A novel symplectic-geometry and Hamiltonian-dynamics-based multi-energy flow cooperative optimization framework for the dairy eco-industrial park considering multi-path biomass energy

Dairy eco-industrial parks face urgent deep-decarbonization imperatives, yet the strongly coupled nonlinear evolution of multi-energy flows and the spatiotemporal randomness of electric heavy trucks driven by distribution-based arrival times, initial states of charge, and energy consumption perturbations present significant operational challenges. Addressing the research gaps in heterogeneous energy-mass flow modeling and industrial-transportation co-optimization, this paper introduces symplectic geometry and Hamiltonian dynamics to develop a multi-energy-flow coordination optimization model, and proposes a multi-path biomass energy utilization and mobile energy storage multi-modal co-optimization intelligence decision framework for industrial-transportation integration in the dairy park. The framework maps the energy-mass network and heavy truck behaviors into a regularized phase space governed by a canonical symplectic-matrix regularized evolution equation to preserve energy-mass invariants. Furthermore, a waste-driven vehicle-coupled topology with three biomass conversion pathways is constructed, supported by a multi-branch mode adaptive regulation mechanism that balances economics, carbon reduction, and supply security. Case studies demonstrate that the interaction between multi-path biomass utilization and localized vehicle-to-grid autonomy increases the park's electricity self-sufficiency to 78.1% and reduces carbon emissions by 7.4%, while the adaptive regulation mechanism further decreases system operating costs by 5.0% and ensures resilient energy-mass supply under emergency conditions.