A smart energy scheduling under uncertainties of an iron ore stockyard-port system using a rolling horizon algorithm
Planning the efficient use of electricity in iron ore stockyard operations is a strategic issue due to the constant rise in energy prices nowadays and its considerable impact on production costs. This paper proposes a new large-scale mixed-integer nonlinear programming (MINLP) model for stockyard-po...
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| Veröffentlicht in: | Computers & operations research Jg. 164; S. 106518 |
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| Sprache: | Englisch |
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01.04.2024
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| ISSN: | 0305-0548, 1873-765X |
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| Abstract | Planning the efficient use of electricity in iron ore stockyard operations is a strategic issue due to the constant rise in energy prices nowadays and its considerable impact on production costs. This paper proposes a new large-scale mixed-integer nonlinear programming (MINLP) model for stockyard-port energy planning solved by the energy scheduling algorithm and a commercial solver to minimize power costs. The proposed nonlinear optimization problem is solved through an equivalent MILP model to minimize the flows of power and material between the stockyard-port equipment. The electrical machines are powered by different electricity energy providers, and eventually consume storage energy from batteries. The energy scheduling algorithm allows the planner to find a solution that saves electrical power costs in real time under unforeseen operational changes. Numerical results obtained through the proposed algorithm with a scheduling horizon of 24 h, show that the presence of the battery in the stockyard-port electrical grid allows for an energy cost reduction of up to 17.88% compared to the case without the battery. The energy scheduling based on rolling horizon algorithm provides feasible solutions near to the optimal solution, with an average distance of 1.78%, and it has an affordable computation time in instances where the MINLP model is not able to provide a solution.
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•Two MINLP models are proposed for a smart energy scheduling of an iron ore stockyard-port system.•They manage iron ore flow and electricity consumption from different electric power suppliers and/or batteries.•A rolling horizon approach is introduced to deal with parameter uncertainties of the MINLP models.•This approach allows for updating the estimated parameters in real time along the prediction horizon. |
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| AbstractList | Planning the efficient use of electricity in iron ore stockyard operations is a strategic issue due to the constant rise in energy prices nowadays and its considerable impact on production costs. This paper proposes a new large-scale mixed-integer nonlinear programming (MINLP) model for stockyard-port energy planning solved by the energy scheduling algorithm and a commercial solver to minimize power costs. The proposed nonlinear optimization problem is solved through an equivalent MILP model to minimize the flows of power and material between the stockyard-port equipment. The electrical machines are powered by different electricity energy providers, and eventually consume storage energy from batteries. The energy scheduling algorithm allows the planner to find a solution that saves electrical power costs in real time under unforeseen operational changes. Numerical results obtained through the proposed algorithm with a scheduling horizon of 24 h, show that the presence of the battery in the stockyard-port electrical grid allows for an energy cost reduction of up to 17.88% compared to the case without the battery. The energy scheduling based on rolling horizon algorithm provides feasible solutions near to the optimal solution, with an average distance of 1.78%, and it has an affordable computation time in instances where the MINLP model is not able to provide a solution.
[Display omitted]
•Two MINLP models are proposed for a smart energy scheduling of an iron ore stockyard-port system.•They manage iron ore flow and electricity consumption from different electric power suppliers and/or batteries.•A rolling horizon approach is introduced to deal with parameter uncertainties of the MINLP models.•This approach allows for updating the estimated parameters in real time along the prediction horizon. |
| ArticleNumber | 106518 |
| Author | Servare, Marcos W.J. de Oliveira Rocha, Helder R. Salles, José L. Félix |
| Author_xml | – sequence: 1 givenname: Marcos W.J. orcidid: 0000-0002-4531-9934 surname: Servare fullname: Servare, Marcos W.J. – sequence: 2 givenname: Helder R. surname: de Oliveira Rocha fullname: de Oliveira Rocha, Helder R. – sequence: 3 givenname: José L. Félix orcidid: 0000-0002-3417-6544 surname: Salles fullname: Salles, José L. Félix email: jose.salles@ufes.br |
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| Keywords | Energy scheduling Stockyard Rolling horizon algorithm Mixed-integer nonlinear programming model Battery management Port |
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