Integrated modeling and optimization of coal supply chain and planning of thermal power plants based on MILP

Ensuring a sustainable and reliable electricity supply on a large scale requires the optimization of energy resources and the efficient management of supply chains. Coal-fired thermal power plants play a vital role in electricity generation due to their high production capacity, relatively low fuel cost, and operational reliability. Nevertheless, these systems face significant challenges caused by the complexity of coal procurement, transportation, storage, and blending processes, in addition to stringent operational and environmental constraints. Selecting the optimal combination of various coal types with different qualities and costs is essential for minimizing transportation expenses, improving combustion efficiency, and maintaining stable power generation.

Furthermore, production planning involves intricate scheduling decisions, including determining unit start-up and shut-down times, allocating generation loads, and meeting technical requirements such as minimum up and down times. When the coal supply chain and production scheduling are optimized separately, the resulting decisions are often uncoordinated, leading to inefficient resource utilization and higher total system costs.

To overcome these limitations, this study develops an integrated Mixed-Integer Linear Programming (MILP) model designed to simultaneously optimize coal blending, storage, transportation, and production scheduling in coal-fired power plants. The model incorporates detailed operational characteristics of generation units, including start-up and shut-down costs, desulfurization costs, storage capacity limits, and production capabilities. The objective function aims to minimize total system costs, encompassing production, transportation, storage, fuel treatment, and shortage penalties.

The proposed integrated optimization framework enables better coordination between the quantity and quality of coal supplied and the power generation requirements. The results demonstrate that this unified approach not only reduces total operational costs but also enhances fuel efficiency, lowers pollutant emissions, and improves the overall reliability and sustainability of electricity generation systems. This model can serve as an effective decision-support tool for planners and policymakers seeking to achieve efficient and environmentally responsible energy production.