Domain Generalizable Person Re-Identification (DG-ReID) strives to transfer learned feature representation from source domains to unseen target domains, despite significant distribution shifts. While most existing methods enhance model generalization and discriminative feature extraction capability by introducing Instance Normalization (IN) in combination with Batch Normalization (BN), these approaches still struggle with the overfitting of normalization layers to the source domains, posing challenges in domain generalization. To address this issue, we propose ReNorm, a purely normalization-based framework that integrates two complementary normalization layers through two forward propagations for the same weight matrix. In the first forward propagation, Remix Normalization (RN) combines IN and BN in a concise manner to ensure the feature extraction capability. As an effective complement to RN, Emulation Normalization (EN) simulates the testing process of RN, implicitly mitigating the domain shifts caused by the absence of target domain information and actively guiding the model in learning how to generalize the feature extraction capability to unseen target domains. Meanwhile, we propose Domain Frozen (DF), freezing updates to affine parameters to reduce the impact of these less robust parameters on overfitting to the source domains. Extensive experiments show that our framework achieves state-of-the-art performance among all the popular benchmarks. Code will be released upon publication.