A power system unit commitment (UC) problem considering uncertainties of renewable energy sources is investigated in this paper

through a distributionally robust optimization approach. We assume that the first and second order moments of stochastic parameters can be inferred from historical data

and then employed to model the set of probability distributions. The resulting problem is a two-stage distributionally robust unit commitment with second order moment constraints

and it can be recast as a mixed-integer semidefinite programming (MI-SDP) with finite constraints. The solution algorithm of the problem comprises solving a series of relaxed MI-SDPs and a subroutine of feasibility checking and vertex generation. Based on the verification of strong duality of the semidefinite programming (SDP) problems

we propose a cutting plane algorithm for solving the MI-SDPs; we also introduce an SDP relaxation for the feasibility checking problem

which is an intractable biconvex optimization. Experimental results on the IEEE 6-bus system are presented

showing that without any tuning of parameters

the real-time operation cost of distributionally robust UC method outperforms those of deterministic UC and two-stage robust UC methods in general

and our method also enjoys higher reliability of dispatch operation.