An Overview of Multi-Energy Microgrid in All-Electric Ships

Abstract

Owing to the severe fossil energy shortage and carbon pollution, the extensive electrification of maritime transportation, represented by all-electric ships (AESs), has become an appealing solution to increase the efficiency and environmental friendliness of the industry. To improve energy utilization, not only renewable energy but also thermal energy has been introduced is used in AESs. However, various uncertainties that are associated with renewable energy and ship motions significantly inhibit and complicate the operation and navigation of multi-energy shipboard microgrids. Accordingly, a new coordination of optimal energy management and voyage scheduling is important in reducing both the costs and emissions of AESs. This overview characterizes shipboard microgrids and several emerging technical challenges related to joint power and voyage scheduling, and elucidates prospects for further research, based on a comprehensive survey of the relevant literature.

1 Introduction

In the last decade, almost 90% of global overseas trading by value involved maritime transportation (Fiadomor, 2009). Due to the increasing global concern about the huge fuel consumption and GHG emissions of traditional ships, the IMO has proposed a series of regulations to limit the contamination footprint of shipping and its energy waste (IMO, 2008). The AES has been proposed as promising and exemplary technology for improving energy efficiency and reducing carbon emissions (Skjong et al., 2016).

Different from land-based microgrid, an all-electric ship microgrid consists of propulsion system and electric power system. The on-board generation supplies electric power for the ship’s propulsion system and load through the electric network, so as to realize the integration of the ship’s power generation, loads, and storage. AESs are equipped with a fully electrified propulsion system, making their navigation more flexible than that of conventional ships. To reduce the use of fossil fuels (Apsley et al., 2009; Nuchturee et al., 2020), renewable generation (Geertsma et al., 2017) and thermal energy have been applied in shipboard microgrids (Li F. et al., 2018; Yuan et al., 2018; Li et al., 2021). However, uncertainties that are caused by multiple energy sources pose a critical challenge in the interaction between the operation and the navigation of an AES. Therefore, energy storage systems have drawn attention for their use in on-board power balance and efficiency improvement. Coordinated optimal power management and voyage scheduling are necessary to improve the efficiency of operation of on-board sources, considering various uncertainties (Sulligoi et al., 2016; Yigit and Acarkan, 2018).

The main contributions of this paper are as follows: 1) The developments, benefits, drawbacks, and applications of multi-energy systems in AESs are summarized; 2) efficient energy management and voyage scheduling pathways for the shipping industry are pro-posed; 3) several emerging technical challenges are raised and a future research roadmap is outlined.

The rest of this paper is organized as follows. Section 2 introduces a background of AESs. Section 3 discusses the configuration of the multi-energy systems of an AES. Section 4 present a joint optimization scheme for optimal energy management and voyage scheduling, and surveys the literature on multi-energy coordination and its potential maritime applications. Section 5 proposes a optimization scheme roadmap. Section 6 draws conclusions.

2 Characteristics of All-Electric Ships

Due to the low efficiency of traditional ships, an electric propulsion system is integrated into the shipboard power system so that both service loads and propulsion loads are powered by electricity. Thus, total fuel consumption is reduced and flexible navigation is achieved. AESs have been successfully used for military and commercial purposes.

3 Configuration of Multi-Energy Systems in All-Electric Ships

Figure 1 shows a typical topology of an all-electric ship. The diesel generators and energy storage systems deliver power via the energy network to meet the power demand of service and propulsion loads. To enhance the interaction of energy systems, electric boilers and electric chillers convert power into heat/cooling. The ship service load is associated with various pieces of onboard equipment, such as the radar, air conditioners, the navigation system and lights. The propulsion load drives the AES.

FIGURE 1

3.1 Power Generation

3.1.1 Diesel Generator

As the main power sources in a power system of AESs, diesel generators satisfy the load demand when the total power that is generated by both the renewable energy modules and the energy storage systems is insufficient. The fuel consumption of a diesel generator depends on the output power (Lin and Wang, 2019), as defined by Eq. 1.

Conventional reciprocating combustion engines, gas turbines, and boilers replace the traditional diesel generator. As either pure fuels or in fuel mixtures, LNG, biofuels, hydrogen, and ammonia have been widely piloted in commercial shipping as alternatives to conventional fuel oils.

3.1.2 Eco-Friendly Fuels

LNG was initially used as a fuel for steam engines, which are installed on large LNG carrier ships. Dual-fuel marine diesel generators use LNG as a secondary fuel (Tang et al., 2022). Alter-native fuels such as liquefied petroleum gas, methanol and ethanol have also been used as secondary fuels (Ali Shah et al., 2021). LNG emits around 12–20% less CO₂ than traditional maritime fuel oils (Fernández et al., 2017; Balcombe et al., 2021; Jo et al., 2021). However, the required LNG storage infrastructure, operational risk and regulatory uncertainty have prevented the large-scale use of LNG-fueled ships (Qyyum et al., 2021). The commercial viability and market-acceptance significantly motivate the use of LNG-fueled ships due to their economic and environmental merits. LNG-fueled ships will play an essential role in a near-term transition toward zero-carbon shipping (Kumar et al., 2011; Thomson et al., 2015; IMO, 2016; Schinas and Butler, 2016; Xin et al., 2021).

A decarbonizing future will favor the use of other eco-friendly fueled ships, including methanol-fueled and biomass-fueled shipping (Imran et al., 2013; Balamurugan and Nalini, 2014; Ellis and Tanneberger, 2015; Svanberg et al., 2018). With the growth of the hydrogen economy on land, hydrogen-fueled shipping is expected to be important in the future (Gohary and Seddiek, 2013; Welaya et al., 2013; Pan et al., 2014).

3.1.3 New Energy Sources

Since fossil fuel reserves are limited and carbon emissions are becoming serious, the IMO and researchers are paying more attention to new energy applications, such as solar energy, wind energy, fuel cells, nuclear energy (Xu et al., 2020).

Solar energy is abundant, non-polluting and freely available. On a ship, sunlight is converted to electricity through the installed PV generation system. Limited by the energy density and relatively low energy conversion efficiency, PV generation systems in AESs have power levels from hundred Watts to several kilo Watts.

Solar power has a significant role in AES coordination because both navigation and power plans are influenced by the received solar irradiation. To estimate the magnitudes of these effects, a mathematical model for calculating the power output of an on-board PV generation system (Qiu et al., 2019) is presented, as follows.

