Flexibility-Aware Transformer Capacity Planning for Industrial Parks via Cross-Dimensional Deep Reinforcement Transfer Learning

To exploit the operational flexibility of industrial parks in transformer capacity planning while addressing the expansion of controllable resources during system upgrades, this paper proposes a flexibility-aware bi-level planning framework based on cross-dimensional deep reinforcement transfer learning (DTRL). The upper level optimizes transformer capacity by minimizing the life-cycle cost, while the lower level formulates the coordinated operation of generation, flexible loads, and energy storage as a Markov decision process. To efficiently transfer scheduling knowledge from the existing system to an expanded configuration with additional control dimensions, a cross-dimensional DTRL framework integrating Monte Carlo scenario augmentation and elastic weight consolidation (EWC) is developed. Monte Carlo scenario augmentation enriches the limited operating samples available in the target domain, while the importance of pretrained network parameters is quantified using the Fisher information matrix. During fine-tuning, elastic regularization is introduced to preserve transferable scheduling knowledge and facilitate adaptation to newly introduced control dimensions. A case study of a typical industrial park shows that the proposed method achieves stable convergence under the expanded system configuration and reduces the planned transformer capacity from 1250 kVA to 1000 kVA while maintaining the maximum loading rate within the prescribed peak-loading constraint. Compared with the benchmark methods, the proposed approach reduces the annualized life-cycle cost by up to 1.9%, demonstrating the value of coordinated operational flexibility in balancing transformer capacity investment and operating economy.