.subject | smart grid | |
| dc.subject | data mining | |
| dc.title | Aggregated Prosumer Flexibility: An Integrated Approach from Data to Effective Demand Response Program | |
| dc.type | Thesis | en |
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