@inproceedings{8de88b95896d47e5b81d60e3539c15ab,
title = "A Demand Charge Model for Energy-intensive Enterprises based on Model Predictive Control",
abstract = "The emission reduction of energy-intensive enterprises has become increasingly important due to the goal of carbon dioxide peaking. While ensuring economic performance, how to reduce the demand charge is an important part to optimize electrical consumption. However, the problem is hard to consider in the short-term period because the monthly demand charge may increase due to the previous strategies. Based on the peak load shifting method, this paper proposes a short-term scheduling problem with the measurement of the slip type demand which combines the short-term forecast data with day-ahead forecast data to introduce the effect of future demand. The problem is formulated into a mixed integer linear programming problem with the objective of minimizing the economic cost. Numerical results show the demand charge can be reduced by 7.97\% with the short-term scheduling strategies, and the forecast demand can be well used in short-term regulation decisions.",
keywords = "demand charge, demand control, load forecast, short-term scheduling",
author = "Yifei Geng and Feng Gao and Kun Liu and Xiangxiang Dong and Bin Xiao and Zhenxin Gao",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",
year = "2023",
doi = "10.1109/CCDC58219.2023.10327045",
language = "英语",
series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4266--4271",
booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
}