Baseline-Free Flexibility Aggregation and Target Power Tracking for Source–Load Coordination of Industrial Microgrid Clusters

Industrial microgrids are emerging as important providers of demand-side flexibility for active distribution networks. However, their participation in source–load coordination is hindered by complex production constraints, limited dispatch executability, and the widespread reliance on baseline-based demand response mechanisms. To address these challenges, this paper proposes a baseline-free source–load coordination framework for industrial microgrid clusters. A linear state–task network (LSTN) model is employed to characterize industrial production processes while preserving equipment operation, material balance, buffer storage, and production target constraints. Based on the feasible operating regions of individual microgrids, a simplified optimal adjustable load model (OALM) is developed at the aggregator level to identify and aggregate cluster-level flexibility boundaries without disclosing detailed production information. Building upon the aggregated flexibility region, a baseline-free target power tracking strategy is established, in which the distribution network issues absolute power targets and the aggregator coordinates multiple industrial microgrids to achieve their realization. The proposed framework simultaneously ensures production feasibility, scalable flexibility aggregation, and practical dispatch implementation. Case studies demonstrate that the aggregated industrial load can accurately track dispatch targets while satisfying all production constraints, thereby enhancing the capability of industrial microgrid clusters to participate in large-scale source–load interaction and renewable energy accommodation. Case study results show that when the number of industrial microgrids increases from 10 to 200, the total computation time increases from 5.08 s to 337.40 s, while the target tracking error remains within numerical tolerance. Compared with the conventional baseline-based virtual-battery (VB) method, whose root mean square error (RMSE) with respect to the intended absolute target reaches 241.30 kW under a ±10% baseline estimation error, the proposed method achieves near-zero tracking error. In addition, the production-agnostic aggregation model expands the flexibility boundary by 21.84% and generates targets that are not exactly executable under the full LSTN production constraints.