The invention relates to the technical field of
new energy power generation and control, and discloses a
new energy station operation
decision support system based on multi-objective optimization, which comprises a multi-source
state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive
inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The
system collects
station data to construct a dimensionless
state space, constructs a Riemannian metric
tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection
strength factor; the factor is used to adaptively modulate a
virtual inertia matrix, and a dissipative Hamiltonian
kinetic model is constructed; and finally, solving the steady-state
generalized coordinates through a pungent-preserving numerical
integration algorithm, and decoding the steady-state
generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive
inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.