Hydrogen-electricity integrated multi-energy systems are promising approaches to reduce carbon emissions in ports. However, the stochastic nature of renewable energy and the imbalance between the renewable generation and load demand in ports necessitate the design of an appropriate coupled hydrogen-electricity energy storage systems (CHEESS). This paper proposes a multi-objective optimization model for CHEESS configuration in random unbalanced port integrated multi-energy systems (PIMES), aiming to minimize its life-cycle cost and carbon emissions through co-optimization of sizing and energy management. A hierarchical two-stage framework is proposed to solve the multi-objective model. The proposed optimization framework is applied to a real PIMES at the Ningbo-Zhoushan Port. The results show that the proposed method can save 10.54% of the monetary cost and 19.67% of carbon emissions over the entire life-cycle of the system. The study demonstrates that the proposed framework has the potential to generate significant economic and environmental benefits and provides a feasible solution for port authorities seeking to implement CHEESS, aiming to promote sustainability in port operations.
Wind Propulsion Systems (WPS) have gained significant attention as a means of decarbonizing shipping. Limitations in available deck space, emissions reduction targets, and regulatory compliance have led to a wide array of potential WPS configurations, each exhibiting distinct aerodynamic performance and requiring unique optimum sail trims for each unit due to complex interactions. This variability challenges existing aerodynamic models and optimization efforts for maximizing fuel savings. To address this, we present a novel methodology that, for the first time in WPS aerodynamic performance prediction, combines Computational Fluid Dynamics (CFD), independent sail trim optimization, and Machine Learning (ML) to develop surrogate models — Gaussian Process Regression and Feedforward Neural Networks — that rapidly predict aerodynamic performance with CFD-equivalent accuracy. These surrogates capture aerodynamic interactions across various WPS configurations, including unit number, deck arrangement, independent sail trim, hull characteristics, and wind conditions. While employing established ML techniques, our approach is novel in its resource-efficient generation of a comprehensive aerodynamic database, derived from the first in-depth independent trim optimization of a DynaRig case study. Our approach enables the modeling of complex, non-linear interactions that traditional interpolation methods fail to capture. Results show that the developed surrogate models achieve CFD-level accuracy, with an average error below 1 while significantly reducing computational time. This ML-enhanced framework facilitates extensive, rapid WPS design optimizations, supporting efficient integration into performance prediction programs (PPPs) and maximizing fuel savings and emissions reductions tailored to specific routes and wind conditions.Machine Learning; CFD-Simulations; Aerodynamic Performance; Wind Propulsion Systems; Green Shipping; Independent Sail Trim Optimization.
Driven by regulatory mandates, International Maritime Organization (IMO) decarbonization targets, market pressure, and evolving societal expectations, the maritime industry is undergoing a fundamental transition towards full decarbonization. This shift has renewed interest in Wind Propulsion Systems (WPSs) as viable propulsion alternatives, reflected in their increasing adoption. However, widespread implementation remains challenging. Each WPS installation design excels under specific conditions, which makes selecting the most cost-effective WPS installation complex. Failure to optimize design and placement can lead to suboptimal fuel savings or unprofitable deployments, limiting industry confidence, and slowing adoption.
To address these challenges, this PhD Thesis presents a novel modelling framework to optimize WPS installation designs by evaluating their cost-benefit trade-offs. The framework identifies the optimal WPS class, design, positioning, and arrangement to maximize fuel savings and emission reductions while minimizing investment costs, tailored to an operator’s specific profile. The study addresses three main objectives: (1) determining the most cost-effective WPS installation design, (2) enhancing industry understanding of WPS performance, and (3) supporting informed decision-making for shipowners and operators.
The results demonstrate that there is no on-size-fits-all WPS solution; instead, each optimal configuration requires a use-case-specific evaluation, accounting for factors such as ship type, route, wind conditions, emissions reduction targets, and operational constraints. However, general trends emerge. Higher emissions reduction ambitions – requiring larger WPS installations — favor high lift-to-drag ratio and lightweight technologies for costeffectiveness. In contrast, low lift-to-drag ratio systems are more sensitive to deck placement and wind conditions due to the resulting hydrodynamic penalties to counteract aerodynamic
forces, though these effects become less significant for lower emissions reduction targets. Installation viability is further constrained by factors such as maximum air draft and cargo space loss due to weight penalties, which may significantly impact economic feasibility.
