Enhancing the operational flexibility of dairy industrial parks: dual-state flexibility margin-driven cold-power synergistic reconfiguration dispatch strategy for electric trucks

Introduction Driven by the global energy transition and the “Dual-Carbon” strategy, the electrification of heavy-duty logistics has introduced massive mobile energy storage resources Electric Trucks (ETs) into dairy industrial park (DIP). However, the random charging/discharging behaviors of ETs, coupled with multi-energy flow coupling and conflicting stakeholder interests, exacerbate system peak–valley imbalances and operational instability. Methods To address these challenges, this study proposes three synergistic innovations: a Multimodal Operating Domain and Dynamic Collaborative Matrix Optimization Framework that quantifies equipment-level synergistic gains; a Dual-State Flexibility Margin-Driven Multi-Dimensional Flexible Dispatch Strategy with Cold-Power Synergistic Reconfiguration (DFDS); and an improved Multi-objective Beaver Behavior Optimization Algorithm (MBBOA) incorporating adaptive watershed partitioning, material quality assessment, and dam-health-driven maintenance mechanisms. The DFDS quantifies the schedulable potential of ETs into a user-perceivable cooling–power flexibility indicator (Dual-State Flexibility Margin, DFM), dynamically links it to charging/discharging electricity prices, and constructs coupled user decision models for pre-cooling and discharging behaviors. This enables proactive responses such as “high-DFM ETs charging off-peak and pre-cooling + discharging on-peak,” fully unlocking the flexible energy storage potential of ETs. Based on this, a two-layer “pricing–response–re-dispatch” closed-loop scheduling framework is established: the upper layer employs MBBOA to optimize energy trading prices among the DIP, energy producer, and energy manager; the lower layer uses GUROBI to solve optimal dispatch under multiple energy balances. Results Case study results demonstrate that: The integration of the multimodal framework and DFDS reduces the DIP’s comprehensive economic cost by 19.7% and shaves peak electrical load by 22.6%. Conclusion This research provides a replicable integrated solution for low-carbon, flexible, and intelligent energy management in energy-intensive industrial parks.