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
Hauptverfasser: Servare, Marcos W.J., de Oliveira Rocha, Helder R., Salles, José L. Félix
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2024
Schlagworte:
Battery management
Energy scheduling
Mixed-integer nonlinear programming model
Port
Rolling horizon algorithm
Stockyard
Energy scheduling
Stockyard
Rolling horizon algorithm
Mixed-integer nonlinear programming model
Battery management
Port
ISSN:0305-0548, 1873-765X
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Zusammenfassung: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.
ISSN:0305-0548
1873-765X
DOI:10.1016/j.cor.2023.106518