Unlike a land-based PV system, an on-board PV generation system is influenced by both solar irradiance and the moving and rolling of the AES, increasing associated uncertainties. Therefore, solar energy is normally the main power source in small-scale ships, but an auxiliary power source in large-scale ships (Zapałowicz and Zeńczak, 2021). Table 1 presents relevant characteristics these two kinds of solar-powered ship.

The two main ways of using wind energy in today’s shipping industry are wind-assisted ship propulsion and wind power generation (Talluri et al., 2016). On-board wind power generation can produce electricity irrespective of the direction of the wind. A suitable wind turbine must be used in the ship’s power system. Even though horizontal-axis wind tur-bines can operate with higher efficiency and produce more power than vertical-axis wind turbines (Kramer and Steen, 2022), the latter are more suitable for use on AESs because of they are less complex, cheaper costs and more stable (Mphatso et al., 2021).

Wind-assisted ships are primarily powered by the main engine, assisted by sails that are controlled by computers on different principles on which regular sails are based. Numerical calculations and experiments have shown that 15–25% of the thrust force that is generated by a ship’s propeller can be replaced by wind-assisted ship propulsion, saving fuel and reducing emissions (Ballini et al., 2017; OECD and ITD, 2018; Tillig and Ringsberg, 2020; Nyanya et al., 2021).

Nuclear-powered marine propulsion has mainly been used for military vessels, such as submarines, aircraft carriers, and icebreakers (Khlopkin and Zotov, 1997). Only four nuclear-powered merchant ships have ever been built, but none of them has proved profitable owing to the large initial investment and operational costs (Freire and Andrade, 2015; Ortiz-Imedio et al., 2021). The advantages of nuclear-powered merchant ships include the lack of a need for frequent refuelling, their having more space for cargo than available in other merchant ships, their higher power and speed, and their lower air pollution and emission of GHG. However, safety is the biggest concern around the use of nuclear energy in AESs. Furthermore, the generation of radioactive nuclear waste and the need for refuelling are the main barriers their widespread use.

The fuel cell is another promising technology for use in AESs. The energy efficiency of fuel cell ships is typically between 40 and 60%; it can reach 85% if waste heat is captured in a cogeneration scheme (Tronstad et al., 2017).

3.2 Service Load

Unlike loads on land, the service loads of AESs vary with the navigation mode and exhibit clear seasonality. The service loads of AESs require less power than propulsion loads, and they are independent of frequency. Fluctuations of service loads are relatively slow, regardless of randomness and uncertainties.

The service loads in AESs always involve pump loads, deck machinery, auxiliary equipment, air conditioning and refrigerating equipment, fans, repair equipment, kitchen equipment, and lighting devices. Table 2 presents the typical service loads of an AES un-der sailing conditions (Fang et al., 2019a).

During ship sailing, the ship’s service load reaches its maximum peak at 12:00. At 23:00 the service load is the minimum. Within 24 h, due to the daily routine of personnel, the service load in the three periods of morning, noon, and evening is relatively high, and the service load in other periods is relatively low.

3.3 Propulsion Load

The sailing speed of an AES, as a mobile microgrid, can be changed by electric pro-pulsion motors. The relationship between propulsion load and ship speed is defined as follows (Kanellos, 2014).

According to Eq. 3, voyage optimization is critical to shipboard power management because the navigation of an AES can be optimized by adjusting the sailing speed, which corresponds to load demand.

3.4 Thermal Load

Unlike electric loads, onboard thermal loads support heating and cooling, with the conversion of power into heat.

The thermal system in an AES counteracts the exchange of heat with the outdoor ambient temperature to maintain a comfortable indoor temperature for passengers (Y. Chen et al., 2019). The temperature change that is associated with the heat variation is specified by Eq. 4. means specific heat capacity of air, which is 1.29 kg/m³ means specific heat capacity of water, which is 4.2 × 10³ J/(kg·C°).

Equation 4 represents the heat exchange of air and the hot water system, respectively. The left side of the function represents the energy required for temperature change per unit time, and the right side include three parts, the first part represents the power required by the heating component, and the second part represents the heat dissipation energy of the surrounding environment, the third part represents the water energy added by thermal boiler. From the equation, it can be seen that the energy demand of the thermal load varies with temperature. Through the coordination and cooperation between the thermal system and electric system, the comprehensive utilization of energy can also be improved.

An electric boiler is an on-board device that converts power into heat. Since various electric boilers operate similarly, an electric boiler can be modeled as a heat-generating device with a constant efficiency (Li and Xu, 2018), as presented in Eq. 5.

3.5 Energy Storage System

In order to maintain the power balance of multi-energy systems in AESs, ESSs are utilized to compensate for the power fluctuations that are caused by renewable sources and ship loads. ESSs provide operational benefits in terms of power quality and fuel cost (Posthumus et al., 1996; Monti et al., 2008). The charging/discharging behavior of on-board battery is given below (Wen et al., 2017; Lan et al., 2019).

As presented in Eq. 6, when the total power that is generated from DGs and the PV generation system exceeds the total load demand, the ESS is charged. Otherwise, the ESS discharges to provide needed power.

4 Key Technologies for Multi-Energy All-Electric Ships

The intermittent and uncertain nature of renewable energy generation raises crucial challenges in maintaining stable and sustainable operations. Irregular and stochastic waves make the outputs of a dynamic positioning system uncertain, increasing the difficulty and complexity of energy management in an AES over that in a terrestrial power system.

4.1 Optimal Energy Management

In the maritime industry, efficiency and emissions are becoming increasingly important issues (IMO-MEPC, 2009; MEPC-IMO, 2009; MEPC-IMO, 2011). Fuel consumption also becomes increasingly important as the penalization of emissions adds to ships’ operational costs. Accordingly, power management should be optimized in a manner that takes into account several factors, such as the operational restrictions that are imposed by each power source, operating and maintenance costs, safety constraints, and load demand (Li and Xu, 2018; Othman et al., 2018; Accetta and Pucci, 2019; Yin et al., 2019; Hasanvand et al., 2020; Hein et al., 2020; Yang et al., 2020; Rafiei et al., 2021; Yang et al., 2021).

