The importance of energy efficiency and demand response management while harnessing renewable energy draws more attention from many industries in recent years. Seaports, as large scale end-users, aim to adopt energy management systems (EMS) since energy prices have increased over years and sustainable operations is a key target for greening the port industry. Many seaports start to install fully electrified equipment and use electricity as the source of energy because electricity consumption, instead of carbon-intensive energy sources, contributes to the climate change mitigation targets. In this study, a mixed integer linear programming model is suggested to solve the integrated operations planning and energy management problem for seaports with smart grid (e.g. port microgrid) considering uncertain renewable energy generation. The operations planning aims to determine the number of quay cranes (QCs) and yard equipment to assign to each ship for each one hour period. It also determines each ship’s berthing duration which affects the hourly energy consumption due to the cold ironing and the available reefer containers. These plans result in energy demand. Meanwhile, energy management matches energy demand and supply considering different energy pricing schemes and bidirectional energy trading between energy sources (e.g. utility grid, renewable energy sources) and energy storage systems. Results indicate that significant cost savings can be achieved with smart grid (port microgrid) compared to conventional settings. Deploying energy storage systems in port microgrid results in important cost savings. Energy consumption is dominated by QCs, cold-ironing and reefer containers. Finally ports which harness renewable energy obtain significant costs savings on total cost.

Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty

The Emission Control Areas (ECAs) established by the International Maritime Organization are beneficial to reduce the sulphur emissions in maritime transportation but bring a significant increase in operating cost for shipping liners. Low sulphur emissions are required when ships berth or sail within ECAs. It is an irreversible trend that green technologies such as scrubbers and shore power will be implemented in maritime shipping industry. However, the literature lacks a quantitative decision methodology on green technology adoption for fleet deployment in a shipping network in the context of ECAs. Given a shipping network with multiple routes connected by transshipment hubs, this study proposes a nonlinear mixed integer programming model to optimally determine fleet deployment along routes (including green technology adoption), sailing speeds on all legs, timetables, cargo allocation among routes for each origin-destination pair, and berth allocation considering the availability of shore power at different berths in order to minimize total five types of cost. A three-phase heuristic is also developed to solve this problem. Numerical experiments with real-world data are conducted to validate the effectiveness of the proposed model and the efficiency of the three-phase heuristic. Some managerial implications are also outlined on the basis of the numerical experiments.

Green technology adoption for fleet deployment in a shipping network

Many seaports worldwide are equipped with one-way navigation channel, in which only one direction is allowed to sail through at a time and vessels thus have to queue up to enter and leave the port alternately. This paper proposes a mixed-integer linear programming (MILP) model for integrated planning of berth allocation and vessel sequencing in such a one-way navigation channel seaport, aiming to minimize weighted dwelling time of all vessels. A tailored adaptive large neighborhood search algorithm is developed for solving the model within a reasonable time. To evaluate the solution quality, a lower bound of the solution of the problem is given by a column generation algorithm that solves a set-partitioning model adapted from the MILP model. Numerical experiments on Jingtang port of China show that (i) the integrated planning model can reduce 20.90%-29.21% of the weighted dwelling time of vessels compared to the current port scheduling schemes; and (ii) the proposed adaptive large neighborhood search algorithm outperforms the GUROBI solver and some existing methods, solving all problem instances within 3 minutes with a 4.83% average optimality gap. Some managerial insights are offered to guide the port operations in a one-way navigation channel seaport.

Integrated planning of berth allocation and vessel sequencing in a seaport with one-way navigation channel

This article investigates a low carbon-oriented berth allocation and quay crane assignment problem considering vessels’ uncertain arrival time and loading/unloading workload for vessels. A two-stag...

Column generation for low carbon berth allocation under uncertainty

Recent trends in pursuing green ports have had positive effects on terminal operators constructing more eco-friendly decision schemes. A yard template in a container port not only determines the scheduling of the terminal handling equipment by container allocation, but also affects the carbon emissions from yard trucks caused by loading and unloading operations, due to the differing degree of yard road congestion. Moreover, the uncertainty regarding the number of transporting containers makes it challenging to construct a robust yard template for a container terminal. Motivated by research pointing to the need to alleviate the exhaust pollution generated by port handling equipment, this paper presents a two-stage stochastic programming model for yard template planning, with the aim of minimizing the maintenance and manpower costs of activating yard cranes for container handling and of reducing the carbon dioxide emitted from yard trucks. More specifically, the paper considers the impact of road congestion on carbon dioxide emissions from the perspective of the increase in transportation time caused by yard truck interruptions. The uncertainty regarding the number of transport containers is reflected by taking into account numerous scenarios in which different numbers of containers are loaded onto (discharged from) vessels that visit the port periodically. In addition, an improved Benders decomposition-based solution method is designed to solve the proposed model with large-scale problem instances. Several experiments are performed to validate the effectiveness of the model and the efficiency of the method. The numerical results demonstrate that yard template planning that considers road congestion can effectively reduce the carbon emissions of yard trucks in loading and unloading operations.

Improved Benders decomposition for stochastic yard template planning in container terminals

Global ports and maritime shipping networks are important carriers for global supply chain networks, but they are also the main sources of energy consumption and pollution. To limit ship emissions in ports and offshore areas, the International Maritime Organization, as well as some countries, has issued a series of policies. This study highlights the importance and necessity of investigating emergent research problems in the operation management of green ports and maritime shipping networks. Considerable literature related to this topic is reviewed and discussed. Moreover, a comprehensive research framework on green port and shipping operation management is proposed for future research opportunities. The framework mainly comprises four research areas related to emission control and grading policies. This review may provide new ideas to the academia and industry practitioners for improving the performance and efficiency of the operation management of green ports and maritime shipping networks.

Operation management of green ports and shipping networks: overview and research opportunities

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Operation management of green ports and shipping networks: overview and research opportunities