Multi-process production scheduling with variable renewable integration and demand response

•A production schedule is optimized to maximize the usage of renewable energy.•A two-stage robust optimization model is proposed to address renewable uncertainty.•A nested column-and-constraint generation algorithm is designed.•Numerical experiments and a case study are presented to show the effectiveness. 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.