Service loads mainly consist of power and thermal loads. Power loads come are associated with all of the appliances on-board and thermal loads are mainly associated with space heating in winter or cooling in summer (Li and Xu, 2018).

In this paper, the capacity of the electric propulsion system for the ship is 12 MW, and the capacity of the battery is 1 MW.

FIGURE 2

4.2 Voyage Scheduling

With respect to power consumption, the ship’s propulsion motors dominate the propulsion loads, which depend strongly on the ship’s speed (Kanellos et al., 2014; Sen and Padhy, 2015; Zaccone et al., 2018).

The main objective of voyage optimization is to reduce the total resistance of the ship, and thus to reduce operational cost during navigation (Fang et al., 2020). The basic variables are the ship’s specifications, sailing speed, and weather conditions. The main constraints are safety of handling, the time at ports and fuel consumption. The variables and constraints are all related to distance travelled, speed at time (Sen and Padhy, 2015; Fang et al., 2020; Wen et al., 2020).

Speed optimization refers to a specific route under the constraints of an uncertain service time and weather conditions (Li X. et al., 2018; Wang et al., 2018; Li X. et al., 2020). Ship routes and scheduling methods were initially used to shorten distances, optimize the time of navigation, improve safety, and reduce costs (Kontovas, 2014; Vettor and Soares, 2016).

A ship’s voyage consists of periods of cruising and berthing. During cruising periods, a ship operates at full speed. During berthing periods, a ship operates at zero speed (Kanellos et al., 2014; Kanellos et al., 2017). During other periods, the speed of the ship is between zero and full speed.

As shown in Figure 3, the speed of the ship is flexible but it must be kept between the upper and lower bounds for an efficient and safe voyage. The ship’s cruising distance is determined by the cruising speed for the corresponding duration. A punctual arrival requires that the ship reaches the vicinity of the relevant ports during the berthing periods (Fang et al., 2019b).

FIGURE 3

Figure 4 reveals that the cruise ship can maximally exploit its electric propulsion motors by adjusting its sailing speed. In Case 2.1, the proposed method enables the AES to arrive at the final terminal 3 h earlier than that in Case 1.1 as a result of optimal voyage scheduling.

FIGURE 4

The ship speed after coordination optimization of power generation and voyage scheduling is the optimized speed during the all-electric ship navigation. The optimization research is conducted on the voyage of all-electric ships. By optimizing the voyage of all-electric ship during the voyage and indirectly adjusting the load demand of the electric propulsion system, the output power of the diesel engine power generation system can be indirectly optimized. Although the economic dispatch of all-electric ships is not considered in Case 2, the operating costs and pollution emissions of all-electric ships during sailing can still be reduced to a certain extent.

4.3 Coordination of Optimal Power Management and Voyage Scheduling

The cited works consider only optimal power management or voyage scheduling when the ship is cruising. However, a ship’s voyage can be scheduled simultaneously for an energy dispatch (Fang et al., 2019a). Under an obligation to arrive punctually, the cruising speed of a ship can be varied in a secure range. Since the ship speed is driven by the propulsion loads, the ship’s energy dispatch. A few investigations have addressed voyage scheduling (Kanellos, 2014; Kanellos et al., 2014; Shang et al., 2016a; Shang et al., 2016b; Kanellos et al., 2017; Bouaicha et al., 2018).

As proved by Figure 5, the potential of an electric propulsion system of an AES can be optimally realized by the coordination of optimal power management and voyage scheduling which minimize both the operational cost and emissions associated with the cruise.

FIGURE 5

The ship with voyage scheduling can reach the final port in 1 h before it does so without voyage scheduling case, as displayed in Figure 6.

FIGURE 6

4.4 Multi-Energy All-Electric Ship Considering Uncertainty

In multi-energy AES energy systems, the solar irradiation that is received by an AES varies with the motion of the ship, resulting in dramatic power fluctuations. Furthermore, the stochastic nature of the wind increases the risk of unsafe sailing and late arrival. Not only does shipboard operating performance significantly affect the environment, but also the deployment of on-board solar energy raises a crucial challenge in the interaction be-tween the operation and the navigation of an AES. Therefore, coordinated optimal power management and voyage scheduling are necessary to improve the efficiency of operation of on-board sources, considering various uncertainties.

For the optimization of uncertainty, the three currently available methods are, interval optimization (Wen et al., 2016), robust optimization Li Z. et al., 2020) and probabilistic optimization (Huang et al., 2020).

Scenario 1: Multi-energy AES without any uncertain variables.

Scenario 2: Multi-energy AES considering uncertain irradiation.

Scenario 3: Multi-energy AES considering uncertain wind energy.

Scenario 4: Multi-energy AES considering uncertain ship rolling.

Scenario 5: Multi-energy AES considering combined uncertain factors.

Figure 7A-Hourly optimized outputs of DGs and on-board ESS under various uncertainties are proved by Figure 7. It can be observed from above that solar irradiance and ship rolling have a greater effect on the power outputs of DGs and the wind source has a greater effect on voyage optimization. Even though the uncertainties vary considerably among various time periods, the proposed method can ensure the stability of the operation of the AES, implying the robustness of the proposed algorithm. Among uncertain variables, ship rolling most strongly affects the power outputs since the motion of the ship has a deep influence on not only the on-board solar power but also the navigation of the ship.

FIGURE 7

5 Prospects and Research Roadmap

5.1 Multi Microgrid Coordination Between Seaport and Ships

The seaport microgrid is a recently proposed concept in seaport management (Parise et al., 2014). Since the extensive electrification of maritime transportation, the logistics and electrical side of seaports and ships have been connected (Paul et al., 2014; Sciberras et al., 2015). A seaport and ships will jointly operate with respect to both logistics and electric in future maritime transportation management, exhibiting two operating patterns.

Berthed-in mode: AESs are berthed in the seaport and receive a cold-ironing power supply. In this mode, ships coordinate with the seaport. The seaport microgrid system has many sub-systems and each has a clear function; they include the renewable energy sub-system and the port crane sub-system (Kanellos et al., 2019), Research into distributed control frame-works should be carried out.

Berthed-out mode: AESs navigate on the sea, coordinating with the seaport under the punctuality requirement, given the selected navigation route. In this mode, more features of shipboard microgrids should be considered. Multi-functional and multi-timescale power management systems on shipboard microgrids power quality and power-sharing problems should be considered in the future.

