Driven by the “dual-carbon” goals, the establishment of integrated energy systems in industrial parks, and the large-scale renewable integration have imposed heightened flexibility requirements on coal blending for thermal power units. The coal blending process comprises two stages: pre-furnace and in-furnace operations. During pre-furnace blending, a minimum coal quality deviation model addresses low-calorific-value coal utilization. Chaos search-based adaptive mutation particle swarm optimization blends such coal into furnace-compliant mixtures meeting boiler specifications. For in-furnace blending, dynamic adjustment of coal ratios across load ranges ensures load stability while minimizing fuel costs. A two-stage optimization model resolves circulating fluidized bed (CFB) boiler blending: Stage 1 selects coal feeder combinations according to weekly peak chemical plant loads and PV generation scenarios; Stage 2 optimizes coal feed rates under load-balance constraints, incorporating desulfurization-driven sulfur content limits. Comparative analysis under spring irradiance conditions reveals that in-furnace blending of two coals reduces daily combustion costs by 4.36×10⁵ yuan. Post-retrofit evaluation of blending of three coals demonstrates a further reduction in daily fuel costs.