To address transmission capacity bottlenecks of critical transmission corridors in large-scale hybrid AC/DC power grids and to accommodate greater renewable energy output at the sending end

thyristor controlled series compensation (TCSC) and static synchronous compensator (STATCOM) can be rationally deployed in the system. Accordingly

considering the uncertainties of renewable energy output

a multi-objective robust optimization model for the allocation of TCSC and STATCOM in large-scale hybrid AC/DC power grids is established

with the objectives of minimizing the annualized equivalent investment cost

maximizing corridor transmission capacity

and maximizing renewable energy accommodation capacity. First

the original mixed integer nonlinear programming model is transformed into mixed integer second-order cone programming model using convex relaxation technology

thereby improving computational efficiency and the quality of the obtained solutions. Then

the multi-objective robust optimization model is transformed into a series of single-objective optimization models using the normalized normal constraint algorithm. Subsequently

the column and constraint generation algorithm is used to decompose each single-objective model into a master problem and subproblem that are solved iteratively

yielding a set of Pareto-optimal solutions for the multi-objective optimization model under the worst-case uncertainty of maximum available renewable generation at renewable energy plants. The entropy weight method is further applied to identify a compromise optimal solution among the Pareto set. Finally

based on the calculation results of the modified IEEE 39-bus system and an actual large-scale AC-DC power grid

the effectiveness of the proposed model and algorithm is verified.