Future seaport and shipboard energy management will need to consider more complicated variables or constraints than must be considered conventional land-based microgrids, to address the characteristics of maritime transportation, such as cruise speed constraints (Lindstad and Eskeland, 2015), voyage distance or arrival times of ships (Lanellos et al., 2014; Shang et al., 2016a), and berth position/time allocation by seaports (Imai et al., 2001; Bierwirth and Meisel, 2015; Lu, 2015).

5.2 Adaptive Energy Management Methods

In berthed-in mode, a seaport has two roles: it acts as the service provider for AESs, supplying power, loading and unloading. The seaport also acts as an interface between AESs and the main grid owing to the very high energy demand. To ensure economic and environmental operations, adaptive energy management methods are needed. Operation-al methods should consider various uncertainties and coordinate the operations of electrical and logistic sub-systems.

Seaport microgrids typically purchase electricity from the main grid. Both ships and seaports can change their schedules for economic benefit. For example, ships can choose an arrival time when the electricity price is low, and seaports can arrange berths to ships with larger cold-ironing power in the first place, potentially establishing a power market.

Numerous energy management methods for AESs in berthed-out mode are available (Goel et al., 2015; Lan et al., 2015; Wang et al., 2015; Lu et al., 2016; Wen et al., 2016; Yao et al., 2018; Wen et al., 2021). Adaptive energy management methods should consider cruising speed regulation, PV integration and other uncertainties. Two topics must also be considered with respect to AESs and seaports. First, the cruising vessels need to provide various services to tourists, so they have a higher service load than conventional ships. Managing service loads in berthed-in and berthed-out modes. Second, AESs, and seaports must exchange information, and exchanged information can directly influence subsequent actions.

The resilience and reconfiguration control of a shipboard microgrid is another important topic. Methods for predicting fault and take pre-fault actions become quite important. Advanced protection and fault recognition techniques should be considered in adaptive energy management methods of AESs and seaport microgrids.

Above all, Energy saving and emission reduction is the general trend of ship development, and the development of multi-energy microgrid in all-electric ships is a choice that conforms to the future development trend of ships. At present, the global research on multi-energy microgrid in all-electric ships is not perfect, and multi-energy ships have great development potential and development space.

The in depth planning of port ecologicalization is the basis for the development of green ecological ports. From planning, payout to construction, the concept of scientific development should be adhere to. In the process of ecological port construction, there is no reasonable planning for local natural and biological resources. Therefore, it is necessary to embody the concept of ecological environmental protection in the daily operation of the port and the construction of the ecological wharf. On the one hand, it can realize the mutual balance between the local ecosystem and the port resources, and on the other hand, it can achieve the standard of the port and ship polluting waste.

6 Conclusion

References

• 1

  AccettaA.PucciM. (2019). Energy Management System in DC Micro-grids of Smart Ships: Main Gen-Set Fuel Consumption Minimization and Fault Compensation. IEEE Trans. Ind. Applicat.55, 3097–3113. 10.1109/TIA.2019.2896532

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  Ali ShahS. F.QyyumM. A.QadeerK.LeeM. (2021). Sustainable Economic Growth and export Diversification Potential for Asian LNG-Exporting Countries: LNG-Petrochemical Nexus Development Using Product Space Model. Energy236, 121334. 10.1016/j.energy.2021.121334

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  ApsleyJ. M.Gonzalez-VillasenorA.BarnesM.SmithA. C.WilliamsonS.SchuddebeursJ. D.et al (2009). Propulsion Drive Models for Full Electric marine Propulsion Systems. IEEE Trans. Ind. Applicat.45, 676–684. 10.1109/TIA.2009.2013569

  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  BalamuruganT.NaliniR. (2014). Experimental Investigation on Performance, Combustion and Emission Characteristics of Four Stroke Diesel Engine Using Diesel Blended with Alcohol as Fuel. Energy78, 356–363. 10.1016/j.energy.2014.10.020

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  BalcombeP.StaffellI.KerdanI. G.SpeirsJ. F.BrandonN. P.HawkesA. D. (2021). How Can LNG-Fuelled Ships Meet Decarbonisation Targets? an Environmental and Economic Analysis. Energy227, 120462. 10.1016/j.energy.2021.120462

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  BalliniF.ÖlçerA. I.BrandtJ.NeumannD. (2017). Health Costs and Economic Impact of Wind Assisted Ship Propulsion. Ocean Eng.146, 477–485. 10.1016/j.oceaneng.2017.09.014

  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  BierwirthC.MeiselF. (2015). A Follow-Up Survey of Berth Allocation and Quay crane Scheduling Problems in Container Terminals. Eur. J. Oper. Res.244, 675–689. 10.1016/j.ejor.2014.12.030

  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  BouaichaH.NejimS.DallagiH. (2018). “Optimal Economic and Pollution-Constrained Management of a Hybrid DC Shipboard Power System,” in 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 435–440. 10.1109/ASET.2018.8379896

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  ChenY.XuY.LiZ.FengX. (2019). Optimally Coordinated Dispatch of Combined‐heat‐and‐electrical Network with Demand Response. IET Generation, Transm. & Distribution13, 2216–2225. 10.1049/iet-gtd.2018.6992

  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  Man Diesel and Turbo (2021). Diesel Electric Propulsion Plants: a Brief Guideline How to Design a Diesel-Electric Propulsion Plant. Available online https:// marine. Mandieselturbo.com.

  • Google Scholar
  • View reference in article

• 11

  DoerryN.AmyJ.KrolickC. (2015). History and the Status of Electric Ship Propulsion, Integrated Power Systems, and Future Trends in the U.S. Navy. Proc. IEEE103, 2243–2251. 10.1109/JPROC.2015.2494159

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  Electric and solar powered boats (2021). Available: http://www.solarnavigator.net/solar_boats.htm (Accessed July 22, 2021).

  • Google Scholar
  • View reference in article

• 13

  EllisJ.TannebergerK. (2015). Study on the Use of Ethyl and Methyl Alcohol as Alternative Fuels in Shipping, Report Prepared for the European Maritime Safety Agency (EMSA).

