AI 中文总结
该研究针对工业负荷建模中私有数据不可用的问题，提出基于逆优化的生产调度识别方法，利用智能电表数据建模，在钢铁粉末厂和水泥厂的测试中建模误差均低于8.5%。
AI 中文摘要
为了以成本效益的方式管理电力系统的供需平衡，可通过需求侧响应利用工业用户的灵活性。为了在需求侧响应期间尽量减少对工业用户生产的负面影响，通用模型如状态任务网络（STN）被广泛用于建模工业生产过程的能耗约束。然而，所需的模型参数无法设定，因为所需数据由工业用户私有且无法直接获取，这阻碍了工业负荷的准确建模。本文提出生产调度识别（PSI），一种在信息不完整情况下用于工业负荷建模的逆优化方法。在PSI中，利用工业用户的智能电表数据识别生产调度参数，从而解决私有数据不可用时的准确负荷建模问题。我们用改进的STN实现了PSI，并提出了一种实用算法以获得有效解。数值测试表明，PSI仅使用21天的每小时智能电表数据，即可以可接受的精度识别钢铁粉末厂和水泥厂的模型参数。与通过直接访问私有数据建立的准确模型相比，建模误差分别不超过8.5%和5.2%。
英文摘要
To cost-effectively manage the supply-demand balance of the power system, the flexibility of industrial users could be harnessed through demand-side response. To minimize the negative impact on the production of industrial users during demand-side response, general-purpose models such as the state-task network (STN) are widely used to model the energy-consuming constraints of industrial production processes. However, the required model parameters cannot be set because the required data are privately owned by industrial users and are not directly available, hindering the accurate modeling of industrial loads. In this paper, we propose production scheduling identification (PSI), an inverse-optimization-based approach for industrial load modeling under incomplete information. In PSI, industrial users' smart meter data are used to identify production scheduling parameters, thus addressing the problem of accurate load modeling when private data are unavailable. We implemented PSI with a modified STN and proposed a practical algorithm to obtain an effective solution. Numerical tests showed that PSI can identify the model parameters of a steel powder plant and a cement plant with acceptable accuracy, using only 21 days of hourly smart meter data. Compared with accurate models established with direct access to private data, the modeling error does not exceed 8.5% and 5.2%, respectively.
CommentsPublished in: IEEE Transactions on Smart Grid ( Volume: 16, Issue: 2, March 2025)
