This research aims to participate in the efforts that have been carried out in the renewable energy deployment in Saudi Arabia, by conducting a research study that is aligned with the Vision of 2030. The goal of the vision is to substantially increase the share of renewable energy in the total energy mix, targeting the generation of 3.45 gigawatts (GW) of renewable energy by 2020 under the National Transformation Program (NTP), and 9.5GW by 2023, towards Vision 2030. This proposal introduces a distributionally robust planning model to determine the optimal allocation of hybrid wind and solar farms in a multi-area power system where the expected energy not served (EENS) is minimized under uncertain conditions of wind, solar and generator forced outages. Unlike conventional stochastic programming approaches that rely on detailed information of the exact probability distribution, the proposed method attempts to minimize the expectation term over a collection of distributions characterized by accessible statistical measures. Therefore, it is more practical in cases where the detailed distribution data is unavailable. This planning model is formulated as a two-stage problem, where the wind and solar power capacity allocation decisions are determined in the first stage, before the observation of uncertainty outcomes. The operation decisions are made in the second stage under specific uncertainty realizations. In the proposed method, the second-stage decisions are approximated by linear decision rule functions, so that the distributionally robust model can be reformulated into a tractable second-order cone programming problem. Case studies based on a five-area system are conducted to demonstrate the effectiveness of the proposed method. The correlation between the wind and solar power is investigated to capture the diversity and the availability of all included power resources.