  • Google Scholar
  • View reference in article

• 14

  FangS.FangY.WangH.ZhangS. (2020). “Adaptive Voyage-Generation Scheduling in Shipboard Microgrid under Time-Varied Weather Conditions,” in 2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS), 1–8. 10.1109/ICPS48389.2020.9176790

  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  FangS.XuY.LiZ. (2019b). “Joint Generation and Demand-Side Management for Shipboard Carbon Capture and Storage System,” in 2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS), 1–8. 10.1109/ICPS.2019.8733353

  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  FangS.XuY.LiZ.ZhaoT.WangH. (2019a). Two-Step Multi-Objective Management of Hybrid Energy Storage System in All-Electric Ship Microgrids. IEEE Trans. Veh. Technol.68, 3361–3373. 10.1109/TVT.2019.2898461

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  FernándezI. A.GómezM. R.GómezJ. R.InsuaÁ. B. (2017). Review of Propulsion Systems on LNG Carriers. Renew. Sust. Energ. Rev.67, 1395–1411. 10.1016/j.rser.2016.09.095

  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  FiadomorR. (2009). Assessment of Alternative Maritime Power (Cold-Ironing) and its Impact on Port Management and Operations. Ph.D. dissertation. Malmö, Sweden: Maritime Affairs Dept., World Maritime University.

  • Google Scholar
  • View reference in article

• 19

  FreireL. O.AndradeD. A. d. (2015). Historic Survey on Nuclear Merchant Ships. Nucl. Eng. Des.293, 176–186. 10.1016/j.nucengdes.2015.07.031

  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  GeertsmaR. D.NegenbornR. R.VisserK.HopmanJ. J. (2017). Design and Control of Hybrid Power and Propulsion Systems for Smart Ships: a Review of Developments. Appl. Energ.194, 30–54. 10.1016/j.apenergy.2017.02.060

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  GoelV.SluskyM.van HoeveW.-J.FurmanK. C.ShaoY. (2015). Constraint Programming for LNG Ship Scheduling and Inventory Management. Eur. J. Oper. Res.241, 662–673. 10.1016/j.ejor.2014.09.048

  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  GoharyM. M. E.SeddiekI. S. (2013). Utilization of Alternative marine Fuels for Gas Turbine Power Plant Onboard Ships. Int. J. Naval Architecture Ocean Eng.5, 21–32. 10.2478/IJNAOE-2013-0115

  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  HasanvandS.RafieiM.GheisarnejadM.KhoobanM.-H. (2020). Reliable Power Scheduling of an Emission-free Ship: Multiobjective Deep Reinforcement Learning. IEEE Trans. Transp. Electrific.6, 832–843. 10.1109/TTE.2020.2983247

  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  HeinK.XuY.WilsonG.GuptaA. K. (2020). "Condition-based Optimal Maintenance and Energy Management of All-Electric Ships," IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 3767–3772. 10.1109/IECON43393.2020.9254842

  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  HuangY.LanH.HongY.-Y.WenS.FangS. (2020). Joint Voyage Scheduling and Economic Dispatch for All-Electric Ships with Virtual Energy Storage Systems. Energy190, 116268. 10.1016/j.energy.2019.116268

  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  ImaiA.NishimuraE.PapadimitriouS. (2001). The Dynamic Berth Allocation Problem for a Container Port. Transportation Res. B: Methodological35, 401–417. 10.1016/S0191-2615(99)00057-0

  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  IMO. (2008). Revised MARPOL Annex VI: Regulations for the Prevention of Air Pollution from Ships.

  • Google Scholar
  • View reference in article

• 28

  IMO (2016). Studies on the Feasibility and Use of LNG as a Fuel for Shipping. Technical report. London: International Maritime Organisa-tion.

  • Google Scholar
  • View reference in article

• 29

  IMO-MEPC (2009). Guidelines for Voluntary Use of the Ship Energy Efficiency Operational Indicator (EEOI). Tech. Rep. MEPC.1, Circ.684.

  • Google Scholar
  • View reference in article

• 30

  ImranA.VarmanM.MasjukiH. H.KalamM. A. (2013). Review on Alcohol Fumigation on Diesel Engine: a Viable Alternative Dual Fuel Technology for Satisfactory Engine Performance and Reduction of Environment Concerning Emission. Renew. Sust. Energ. Rev.26, 739–751. 10.1016/j.rser.2013.05.070

  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  JoY.ShinK.HwangS. (2021). Development of Dynamic Simulation Model of LNG Tank and its Operational Strategy. Energy223, 120060. 10.1016/j.energy.2021.120060

  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  KanellosF. D.Anvari-MoghaddamA.GuerreroJ. M.Chun-LienS.GuerreroJ. M. (2017). A Cost-Effective and Emission-Aware Power Management System for Ships with Integrated Full Electric Propulsion. Electric Power Syst. Res.150, 63–75. 10.1016/j.epsr.2017.05.003

  • CrossRef
  • Google Scholar
  • View reference in article

• 33

  KanellosF. D. (2014). Optimal Power Management with GHG Emissions Limitation in All-Electric Ship Power Systems Comprising Energy Storage Systems. IEEE Trans. Power Syst.29, 330–339. 10.1109/TPWRS.2013.2280064

  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  KanellosF. D.VolanisE.-S. M.HatziargyriouN. D. (2019). Power Management Method for Large Ports with Multi-Agent Systems. IEEE Trans. Smart Grid10, 1259–1268. 10.1109/TSG.2017.2762001

  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  KhlopkinN. S.ZotovA. P. (1997). Merchant marine Nuclear-Powered Vessels. Nucl. Eng. Des.173, 201–205. 10.1016/S0029-5493(97)00109-X

  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  KontovasC. A. (2014). The green Ship Routing and Scheduling Problem (GSRSP): A Conceptual Approach. Transportation Res. D: Transport Environ.31, 61–69. 10.1016/j.trd.2014.05.014

  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  KramerJ. V.SteenS. (2022). Simplified Test Program for Hydrodynamic CFD Simulations of Wind-Powered Cargo Ships. Ocean Eng.244, 1–19. 10.1016/j.oceaneng.2021.110297

  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  KumarS.KwonH.-T.ChoiK.-H.LimW.ChoJ. H.TakK.et al (2011). LNG: An Eco-Friendly Cryogenic Fuel for Sustainable Development. Appl. Energ.88, 4264–4273. 10.1016/j.apenergy.2011.06.035

