Founding Research Journal

Founding Research Journal

Energy Management in Steelmaking by Electric Arc Furnace Based on OLTC

Document Type : Original Research Article

Authors
S. Mohammad Rezaeian 1
Nabiollah Ramezani 2
Majid Abbasi 3
1 M.Sc. Student, Department of Electrical and Computer Engineering- University of Science and Technology of Mazandaran
2 Assistant Professor, Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran
3 Associate Professor, Faculty of Materials and Industrial Engineering, Babol Noshirvani University of Technology, Mazandaran, Iran
10.22034/frj.2020.218472.1114
Abstract
Demand side management (DSM) is a popular topic that has been widely discussed in the electricity industry in recent years.Industrial processes, such as steel production, have complex planning in general.In many industrialized countries, these plants regularly participate in DR programs.Electric arc furnaces (EAFs) in steel mills have been recognized as having high potential for DSM.The widely used method for modeling, optimizing and planning such plants in general is the Resource-Task Network (RTN). To solve the problem of achieving optimal solution, it is recommended to use a meta-heuristic algorithm. For the purpose of comparison, two genetic (GEN) and particle swarm Optimization (PSO) algorithms are used for this purpose. Case studies were performed to illustrate the impact of the proposed method on the daily planning of a steel plant. Overall, the results indicate that incorporating OLTC into the planning of a steel plant results in improvements in reducing its daily costs.
Keywords
Steel Industrials
Electric arc furnaces
Demand Side Management
OLTC
Meta-heuristic algorithm
Subjects
[1]     Ilic M. D., Xie L., Joo J.-Y., Efficient coordination of wind power ´and price-responsive demand- Part I: Theoretical foundations, IEEE Transactions on Power Systems, 2011, 26(4) 1875–1884.
[2]     Xu Y., Li N., Low S., Demand response with capacity constrained supply function bidding, IEEE Transactions on Power Systems, 2015, 99, 1–18.
[3]     Wei W., Liu F., Mei S., Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty, IEEE Transactions on Smart Grid, 2015, 6(3) 1364–1374.
[4]     Fang X., Hu Q., Li F., Wang B., Li Y., Coupon-based demand response considering wind power uncertainty: A strategic bidding model for load serving entities, IEEE Transactions on Power Systems, 2016, 3(2) 1025–1037.
[5]     Paulus M., Borggrefe F., The potential of demand-side management in energy-intensive industries for electricity markets, in Germany, Applied Energy, 2011, 88(2) 432–441.
[6]     Li N., Chen L., Dahleh M., Demand response using linear supply function bidding, IEEE Transactions on Smart Grid, 2015, 6(4) 1827–1838.
[7]     Kamyab F., Amini M., Sheykhha S., Hasanpour M., Jalali M., Demand response program in smart grid using supply function bidding mechanism, IEEE Transactions on Smart Grid, 2015, 7(3) 1277-1284.
[8]     Ji S., Lu T., Zhao Z., Yu H., Yuan L., Yang S., Secrest C., Physical model analysis during transient for series-connected HVIGBTs, IEEE Transactions on Power Electronics, 2014, 29(11) 5727–5737.
[9]     Zhang X., Hug G., Optimal regulation provision by aluminum smelters, in IEEE Power and Energy Society General Meeting, 2014.
[10]  Zhang X., Hug G., Kolter J.Z., Harjunkoski I., Model predictive control of industrial loads and energy storage for demand response, in IEEE PES General Meeting, 2016.
[11]  Ashok S., Peak-load management in steel plants, Applied Energy, 2006, 83(5) 413–424.
[12]  Nolde K., Morari M., Electrical load tracking scheduling of a steel plant, Computers & Chemical Engineering, 2010, 34(11) 1899–1903.
[13]  Zhang X., Hug G., Harjunkoski I., Cost-effective scheduling of steel plants with flexible EAFs, IEEE Trans. on Smart Grid, 2016.
[14]  Zhang X., Hug G., Kolter J. Z., Harjunkoski I., Computational approaches for efficient scheduling of steel plants as demand response resource, in IEEE Power Systems Computation Conference, 2016.
[15]  Harjunkoski I., Grossmann I. E., A decomposition approach for the scheduling of a steel plant production, Computers & Chemical Engineering, 2001, 25(1112) 1647–1660.
[16]  Pantelides C.C., Unified frameworks for the optimal process planning and scheduling, Proceedings of the 2nd Conference on the Foundations of Computer Aided Operations, New York: Cache Publications 1994, 253.
[17]  Castro P.M., Barbosa-Póvoa A.P., Matos H.A., Novais A.Q., Simple continuous-time formulation for short-term scheduling of batch and continuous processes, Industrial & Engineering Chemistry Research, 2004, 43, 105-118.
[18]  P. M. Castro, Ignacio E. Grossmann and Augusto Q. Novais, New continuous-time models for the scheduling of multistage batch plants with sequence dependent changeovers, Industrial & Engineering Chemistry Research, 2006, 45, 6120-6226.
[19]  فرشاد مریخ بیات، الگوریتم‌های بهینه‌سازی فرا ابتکاری (همراه با کاربردهایی در مهندسی برق)؛ انتشارات جهاد دانشگاهی؛ 1393.
[20] یقینی م.، کاظم‌زاده اخوان م.ر.، الگوریتم‌های بهینه‌سازی فرا ابتکاری، جهاد دانشگاهی واحد صنعتی امیرکبیر، چاپ سوم، 1395.

  • Receive Date 03 February 2020
  • Revise Date 23 June 2020
  • Accept Date 07 April 2020