False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation.