Rising energy expenses, the shift towards renewable sources, and grid congestion considerably affect the operations of container terminals. To tackle these challenges, it is necessary to implement energy-aware integrated operational planning which considers related uncertainties. This work proposes a two-stage stochastic mixed integer programming model to optimize container terminal operations planning and demand-responsive energy management. To this end, energy consumption is shifted whenever operationally possible and economically beneficial. We solve the proposed model by developing a dedicated progressive hedging algorithm. Operations considered in this model include vessel scheduling at berths, temperature control of refrigerated containers, and allocation of handling capacity of quay cranes, yard cranes, and automated guided vehicles to serve each vessel. Various scenarios for vessel arrival times and electricity prices are explored representing the uncertainty of energy demand and supply, respectively, based on a case study of the Altenwerder container terminal in Hamburg. Our results suggest potential cost savings of 5.9 per cent on average with a single energy price based on a long-term contract and 13.2 per cent when applying varying real-time electricity prices based on wholesale market rates. These findings underscore the substantial potential of demand response strategies for (electrified) container terminal operations.

Storage space management in bulk terminals has become an important focus for research and practical operation due to the increasing demand for bulk cargo and limited storage space in stockyards. The study of storage space management in dry bulk terminals is less thorough and comprehensive, and the existing research investigates the storage space allocation problem with other operational problems like berth allocation problems, but little environmental consideration has been incorporated. We investigate the storage space allocation problem with the consideration of stacker-reclaimer assignment and mist cannon operation to deal with the dust generated during material stacking. A mixed integer programming model has been established with the aim of minimizing energy consumption to reflect the pursuit of the growing emphasis on climate-neutral operations and sustainability. We test the effectiveness of the model by conducting computational experiments. We use the commercial solver CPLEX to obtain the optimal solutions for most of the test instances. Useful managerial insights extracted from the computational results may serve as a reference for storage space management in dry bulk terminals.