  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  LanH.WenS.HongY.-Y.YuD. C.ZhangL. (2015). Optimal Sizing of Hybrid PV/diesel/battery in Ship Power System. Appl. Energ.158, 26–34. 10.1016/j.apenergy.2015.08.031

  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  LanH.ZhangC.HongY.-Y.HeY.WenS. (2019). Day-ahead Spatiotemporal Solar Irradiation Forecasting Using Frequency-Based Hybrid Principal Component Analysis and Neural Network. Appl. Energ.247, 389–402. 10.1016/j.apenergy.2019.04.056

  • CrossRef
  • Google Scholar
  • View reference in article

• 41

  LiF.YuanY.YanX.MalekianR.LiZ. (2018). A Study on a Numerical Simulation of the Leakage and Diffusion of Hydrogen in a Fuel Cell Ship. Renew. Sust. Energ. Rev.97, 177–185. 10.1016/j.rser.2018.08.034

  • CrossRef
  • Google Scholar
  • View reference in article

• 42

  LiX.SunB.GuoC.DuW.LiY. (2020). Speed Optimization of a Container Ship on a Given Route Considering Voluntary Speed Loss and Emissions. Appl. Ocean Res.94, 101995. 10.1016/j.apor.2019.101995

  • CrossRef
  • Google Scholar
  • View reference in article

• 43

  LiX.SunB.ZhaoQ.LiY.ShenZ.DuW.et al (2018). Model of Speed Optimization of Oil Tanker with Irregular Winds and Waves for Given Route. Ocean Eng.164, 628–639. 10.1016/j.oceaneng.2018.07.009

  • CrossRef
  • Google Scholar
  • View reference in article

• 44

  LiZ.WuL.XuY. (2021). Risk-Averse Coordinated Operation of a Multi-Energy Microgrid Considering Voltage/Var Control and Thermal Flow: An Adaptive Stochastic Approach. IEEE Trans. Smart Grid12, 3914–3927. 10.1109/TSG.2021.3080312

  • CrossRef
  • Google Scholar
  • View reference in article

• 45

  LiZ.XuY.FangS.ZhengX.FengX. (2020). Robust Coordination of a Hybrid AC/DC Multi-Energy Ship Microgrid with Flexible Voyage and Thermal Loads. IEEE Trans. Smart Grid11, 2782–2793. 10.1109/TSG.2020.2964831

  • CrossRef
  • Google Scholar
  • View reference in article

• 46

  LiZ.XuY. (2018). Optimal Coordinated Energy Dispatch of a Multi-Energy Microgrid in Grid-Connected and Islanded Modes. Appl. Energ.210, 974–986. 10.1016/j.apenergy.2017.08.197

  • CrossRef
  • Google Scholar
  • View reference in article

• 47

  LinJ.WangZ.-J. (2019). Multi-area Economic Dispatch Using an Improved Stochastic Fractal Search Algorithm. Energy166, 47–58. 10.1016/j.energy.2018.10.065

  • CrossRef
  • Google Scholar
  • View reference in article

• 48

  LindstadH.EskelandG. S. (2015). Low Carbon Maritime Transport: How Speed, Size and Slenderness Amounts to Substantial Capital Energy Substitution. Transportation Res. Part D: Transport Environ.41, 244–256. 10.1016/j.trd.2015.10.006

  • CrossRef
  • Google Scholar
  • View reference in article

• 49

  MEPC-IMO (2009). Interim Guidelines on the Method of Calculation of the Energy Efficiency Design index for New Ships. Tech. Rep. MEPC.1, Circ 681.

  • Google Scholar
  • View reference in article

• 50

  MEPC-IMO.(2011). Reduction of GHG Emissions from Ships- Marginal Abatement Costs and Cost-Effectiveness of Energy-Efficiency Measures. Tech. Rep.61/inf. 18,

  • Google Scholar
  • View reference in article

• 51

  MontiA.D'ArcoS.GaoL.DougalR. A. (2008). “Energy Storage Management as Key Issue in Control of Power Systems in Future All Electric Ships,” in 2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 580–585. 10.1109/speedham.2008.4581218

  • CrossRef
  • Google Scholar
  • View reference in article

• 52

  NuchtureeC.LiT.XiaH. (2020). Energy Efficiency of Integrated Electric Propulsion for Ships - A Review. Renew. Sust. Energ. Rev.134, 1–25. 10.1016/j.rser.2020.110145

  • CrossRef
  • Google Scholar
  • View reference in article

• 53

  NyanyaM. N.VuHuH. B.SchönbornA.ÖlçerA. I. (2021). Wind and Solar Assisted Ship Propulsion Optimisation and its Application to a Bulk Carrier. Sustainable Energ. Tech. Assessments47, 1–12. 10.1016/j.seta.2021.101397

  • CrossRef
  • Google Scholar
  • View reference in article

• 54

  OECD and ITD (2018), Decarbonising Maritime Transport: Pathways to Zero-Carbon Shipping by 2035. Case-specific Policy Analysis Reports by the International Transport Forum.

  • Google Scholar
  • View reference in article

• 55

  Ortiz-ImedioR.CaglayanD. G.OrtizA.HeinrichsH.RobiniusM.StoltenD.et al (2021). Power-to-Ships: Future Electricity and Hydrogen Demands for Shipping on the Atlantic Coast of Europe in 2050. Energy228, 120660. 10.1016/j.energy.2021.120660

  • CrossRef
  • Google Scholar
  • View reference in article

• 56

  OthmanM.SuC.-L.Anvari-MoghaddamA.GuerreroJ. M.KifuneH.TengJ.-H. (2018). Scheduling of Power Generations for Energy Saving in Hybrid AC/DC Shipboard Microgrids. Proc. IEEE Ind. Appl. Soc. Annu. Meet. (Ias), 1–7. 10.1109/IAS.2018.8544723

  • CrossRef
  • Google Scholar
  • View reference in article

• 57

  PanH.PournazeriS.PrincevacM.MillerJ. W.MahalingamS.KhanM. Y.et al (2014). Effect of Hydrogen Addition on Criteria and Greenhouse Gas Emissions for a marine Diesel Engine. Int. J. Hydrogen Energ.39, 11336–11345. 10.1016/j.ijhydene.2014.05.010

  • CrossRef
  • Google Scholar
  • View reference in article

• 58

  PanP. C.SunY. W.YuanC. Q.YanX. P.TangX. J. (2021). Research Progress on Ship Power Systems Integrated with New Energy Sources: A Review. Renew. Sust. Energ. Rev.144, 111048. 10.1016/j.rser.2021.111048

  • CrossRef
  • Google Scholar
  • View reference in article

• 59

  Panasonic supplies green energy technology to ‘Emerald Ace’ hybrid car carrier (2012). Available: https://news.panasonic.com/global/topics/2012/9428.html.

