Production Planning Optimization For Power-Intensive Plants Under Peak-Valley Electricity Price

Power-intensive manufacturing plants can utilize Peak-Valley electricity price to adjust production corresponding to different price time periods achieve lower electricity costs. This paper based on a real large air separation site, studied the optimization of the production plan by combining the actual production data statistical regression analysis with engineering mathematical model, to build the mixed-integer nonlinear programming (MINLP) power cost model, the model can predict the best optimal operating mode and the target lowest power cost against different demanding for products, thus the optimized power cost and incremental cost could be calculated and used as inputs for Master Production Scheduling, thereby a significant improving the production planning process comparing using average power consumption as basis. Implementing the new production planning and execution work process by using Lean Six Sigma methodology, with setting new KPI focus on reducing the gap of actual power cost and target power cost predicted by the optimization model, the preliminary results of satisfying economic benefit. The methodology developed in this paper is workable not only benefit the large air separation company, but also applicable to other power-intensive industries such as cement, chlor-alkali, aluminum etc. while it happens economic down turn and over-capacity in these industries currently and in the future.