A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.

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Recently, there has been an increasing concern regarding the security and reliability of power systems due to the onerous consequences of cascading failures. Among many emergency control operations, controlled power grid islanding is a last resort yet powerful method to prevent large-scale blackouts. Islanding operations split the power grid into self-sufficient operational subnetworks and avoid cascading failures by isolating the failed elements of the power system into a non-operational island. In this paper, we consider a two-stage stochastic mixed-integer program to seek the optimal islanding operations under severe contingency states. Line switching and controlled load shedding are the main tools for the islanding operations and load shedding is considered as a measurement to gauge system's inability to respond to disruption. The number of possible extreme contingencies grows exponentially as the size of the grid increases, and this results in a large-scale mixed-integer program, which is a computationally challenging problem to solve. We present an efficient decomposition method to solve this problem for large-scale power systems.

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