An integrated optimization model covering the entire lifecycle of coal procurement, storage and consumption in a coal-fired thermal power plant was developed in order to optimize the coal-related production process, which accounted for 70%-80% of the total cost in power generation enterprise. A two-stage procurement model was formulated by incorporating thermal coal market information in the procurement stage. The coal demand for different coal quality was calculated based on the law of energy conservation through stepwise decomposition starting from the thermal energy required for power generation. The number of coal types and the matching between coal unloading and reclaiming quantity in the coal yard were considered in the storage stage. Dynamic programming was employed to optimize coal yard inventory when developing storage schemes, and an integer programming model for coal storage strategy was constructed. An expert coal blending model with multi-level constraints was established in the blending stage. The multi-level planning enabled rapid response during scheme formulation while ensuring that the coal quality requirement of the blending scheme was satisfied. The proposed models were validated through a demonstration application at a large grid-connected coal-fired thermal power plant on the southeast coast of China, yielding a significant improvement in operational efficiency. The equivalent net coal consumption rate was reduced by 1.05?g/(kW·h), and the estimated equivalent economic benefit was 11.60?million CNY per annum. Data flow and collaborative operation among the models at different stages were realized through a data platform. The procurement stage provided information on the quantity and quality of coal received, the storage stage determined the allocation of coal storage positions, and the blending stage guided the coal consumption plan. Then the dynamic balance of the coal yard was maintained, thereby significantly improving the overall economic performance of the coal-fired thermal power plant.