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.

Port terminals, especially their reefer container yards, face surging power demands. Efficient reefer charging is critical for port sustainability and efficiency, as it helps reduce peak energy loads and total energy consumption. This requires consideration of reefer characteristics, temperature control requirements, time-variant energy prices, port power distribution network and environmental factors such as ambient temperature and sunlight. Optimising the charging power and internal temperature of reefers is therefore essential. This study introduces mathematical models to optimise two efficient charging schemes for reefers: flexible power charging and on/off power charging. Internet-of-Things (IoT) technologies can enable tailored optimisation strategies for reefer charging by facilitating information sharing among reefers and the charging planning system. This study also proposes a cyber-physical system for IoT that allows these charging schemes to be implemented. Using data from existing ports, the results demonstrate that the optimised reefer charging plan significantly reduces energy costs and alleviates peak energy consumption, consistently outperforming the baseline policy. In the optimised plan, charging periods are slightly adjusted based on energy price in each period as part of a demand response strategy, and intermittent charging is actively used for peak energy shaving. The study also quantifies the positive impact of roof shade installation. Findings provide actionable insights for refrigerated goods.