The demand response potential in copper production

Abstract

Demand response (DR) of large industrial electricity consumers is a promising option to balance the fluctuating supply by renewable energies in the electricity grid. Renewable energy technologies themselves depend on copper as a key material. At the same time, the production of copper is a power-intensive process but its DR potential has not yet been quantified in detail via process scheduling. Here, we analyze the DR potential of copper production by optimally scheduling the batch and continuous tasks of a representative copper process. To determine the optimal schedule, we formulate a mixed-integer linear program (MILP) based on the resource-task network (RTN) formulation approach. We first optimize the production volume to define a reference case and then minimize the electricity costs under time-varying electricity prices while retaining the production target. A sensitivity analysis evaluates the impact of task capacities on the production volume and DR potential. The results indicate a significant DR potential, as optimal scheduling can reduce the annual electricity costs by up to 14.2% while still producing the maximum copper output as the reference process schedule. The power-intensive electrolytic refining shows the largest potential for reducing costs. Offgas handling, air separation, and air compression further show significant cost reduction potentials. These tasks must process large material streams that are directly connected to operating the smelting and refining tasks. Our model shows the potential of considering these interlinked tasks in one scheduling model that focuses on DR. The results suggest that DR scheduling in copper production has a significant economic potential without compromising production goals. Further, the DR scheduling shifts large amounts of electricity demand by responding to fluctuating electricity prices, which enables flexibility of the demand side and can thus support the integration of renewable energy into the electric grid. • Process scheduling of copper production to assess demand response (DR) potential. • Discrete-time resource-task network model to resolve electricity demands. • DR can reduce electricity costs by up to 14% based on German day-ahead market. • Substantial load shifting potential of 16% of the annual electricity demand. • Task-specific DR potentials and impact of process debottlenecking analyzed.