Renewable energy accommodation capacity is a core pillar of the transition to a low-carbon energy system, directly determining the achievement of emission-reduction targets and the success of energy transformation. Scientifically evaluating this capacity is key to enhancing it. To address the limitations of the entropy-weight method, this paper proposes an improved variable-entropy model for determining indicator weights. To tackle the rank reversal problem of the classical Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, a new weighted relative closeness degree is proposed. Using 31 provinces in China as research objects, an evaluation index system for renewable energy accommodation capacity was constructed, comprising 4 primary indicators and 10 secondary indicators, and the improved TOPSIS method was applied for comprehensive evaluation. The evaluation results verified the rationality and effectiveness of the proposed method from multiple dimensions. On this basis, policy recommendations are proposed from the perspectives of regional grid planning and cross-provincial accommodation coordination, providing theoretical support for enhancing the capacity for accommodating renewable energy.