TY - GEN
T1 - Mid-short term industrial demand response strategy-case study for a steel mill
AU - Tang, Wenjun
AU - Liou, Lung Liang
AU - Yang, Hong Tzer
N1 - Funding Information:
This work was supported in part by Ministry of Science and Technology (MOST 108-3116-F-006-008-CC2), Taiwan.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/8/2
Y1 - 2020/8/2
N2 - Demand Response (DR), as one of the popular approaches in today's power system, focuses on changing electricity customer behavior to provide ancillary services for load balance and peak shaving for load factor improvement. As the potential it can provide, the industrial customer is one of the best candidates to be encouraged to participate in the DR program. In view of the industrial characteristics, we propose a mid-short term energy management for the industrial customers to join in the DR program to achieve maximum DR profit. The month-ahead maintenance planning and 5-day load arrangement constitute our model. Specifically, on the basis of monthly job planning, mixed integer linear programming (MILP) algorithm is proposed for the short-term DR scheduling via daily optimization within the 5-day moving window. In the optimization process, the opportunity cost, DR execution days, and manufacturing process are considered. The realistic data from a steel company in Taiwan is employed in our case studies. The results illustrate that the economic efficiency of the proposed method achieves the DR profit and, therefore, considerable electricity cost reduction for the consumer. The importance of opportunity cost consideration is demonstrated in the simulation as well.
AB - Demand Response (DR), as one of the popular approaches in today's power system, focuses on changing electricity customer behavior to provide ancillary services for load balance and peak shaving for load factor improvement. As the potential it can provide, the industrial customer is one of the best candidates to be encouraged to participate in the DR program. In view of the industrial characteristics, we propose a mid-short term energy management for the industrial customers to join in the DR program to achieve maximum DR profit. The month-ahead maintenance planning and 5-day load arrangement constitute our model. Specifically, on the basis of monthly job planning, mixed integer linear programming (MILP) algorithm is proposed for the short-term DR scheduling via daily optimization within the 5-day moving window. In the optimization process, the opportunity cost, DR execution days, and manufacturing process are considered. The realistic data from a steel company in Taiwan is employed in our case studies. The results illustrate that the economic efficiency of the proposed method achieves the DR profit and, therefore, considerable electricity cost reduction for the consumer. The importance of opportunity cost consideration is demonstrated in the simulation as well.
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U2 - 10.1109/PESGM41954.2020.9281426
DO - 10.1109/PESGM41954.2020.9281426
M3 - Conference contribution
AN - SCOPUS:85099170476
T3 - IEEE Power and Energy Society General Meeting
BT - 2020 IEEE Power and Energy Society General Meeting, PESGM 2020
PB - IEEE Computer Society
T2 - 2020 IEEE Power and Energy Society General Meeting, PESGM 2020
Y2 - 2 August 2020 through 6 August 2020
ER -