Robust optimization for integrated production and energy scheduling in low-carbon factories with captive power plants under decision-dependent uncertainty

Low-carbon factories with captive power plants represent a new industrial microgrid paradigm of energy conservation and emission reduction in many countries. However, one of the most common challenges of low-carbon management is the joint regulation of factory production and power plant operations under uncertainty. To meet this challenge, a robust optimization-based integrated production and energy (IPE) scheduling approach is proposed in this paper. Firstly, a two-stage adaptive robust optimization model is established to cover all possible realizations of decision-independent uncertainties (e.g. market demands and output power of renewable sources) and decision-dependent uncertainties (e.g. carbon emission densities depending on the choice of production lines). Secondly, a novel parametric column-and-constraint generation algorithm is utilized to derive robust scheduling schemes. The non-trivial scenarios of decision-dependent uncertainties identified in the subproblem are parametrically characterized based on Karush–Kuhn–Tucker conditions, which can be included in the master problem. Finally, simulations on different cases are conducted to test the rationality and validity of the proposed approach. Compared with the separate scheduling of production and energy, IPE scheduling may increase production and energy costs to ensure the robustness of the resulting schemes. Moreover, the proposed approach can mitigate the impacts of uncertainties on IPE scheduling without significantly increasing the computational complexity.