A smart energy scheduling under uncertainties of an iron ore stockyard-port system using a rolling horizon algorithm

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.