Abstract
For industrial manufacturing to meet the country’s carbon neutrality goals by 2026, energy efficiency optimization is becoming a crucial development orientation. Demand response has played a significant role in improving energy efficiency and balancing supply and demand. The dynamic industrial environment requires the high level of precision and reliability of demand response. Reinforcement learning methods can make more accurate judgments and optimal decisions in response to complex dynamic environments compared with traditional optimization methods, and meet the relevant requirements of demand response. The paper studies cement manufacturing, a typical energy-intensive industry. As a first step, an in-depth modeling analysis of cement manufacturing’s main energy-consuming equipment is conducted based on industrial load characteristics. Then, industrial demand response scheduling methods based on a reinforcement learning algorithm are designed. The effectiveness and feasibility of the scheme are verified by simulation experiments.
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