Identification of Electricity Usage Boundaries and Carbon Reduction for Energy-Intensive Enterprises

Energy-intensive enterprises significantly contribute to industrial carbon emissions, necessitating advanced optimization strategies to mitigate their environmental impact. This paper introduces a boundary-based carbon reduction method, leveraging historical operational data to establish dynamic load boundaries and incorporating multi-period scheduling with real-time carbon emission factors. A constrained nonlinear solver optimizes scheduling across daily and weekly horizons, ensuring engineering feasibility while minimizing carbon emissions. The model dynamically adjusts operational loads based on historical patterns and current carbon intensity, enabling practical low-carbon operations. Simulation results demonstrate the model’s superior capability in load boundary extraction, representation, and validation, highlighting its engineering applicability and practical potential for emission reduction scheduling in energy-intensive scenarios.