Optimization of WPS installation design is found to be critical for maximizing economic returns and ensuring fair comparisons across different WPS classes, as each class has unique performance characteristics. The most cost-effective configurations generally involve max imizing unit spacing to reduce aerodynamic interactions and placing units near the hydrodynamic center of lateral resistance to minimize added resistance penalties. Suboptimal designs can extend payback periods by over 150% compared to optimized configurations. Additionally, while WPS-equipped vessels require higher upfront investment, they demonstrate rapid payback periods and strong profitability, particularly in favorable operational and economic conditions.
A critical threshold of limited return on investment is identified for retrofit installations, occurring when additional WPS units no longer yield increased fuel and emissions savings. This is due to hydrodynamic penalties required to maintain yaw moment balance, ultimately offsetting the WPS benefits. This also underscores the need for an optimized deployment strategy to maximize savings while minimizing investment costs, preventing unprofitable installations that could foster skepticism and hinder adoption.
The methods and findings presented in this PhD Thesis provide a foundation for unlocking the full potential of wind propulsion systems, supporting a more sustainable, cost-effective, and decarbonized shipping industry.
This study introduces WindWise, a cost–benefit analysis and design optimization tool for Wind Propulsion Systems (WPS) in sustainable shipping. By integrating route simulations, ship constraints, and fuel pricing scenarios, WindWise determines the optimal WPS configuration to maximize fuel savings and minimize payback periods. A retrofit case study of an oil tanker evaluates two WPS classes—DynaRigs and Rotor Sails—across multiple operational and economic conditions. Results reveal that optimal configurations vary based on constraints: in an unconstrained scenario, larger, well-spaced installations minimize aerodynamic losses, whereas realistic constraints shift the preference towards smaller, distributed setups to mitigate cargo loss and air draft penalties. Rotor Sails offer lower upfront costs and shorter payback periods for modest savings targets and for side-wind routes, while DynaRigs emerge as the more viable solution for higher emissions reductions and long-term profitability. Optimization of WPS configurations proves crucial, with non-optimized configurations exhibiting payback periods over 150% higher than optimized ones. Although payback period remains an important metric, considering both payback and net present value provides a more comprehensive assessment of WPS financial viability, with Rotor Sails generally offering faster payback but DynaRigs delivering higher long-term profitability across most scenarios.
This study introduces WindWise, a cost–benefit analysis and design optimization tool for Wind Propulsion Systems (WPS) in sustainable shipping. By integrating route simulations, ship constraints, and fuel pricing scenarios, WindWise determines the optimal WPS configuration to maximize fuel savings and minimize payback periods. A retrofit case study of an oil tanker evaluates two WPS classes—DynaRigs and Rotor Sails—across multiple operational and economic conditions. Results reveal that optimal configurations vary based on constraints: in an unconstrained scenario, larger, well-spaced installations minimize aerodynamic losses, whereas realistic constraints shift the preference towards smaller, distributed setups to mitigate cargo loss and air draft penalties. Rotor Sails offer lower upfront costs and shorter payback periods for modest savings targets and for side-wind routes, while DynaRigs emerge as the more viable solution for higher emissions reductions and long-term profitability. Optimization of WPS configurations proves crucial, with non-optimized configurations exhibiting payback periods over 150% higher than optimized ones. Although payback period remains an important metric, considering both payback and net present value provides a more comprehensive assessment of WPS financial viability, with Rotor Sails generally offering faster payback but DynaRigs delivering higher long-term profitability across most scenarios.
This paper proposes a multi-time scale scheduling strategy for a practical port coupled hydrogen-electricity energy system (CHEES) to optimize the integration of renewable energy and manage the stochasticity of port power demand. An optimization framework based on day-ahead, intra-day and real-time scheduling is designed. The framework allows coordinating adjustable resources with different rates to reduce the impact of forecast errors and system disturbances, thus improving the flexibility and reliability of the system. The effectiveness of the proposed strategy is verified by a case study of the actual CHEES in the Ningbo Zhoushan Port, and the impact of equipment anomalies on the port power system operation is studied through simulation of different scenarios. The results show that compared with a scheduling scheme without energy management strategy, CHEES with multi-time scale scheduling can save 25.42% of costs and reduce 14.78% of CO 2 emissions. A sensitivity analysis is performed to highlight the impact of hydrogen price and soft open points (SOP) rated power on the system economy. This study not only provides a new perspective for the optimal scheduling of port energy systems, but also provides a practical framework for managing port energy systems to achieve green transformation and sustainable development.
