TY - JOUR
T1 - Multi-process production scheduling with variable renewable integration and demand response
AU - Ruiz Duarte, José Luis
AU - Fan, Neng
AU - Jin, Tongdan
N1 - Funding Information:
The authors express their sincere thanks to the senior editor and three anonymous reviewers for their constructive comments on the manuscript. J.L. Ruiz Duarte is supported by the Mexican National Council of Science and Technology (CONACYT) and the Mexican Department of Energy (SENER) for his PhD program. Vectors utilized in Fig. 2 were designed by Katemangostar, Naulicreative / Freepik.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/2/16
Y1 - 2020/2/16
N2 - Integrating renewable energy sources to power manufacturing facilities is one approach to achieve low carbon economy. The contribution of this paper is to propose a way to facilitate and assess renewable sources’ integration into manufacturing systems, by exploring an optimization model that obtains a production schedule adapted to match the onsite renewable energy supply, with energy storage systems and the power grid as backups. A multi-process production scheme as well as demand side management policies such as Time-and-Level-of-Use and power consumption reduction requests are considered. To capture renewable uncertainties, a two-stage robust optimization model is formulated to optimize the production scheduling under the worst-case scenario of renewable generation. A nested Column-and-Constraint Generation algorithm is applied to solve this formulation. Numerical experiments are performed on a benchmark case, and sensitivity analysis is conducted by modifying renewable integration, uncertainty, data granularity, scheduling horizon, switch of on-peak prices hours, and zero-inventory policy. Obtained results validate the proposed model and algorithm.
AB - Integrating renewable energy sources to power manufacturing facilities is one approach to achieve low carbon economy. The contribution of this paper is to propose a way to facilitate and assess renewable sources’ integration into manufacturing systems, by exploring an optimization model that obtains a production schedule adapted to match the onsite renewable energy supply, with energy storage systems and the power grid as backups. A multi-process production scheme as well as demand side management policies such as Time-and-Level-of-Use and power consumption reduction requests are considered. To capture renewable uncertainties, a two-stage robust optimization model is formulated to optimize the production scheduling under the worst-case scenario of renewable generation. A nested Column-and-Constraint Generation algorithm is applied to solve this formulation. Numerical experiments are performed on a benchmark case, and sensitivity analysis is conducted by modifying renewable integration, uncertainty, data granularity, scheduling horizon, switch of on-peak prices hours, and zero-inventory policy. Obtained results validate the proposed model and algorithm.
KW - Demand response
KW - OR in energy
KW - Onsite renewables
KW - Production scheduling
KW - Robust optimization
UR - http://www.scopus.com/inward/record.url?scp=85071121461&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071121461&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2019.08.017
DO - 10.1016/j.ejor.2019.08.017
M3 - Article
AN - SCOPUS:85071121461
VL - 281
SP - 186
EP - 200
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 1
ER -