Abstract: As global electricity demand grows, power grid supply chain management faces increasing challenges, particularly in demand fluctuations and supply disruption risks. This study proposes a DRO (distributionally robust optimization)-based inventory management model for power grid supply chains. The model innovatively integrates two production plants (local and remote) with multiple distribution stations into a unified supply network, employs moment-based ambiguity sets to describe demand disturbances, introduces expected constraints to characterize supply disruption risks, and proposes a goal-oriented service level concept to balance cost and service quality. A two-stage decision framework is constructed: the first stage determines production plans and local transportation strategies, while the second stage adjusts remote transportation strategies and service level shortfalls after demand and production conditions are realized. Using a column generation algorithm, numerical experiments demonstrate that compared to deterministic, sample average approximation, and robust optimization models, the proposed DRO model shows significant advantages in total cost and service level stability. Case study validation confirms the model's adaptability across distribution stations with varying demand characteristics, providing an effective decision support tool for power grid supply chain inventory management.