  • Google Scholar
  • View reference in article

• 60

  PariseG.PariseL.MartiranoL.ChavdarianP. B.SuC.-L.FerranteA. (2014). Wise Port & Business Energy Management: Portfacilities, Electrical Power Distribution. Proc. IEEE Ind. Appl. Soc. Annu. Meet., 1–6. 10.1109/IAS.2014.6978475

  • CrossRef
  • Google Scholar
  • View reference in article

• 61

  PaulD.PetersonK.ChavdarianP. R. (2014). Designing Cold Ironing Power Systems: Electrical Safety during Ship Berthing. IEEE Ind. Appl. Mag.20, 24–32. 10.1109/MIAS.2013.2288393

  • CrossRef
  • Google Scholar
  • View reference in article

• 62

  PopeK.DincerI.NatererG. F. (2010). Energy and Exergy Efficiency Comparison of Horizontal and Vertical axis Wind Turbines. Renew. Energ.35, 2102–2113. 10.1016/j.renene.2010.02.013

  • CrossRef
  • Google Scholar
  • View reference in article

• 63

  PosthumusK. J. C. M.SchillemansR. A. A.KluitersE. C. (1996). Sodium-sulphur Batteries for Naval Applications. Proc. 11th Annu. Battery Conf. Appl. Adv., 301–306. 10.1109/BCAA.1996.485013

  • CrossRef
  • Google Scholar
  • View reference in article

• 64

  QiuY.YuanC.TangJ.TangX. (2019). Techno-economic Analysis of PV Systems Integrated into Ship Power Grid: A Case Study. Energ. Convers. Manag.198, 1–12. 10.1016/j.enconman.2019.111925

  • CrossRef
  • Google Scholar
  • View reference in article

• 65

  QyyumM. A.AhmedF.NawazA.HeT.LeeM. (2021). Teaching-learning Self-Study Approach for Optimal Retrofitting of Dual Mixed Refrigerant LNG Process: Energy and Exergy Perspective. Appl. Energ.298, 117187. 10.1016/j.apenergy.2021.117187

  • CrossRef
  • Google Scholar
  • View reference in article

• 66

  RafieiM.BoudjadarJ.KhoobanM.-H. (2021). Energy Management of a Zero-Emission Ferry Boat with a Fuel-Cell-Based Hybrid Energy System: Feasibility Assessment. IEEE Trans. Ind. Electron.68, 1739–1748. 10.1109/TIE.2020.2992005

  • CrossRef
  • Google Scholar
  • View reference in article

• 67

  SchinasO.ButlerM. (2016). Feasibility and Commercial Considerations of LNG-Fueled Ships. Ocean Eng.122, 84–96. 10.1016/j.oceaneng.2016.04.031

  • CrossRef
  • Google Scholar
  • View reference in article

• 68

  SciberrasE. A.ZahawiB.AtkinsonD. J. (2015). Electrical Characteristics of Cold Ironing Energy Supply for Berthed Ships. Transportation Res. Part D: Transport Environ.39, 31–43. 10.1016/j.trd.2015.05.007

  • CrossRef
  • Google Scholar
  • View reference in article

• 69

  SenD.PadhyC. P. (2015). An Approach for Development of a Ship Routing Algorithm for Application in the North Indian Ocean Region. Appl. Ocean Res.50, 173–191. 10.1016/j.apor.2015.01.019

  • CrossRef
  • Google Scholar
  • View reference in article

• 70

  ShangC.SrinivasanD.ReindlT. (2016a). Economic and Environmental Generation and Voyage Scheduling of All-Electric Ships. IEEE Trans. Power Syst.31, 4087–4096. 10.1109/TPWRS.2015.2498972

  • CrossRef
  • Google Scholar
  • View reference in article

• 71

  ShangC.SrinivasanD.ReindlT. (2016b). “NSGA-II for Joint Generation and Voyage Scheduling of an All-Electric Ship,” in 2016 IEEE Congress on Evolutionary Computation (CEC), 5113–5119. 10.1109/CEC.2016.7748338

  • CrossRef
  • Google Scholar
  • View reference in article

• 72

  SkjongE.VoldenR.RodskarE.MolinasM.JohansenT. A.CunninghamJ. (2016). Past, Present, and Future Challenges of the Marine Vessel's Electrical Power System. IEEE Trans. Transp. Electrific.2, 522–537. 10.1109/TTE.2016.2552720

  • CrossRef
  • Google Scholar
  • View reference in article

• 73

  Solar powered boats (2021). Available: http://www.daolar.com/ solar-energy/solar-powered-boats (Access July 22, 2021).

  • Google Scholar
  • View reference in article

• 74

  SulligoiG.VicenzuttiA.MenisR. (2016). All-Electric Ship Design: From Electrical Propulsion to Integrated Electrical and Electronic Power Systems. IEEE Trans. Transp. Electrific.2, 507–521. 10.1109/TTE.2016.2598078

  • CrossRef
  • Google Scholar
  • View reference in article

• 75

  Suntech solar powered ship (2021). Available: http://guangfu.bjx.com.cn/news/20130624/441632-6.shtml (Accessed July 22, 2021).

