Optimization and Management of On-Site Power Plants Under Time-of-Use Power Price: A Case Study in Steel Mill

  • Xiancong ZhaoEmail author
  • Huanmei Yuan
  • Zefei Zhang
  • Hao Bai
Conference paper
Part of the The Minerals, Metals & Materials Series book series (MMMS)


The implementation of time-of-use (TOU) power tariff in Chinese steel industry provides an opportunity for steel mills to reduce electricity bills through an optimal collaboration between the on-site power plant (OSPP) and energy storage equipment (gasholders). In this paper, a mixed-integer linear programming (MILP) based scheduling model was proposed to achieve the optimal operation of OSPP and gasholders in a steel mill under TOU tariff. Compared with previous models, we considered the influence of TOU power tariff on the optimal scheduling of OSPP. The results of a case study demonstrate that the optimization model can achieve better peak-valley shifting of the electricity generation and decrease the electricity purchasing cost by 7.5% with improved gasholder stability. In addition, the overall power generation efficiency can be increased by 2.13% using the proposed model, which indicates that the byproduct gases can be effectively and efficiently used.


Steel making industry Byproduct gases Optimal scheduling Combined cycle power plants Time-of-use (TOU) power price 



This research was supported by Boya post-doctoral project of Peking University.


  1. 1.
    He K, Zhu H, Wang L (2015) A new coal gas utilization mode in China’s steel industry and its effect on power grid balancing and emission reduction. Appl Energ 154:644–650CrossRefGoogle Scholar
  2. 2.
    Zhang XP, Zhao J, Wang W, Cong LQ, Feng WM (2011) An optimal method for prediction and adjustment on byproduct gas holder in steel industry. Expert Syst Appl 38(4):4588–4599CrossRefGoogle Scholar
  3. 3.
    Li L, Li HJ (2015) Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry. J Cent South Univ 22(4):1437–1447CrossRefGoogle Scholar
  4. 4.
    Junior VB, Pena JG, Salles JL (2016) An improved plant-wide multiperiod optimization model of a byproduct gas supply system in the iron and steel-making process. Appl Energ 164:462–474CrossRefGoogle Scholar
  5. 5.
    Zhao XC, Bai H, Lu X, Shi Q, Han JH (2015) A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process. Appl Energ 148(2):142–158CrossRefGoogle Scholar
  6. 6.
    Zhao XC, Bai H, Shi Q, Lu X, Zhang ZH (2017) Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing. Appl Energ 195:100–113CrossRefGoogle Scholar
  7. 7.
    Zeng YJ, Sun YG (2015) Short-term scheduling of steam power system in iron and steel industry under time-of-use power price. J Iron Steel Res Int 22(9):795–803CrossRefGoogle Scholar
  8. 8.
    Robert E (1980) Improving fuel utilization in steel mill operations using linear programming. J Oper Manage 2:95–102Google Scholar
  9. 9.
    Akimoto K, Sannomiya N, Nishikawa Y (1991) An optimal gas supply for a power plant using a mixed integer programming model. Automatica 3:513–518CrossRefGoogle Scholar
  10. 10.
    Kim JH, Yi HS, Han C (2003) A novel MILP model for plant-wide multi-period optimization of byproduct gas supply system in the iron and steel making process. Chem Eng Res Des 8:1015–1025CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2020

Authors and Affiliations

  • Xiancong Zhao
    • 1
    Email author
  • Huanmei Yuan
    • 2
    • 3
  • Zefei Zhang
    • 2
    • 3
  • Hao Bai
    • 2
    • 3
  1. 1.Department of Industrial Engineering and ManagementPeking UniversityBeijingChina
  2. 2.State Key Laboratory of Advanced MetallurgyUniversity of Science and Technology BeijingBeijingChina
  3. 3.School of Metallurgical and Ecological EngineeringUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations