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The concept of shared energy storage in power generation side has received significant interest due to its potential to enhance the flexibility of multiple renewable energy stations and optimize the use of energy storage resources. However, the lack of a well-set operational framework and a cost-sharing model has hindered its widespread implementation and large-scale development. This paper introduces three distinct cost-sharing methods, namely the uniform allocation method, the predictive weighted allocation method, and the dynamic weighted allocation method, which are laterly applied to develop a combined operational and cost distribution model. The objective is to improve the efficiency of the power generation system by incorporating shared energy storage assistance and allocating the associated costs based on the use of various renewable energy stations. Furthermore, the viability of the suggested operational framework for shared energy storage and the methods for allocating costs are confirmed through numerical simulation. The findings indicate that the uniform allocation method represents the most straightforward approach for distributing costs evenly. In contrast, the second model introduces a cost allocation method that addresses the issue of fairness by considering the predicted generation level of each renewable energy plant. However, the third model takes into account the actual generation level of the plants when determining their respective costs. Sensitivity analysis is further conducted to offer valuable insights into cost-saving policies for four representative regions in China. The proposed operation and cost-sharing model is anticipated to serve as a useful reference for the widespread implementation of shared energy storage in power generation side. • Design three cost-sharing methods to allocate costs among various entities. • Formulate a combined operational and cost allocation model. • Optimize operational and cost allocation strategies for various entities. • Reduce total costs by up to 36% through the dynamic weighted allocation method. [ABSTRACT FROM AUTHOR]