Flexibility sharing for collaborative load regulation: Mechanism and solution

Meng, Yan, Yan, Lei, Li, Zuyi, Shahidehpour, Mohammad, Zhang, Yi, Mei, Linjuehao, Shen, Baiqiang and Wang, Jiaying 2026. Flexibility sharing for collaborative load regulation: Mechanism and solution. Applied Energy 420 , 128151. 10.1016/j.apenergy.2026.128151

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Abstract

High regulation costs and insufficient target tracking performance of current load regulation hinder full exploitation and reliable delivery of demand-side flexibility. To address the limitations, this paper proposes a target-oriented flexibility sharing mechanism and with an efficient solution methodology for cost-effective collaborative load regulation. The proposed mechanism characterizes shared commodities as the deviation vector between post-regulated load and the target curve, encompassing both flexibility demand and supply. Relative to the target curve, the flexibility contribution of customers is fairly quantified and their sharing roles are explicitly determined. Towards economically efficient flexibility allocation, an optimal clearing model is formulated to minimize total supply costs with flexibility supply-demand balance strictly satisfied to ensure perfect target tracking. Time-variant per-unit flexibility values are revealed and utilized as the settlement sharing price, with non-negative sharing surplus fairly allocated based on cost causation principle. To achieve optimal outcomes while preserving customer privacy, a synchronous distributed algorithm based on ADMM is developed to decompose the clearing task to individual customer. Computational efficiency is enhanced through a warm-start strategy utilizing solutions from a relaxed clearing model without individual constraints as the initial iteration point. Simulation tests verify the effectiveness of the proposed mechanism in reducing regulation cost and improving target tracking performance. The proposed solution method demonstrates satisfactory computational efficiency and accuracy in both small-scale and large-scale scenarios.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Business (Including Economics)
Publisher:	Elsevier
ISSN:	0306-2619
Date of Acceptance:	28 May 2026
Last Modified:	29 Jun 2026 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/187781

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