Abstract: In the context of electricity market liberalization and the low-carbon transition in energy consumption, high-energy-consuming industrial sectors, such as coal mining enterprises, urgently need to optimize their electricity consumption strategies to reduce production costs. Traditional optimization methods often neglect the scheduling of specific large equipment and the uncertainty in electricity prices, resulting in high production costs, low energy efficiency, and inflexible strategies that difficult to effectively respond to price fluctuations. An optimization method for the operational scheduling of critical equipment in fully-mechanized coal mining is proposed, which accounts for the uncertainty of electricity prices. The method employs electricity market pricing-based mechanisms to dynamically adjust power consumption strategies, thereby effectively controlling the energy use and costs. Firstly, Bayesian optimization theory and an ARIMA time-series model are employed to forecast day-ahead electricity prices. Based on Bayesian predicted prices, key price scenarios simulating price fluctuations, are obtained through Latin Hypercube Sampling and K-medoids clustering. Subsequently, a bi-level optimization model is developed, integrating stochastic programming with Information Gap Decision Theory (IGDT). The upper level focuses on cost optimization through stochastic programming to determine the optimal production strategy under the key price scenarios. The coal output constraints for each mining process are reformulated as constraints of shearer operating distance, linking electricity prices, traction speeds, and coal output. A nonlinear speed-time function model for the shearer is then proposed. The lower level defines the fluctuation range of uncertain electricity price parameters, derives robust electricity price scenarios, and introduces a cost deviation coefficient to adjust the expected cost, performing robust optimization based on IGDT for each scenario. Through iterative solving of the two levels, the speed strategies for each period and the time strategies for various processes are gradually optimized and fine-tuned, ensuring both economic efficiency and robustness under varying price uncertainties. A model was developed based on a real coal mine in Changzhi, Shanxi Province, China, incorporating actual comprehensive mining processes and pricing data. The case simulation results indicate that the optimizing strategies through this model significantly reduce electricity costs. Furthermore, as the uncertain electricity price parameters increase from 0.1 to 0.3 and then from 0.3 to 0.5, the fluctuation in electricity costs under the traditional “three-eight” model remains minimal at 6×10⁻⁶, while the fluctuations under the enhanced “three-N” model are 1.30% and 1.88%, respectively. The two models exhibit strong robustness and stability, even as price uncertainty increases.