Optimal inertia allocation in networked DC microgrids with guaranteed steady-state and small-signal stability

DC microgrids (MGs) are considered an efficient solution for integrating distributed energy sources. In these networks during dynamic power imbalances, the stored energy in the capacitor of DC bus provides the natural tendency to resist voltage changes, equivalent to the physical inertia of rotating machines to resist frequency changes in AC networks. However, the limited capacitance of the DC bus leads to an intrinsic low-inertia, which threatens the voltage security under frequent disturbances. Although inertia emulation strategies have been developed in DC grids to mitigate the above, there is a lack of virtual inertia allocation method for complex networked DC MGs, resulting in a potential system instability and undesired costs. To address this gap, a steady-transient-coordinated bi-stage optimization framework is proposed to enhance the grid performance during different periods. To guarantee the steady state operational requirements of DC MGs, a mixed-integer conic programming is developed in the first stage to design the damping constant, providing equilibrium points for small-signal modelling. On this basis, an ℋ 2 -index-based stability-constrained optimal inertia allocation model is established to enhance global voltage dynamics under disturbances. To capture the implicit feature of the stability constraints, a data-driven method is employed to learn and reformulate the model in a tractable mixed-integer linear manner. Case studies on a 9-bus DC-powered trolleybus system, a 14-bus radial DC microgrid and a 30-bus network are conducted to verify the effectiveness of the proposed method.