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Assessing Grid-Accommodable Renewable Capacity with Small-signal Stability Constraints

The increasing penetration
of renewable energy resources via power-electronic converters
is turning the modern power grid into a multiple-converter
system (MCS). In a MCS, the dynamics of converter-based resources (CBRs) are different from those
of traditional synchronous machines, which poses
great challenges to the grid planning and operation. One of the
major challenges is the emerging small-signal stability problems resulting from
the interaction between CBRs and the power network. These small-signal
stability problems endanger the reliable and stable grid operation. To maintain
grid stability, the capacity of CBR generation in the grid may be limited, which impedes
the progress towards a sustainable future. To enhance the grid-accommodable
capacity (GAC) of renewable generation while maintaining grid stability, this
paper presents a semi-definite programming
(SDP)-based method to assess GAC with small-signal
stability constraints (GAC-SSSC) in a MCS. In the proposed method, the
small-signal stability constraints are formulated by the smallest
eigenvalue of a pertinent weighted Laplacian matrix of power network, so that
the assessment complexity of GAC-SSSC is significantly reduced for a MCS with large-scale CBRs.
It is theoretically proved that the derived SDP can find the optimal solution. The efficacy of the proposed method is demonstrated on
a 39-bus test system.