Coal Procurement Optimization in Coastal Power Plants

Coal-fired power generation remains prominent in China, particularly at coastal power plants, which receive low-cost coal from international suppliers through their ports. Thus, optimizing the coal procurement and transportation strategies at such plants is crucial for improving their operations. Considering a coastal power plant in China, this study introduces a mixed-integer linear programming model integrating factors related to coal combustion, supplier selection, and coal transportation. A machine learning-accelerated column generation algorithm is designed to solve this model. Numerical experiments demonstrate the effectiveness and computational efficiency of this algorithm in solving large-scale problems. Furthermore, a sensitivity analysis provides valuable managerial insights for such power plants, particularly regarding how operators should adjust coal procurement strategies under uncertain electricity demand. The findings reveal that optimizing the inventory allocation for different coals and the selection of coal transport vessels can reduce procurement costs. Validated by real-world data from CHN Energy Quanzhou and extensive sensitivity analyses, this study ensures high research rigor and robust decision support.