Simulation-based analysis estimating both the energy requirement of the entire carbon capture process and the purity of the recovered CO 2 is scarce. The purity of the captured CO 2 is crucial as it must meet a specification before transportation, preventing phase change and damage to the transportation system. This study conducted 31,104 simulations of a monoethanolamine carbon capture plant treating measured flue gas from an existing cement production plant. After capture, the CO 2 is treated through a deoxygenation unit followed by a compression train to fulfill specific quality specifications. Based on the sensitivity analysis, the energy consumption of the post-treatment process decreased with increased purity downstream. Despite this, the total energy consumption was not affected. Moreover, after the two-step purification the CO 2 stream was able to successfully fulfill the specification for NO x, O 2, NH 3, Ar, CO, SO 2. However, failing to meet the H 2O concentration requirements of both considered specifications and the N 2 concentration specified for ship transport. Thus, increasing the post-treatment energy cost or standard adjustments is required for future applications.
This paper investigates the significance of ports in the energy transition (ET) and decarbonisation. Ports, being vital in energy value chains, play a critical role in curbing energy use and emissions. The paper draws from the MAGPIE project, funded by the Horizon 2020 programme, which showcases energy and digital solutions in a real-world setting. The paper focuses on sustainable initiatives in 12 European sea- and inland- ports, analysed through interviews and secondary data. Findings reveal that while many ports discuss ET, few have transformed their plans into significant actions due to technological, regulatory, and financial challenges. Three core themes emerge from the review: ET infrastructure, seagoing ships and hinterland transport, and governance. Ports need more actionable strategies for ET, with port authorities spearheading the adoption of sustainable technologies through collaboration.
Seaports consume a large amount of energy and emit greenhouse gas and pollutants. Integrated multiple renewable energy systems constitute a promising approach to reduce the carbon footprint in seaports. However, the intermittent nature of renewable resources, stochastic dynamics of the demand in seaports, and unbalanced structure of seaport energy systems require a proper design of energy storage systems. In this paper, a framework for multi-objective optimization of hybrid energy storage systems in stochastic unbalanced integrated multi-energy systems at sustainable mega seaports is proposed to minimize life-cycle costs and minimize carbon emissions. The optimization problem is formulated with reference to the energy management of the integrated multi-energy system at the seaport and considering both distributed and centralized hybrid energy storage configurations. Wavelet decomposition and double-layer particle swarm optimization are proposed to solve the multi-objective optimization problem. The real power system of the largest port worldwide, i.e., the Ningbo Zhoushan Port, was selected as a case study. The results show that, with respect to a situation with no energy storage system, the proposed approach can save 81.29 million RMB in electricity purchases and eliminate approximately 497,186 tons of carbon emissions over the entire lifecycle of the energy storage system. The findings suggest that the proposed hybrid energy storage framework holds the potential to yield substantial economic and environmental advantages within mega seaports. This framework offers a viable solution for port authorities seeking to implement hybrid energy storage systems aimed at fostering greater sustainability within port operations.
Port Integrated Multi-Energy Systems (PIMES) play a critical role in advancing sustain-ability at ports. Assessing the dynamic contribution of PIMES to port sustainability is essential for guiding future developments. This research introduces an innovative multi-criteria dynamic sustainability assessment framework tailored to evaluate the performance of PIMES. The framework employs a diverse set of indicators covering multiple criteria to comprehensively assess different aspects of PIMES. A game theory-based combined weighting approach is uniquely applied to integrate subjective and objective evaluations, ensuring a balanced and robust assessment. Furthermore, the cloud model is utilized for an in-depth evaluation of the overall sustainability of PIMES, offering a novel perspective on managing uncertainty. The framework's applicability and effectiveness are demonstrated through a case study of the Ningbo-Zhoushan Port, with a sensitivity analysis of the indicators conducted to enhance reliability and confirm the robustness of the proposed method. The evaluation results indicate that during the development of the PIMES, the sustainability performance of the studied port improves progressively, with ratings of “average”, “poor”, “average”, “average”, “good”, and “excellent”. The sensitivity analysis shows that the sustainability of ports is most influenced by the failure loss rate and operation & maintenance cost of PIMES. This framework can serve as a decision-making tool for port authorities to enhance energy efficiency, reduce emissions, and achieve long-term sustainability objectives at ports.