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.