###### Abstract  
Due to the large-scale integration of renewable energy and the rapid growth of peak load demand, it is necessary to comprehensively consider the construction of various resources to increase the acceptance capacity of renewable energy and meet power balance conditions. However, traditional grid planning methods can only plan transmission lines, often resulting in low utilization rates of newly constructed lines. Additionally, static planning methods can only address single-target scenarios and cannot cope with dynamic growth in load and renewable energy. To address these issues, this paper proposes a multi-stage collaborative planning method for transmission networks and energy storage. This method considers the non-line substitution effect of energy storage resources and their characterization methods. It establishes the coupling relationship between resources across different planning stages to achieve coordinated multi-stage planning for transmission networks and energy storage. Based on the IEEE-24 node system and a case study in a northern province of China, the results show that the proposed method reduces investment costs by approximately 30% compared to static planning methods and by about 7.79% compared to conventional grid planning methods. Furthermore, this method can accommodate more renewable energy. transmission expansion planning; non-wires alternative; alternative energy storage for the power grid; multi-stage planning +
Footnote †: journal: Applied sciences  
promotion of the low-carbon transformation of the energy system, but also has an adverse impact on resource planning, system operation, and market transactions [6,7]. Traditional transmission grid planning aims to determine the optimal locations for new transmission lines to ensure that power loads are met safely under given planning objectives. As the grid evolves and the factors influencing its planning increase, the transmission grid planning model is also changing. In reference [8], the available transfer capability of the transmission grid is quantified and used as the objective function of the lower-level model. This complements traditional economic-optimal transmission network planning models. These models aim to enhance transmission efficiency by considering investments in wind power. Reference [9] addresses the impact of unpredictable climate changes on the system. It develops a robust expansion planning model for transmission networks that adapts to climate uncertainty. The model employs column generation to solve and demonstrate its ability to enhance operational safety under changing climate conditions. It also aims to reduce costs. Reference [10] introduces a scenario-driven expansion planning model for transmission networks, aiming to minimize costs in configuring the transmission network within the electricity market environment. However, the rapid rise in renewable energy proportions has significantly increased uncertainties in the system. This challenge may affect the effectiveness of these methods in future power system planning scenarios. With the large-scale integration of renewable energy, the demands on grid planning have changed. It is necessary to consider the handling of renewable energy uncertainty and aim to enhance renewable energy acceptance capacity. Consequently, scholars in the field have conducted extensive research. In reference [11], researchers propose a distributed robust optimization model, aiming to minimize transmission network expansion costs under uncertain conditions while maximizing penetration levels of renewable energy generation. Reference [12] introduces congestion metrics to reflect price signal changes in the market environment. Based on this, the study explores energy storage deployment, wind farm integration, and the impact of price signals. It develops a transmission expansion planning model under market conditions. Reference [13] addresses the transmission expansion planning problem for wind power integration.