Abstract: Relying on solar thermal electric power generation, concentrating solar power (CSP) plant possesses the inherent capability to effectively manage the uncertainty of variable renewable energy generation and provide technical support for the construction of new power systems aligned with the objectives of carbon peaking and carbon neutrality. Nevertheless, the pivotal challenge confronting CSP plants pertains to overcoming the constraints of substantial construction expenses and strategizing for sustainable advancement. In this context, this study proposes a capacity optimization planning method targeting the subsystem capacity of the CSP plant, with a specific emphasis on incorporating the electricity market mechanism. Firstly, this paper addresses the imperative of augmenting the economic gains of the CSP plant under the operating time scale. By capitalizing on the adaptable regulatory traits inherent to the CSP plant, an innovative bidding strategy is outlined, allowing the CSP plant to partake in electricity markets as an influential price-maker. Then, a bilevel stochastic optimization model is constructed, centered on the concentration, heat storage, and power generation capacity ratios within the CSP plant. The discrete linearization method, which solves the nonlinear terms after model reconstruction, transforms the planning model into a mixed integer linear problem programming (MILP). Finally, the effectiveness of the proposed planning method is verified by an example simulation based on the actual historical data of a certain area in Northwest China, and the analysis shows that the bargaining power in the electricity market can make the CSP plant obtain more profit in comparison with a price-taker behavior.