  • Google Scholar
  • View reference in article

• 76

  SvanbergM.EllisJ.LundgrenJ.LandälvI. (2018). Renewable Methanol as a Fuel for the Shipping Industry. Renew. Sust. Energ. Rev.94, 1217–1228. 10.1016/j.rser.2018.06.058

  • CrossRef
  • Google Scholar

• 77

  TalluriL.NaliandaD. K.KyprianidisK. G.NikolaidisT.PilidisP. (2016). Techno Economic and Environmental Assessment of Wind Assisted marine Propulsion Systems. Ocean Eng.121, 301–311. 10.1016/j.oceaneng.2016.05.047

  • CrossRef
  • Google Scholar

• 78

  TangC.HuF.ZhouX.LiY. (2022). Optimization Methods for Flexibility and Stability Related to the Operation of LNG Receiving Terminals. Energy250, 123620–123638. 10.1016/j.energy.2022.123620

  • CrossRef
  • Google Scholar

• 79

  The emerald Ace- Japan’s prius of the sea (2012). Available at: http://gizmodo.com/5921423/the-emerald-ace-japans-prius-of-the-sea

  • Google Scholar

• 80

  ThomsonH.CorbettJ. J.WinebrakeJ. J. (2015). Natural Gas as a marine Fuel. Energy Policy87, 153–167. 10.1016/j.enpol.2015.08.027

  • CrossRef
  • Google Scholar

• 81

  TilligF.RingsbergJ. W. (2020). Design, Operation and Analysis of Wind-Assisted Cargo Ships. Ocean Eng.211, 107603. 10.1016/j.oceaneng.2020.107603

  • CrossRef
  • Google Scholar

• 82

  Transatlantic SUN 21 (2021). Transatlantic Sun 21. Available: http://www.solarnavigator.net/transatlantic_21.htm/ (Access July 22, 2021).

  • Google Scholar

• 83

  TronstadT.ÅstrandH. H.HaugomG. P.LangfeldtL. (2017). Study on the Use of Fuel Cells in Shipping. Report to European Mari-time Safety Agency by DNV GL.

  • Google Scholar

• 84

  VettorR.Guedes SoaresC. (2016). Development of a Ship Weather Routing System. Ocean Eng.123, 1–14. 10.1016/j.oceaneng.2016.06.035

  • CrossRef
  • Google Scholar

• 85

  WangK.YanX.YuanY.JiangX.LinX.NegenbornR. R. (2018). Dynamic Optimization of Ship Energy Efficiency Considering Time-Varying Environmental Factors. Transportation Res. Part D: Transport Environ.62, 685–698. 10.1016/j.trd.2018.04.005

  • CrossRef
  • Google Scholar

• 86

  WangS.LiuZ.QuX. (2015). Collaborative Mechanisms for Berth Allocation. Adv. Eng. Inform.29, 332–338. 10.1016/j.aei.2014.12.003

  • CrossRef
  • Google Scholar

• 87

  WelayaY. M. A.MoslehM.AmmarN. R. (2013). Thermodynamic Analysis of a Combined Gas Turbine Power Plant with a Solid Oxide Fuel Cell for marine Applications. Int. J. Naval Architecture Ocean Eng.5, 529–545. 10.2478/IJNAOE-2013-0151

  • CrossRef
  • Google Scholar

• 88

  WenS.LanH.HongY.-Y.YuD. C.Zhang,L.ChengP. (2016). Allocation of ESS by Interval Optimization Method Considering Impact of Ship Swinging on Hybrid in PV/diesel Ship Power System. Appl. Energy175, 158–167. 10.1016/j.apenergy.2016.05.003

  • CrossRef
  • Google Scholar

• 89

  WenS.LanH.YuD. C.FuQ.HongY.-Y.YuL.et al (2017). Optimal Sizing of Hybrid Energy Storage Sub-systems in PV/diesel Ship Power System Using Frequency Analysis. Energy140, 198–208. 10.1016/j.energy.2017.08.065

  • CrossRef
  • Google Scholar

• 90

  WenS.ZhaoT.TangY.XuY.FangS.ZhuM.et al (2020). “Joint Energy Management and Voyage Scheduling for All-Electric Ships Using Dynamic Real-Time Electricity Price of Onshore Power,” in 2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS), 1–8. 10.1109/ICPS48389.2020.9176793

  • CrossRef
  • Google Scholar

• 91

  WenS.ZhaoT.TangY.XuY.ZhuM.FangS.et al (2021). Coordinated Optimal Energy Management and Voyage Scheduling for All-Electric Ships Based on Predicted Shore-Side Electricity Price. IEEE Trans. Ind. Applicat.57, 139–148. 10.1109/TIA.2020.3034290

  • CrossRef
  • Google Scholar

• 92

  XinY.ZhangY.XueP.WangK.AduE.TontiwachwuthikulP. (2021). The Optimization and Thermodynamic and Economic Estimation Analysis for CO2 Compression-Liquefaction Process of CCUS System Using LNG Cold Energy. Energy236, 121376. 10.1016/j.energy.2021.121376

  • CrossRef
  • Google Scholar

• 93

  XuS.GuZ.ShenW.LeiandQ.TangW. (2020). Experimental and Numerical Study on Ultimate Bearing Capacity of Pressure Cabin for Nuclear Power Ships. Ocean Eng.218, 1–20. 10.1016/j.oceaneng.2020.108123

  • CrossRef
  • Google Scholar

• 94

  YanX. P.SunY. W.YuanC. Q. (2016). Review on the Application Progress of Solar Ship Technology. Ship Ocean Eng.45, 50–54.

  • Google Scholar

• 95

  YangR.JiangL.DuK.ZhangY.WangL.LiK. (2020). “Research and Experimentation on Energy Management System for Inland Diesel-Electric Hybrid Power Ships,” in 2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT), 102–106. 10.1109/ICCSNT50940.2020.9305003

  • CrossRef
  • Google Scholar

• 96

  YangR.WeiH.WangL. (2021). “Research on Energy Regulation and Optimal Operation Strategy of Multi-Energy Ship Power Station Based on Improved Particle Swarm Algorithm,” in IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 294–298. 10.1109/IAEAC50856.2021.93906642021

  • CrossRef
  • Google Scholar

• 97

  YaoC.ChenM.HongY.-Y. (2018). Novel Adaptive Multi-Clustering Algorithm-Based Optimal ESS Sizing in Ship Power System Considering Uncertainty. IEEE Trans. Power Syst.33, 307–316. 10.1109/TPWRS.2017.2695339

  • CrossRef
  • Google Scholar

• 98

  YigitK.AcarkanB. (2018). A New Electrical Energy Management Approach for Ships Using Mixed Energy Sources to Ensure Sustainable Port Cities. Sust. Cities Soc.40, 126–135. 10.1016/j.scs.2018.04.004

  • CrossRef
  • Google Scholar

• 99

  YinQ.LiM.YuanY.WuP.LiuG.BucknallR. (2019). “The Design of Energy Efficiency Management System for an Electric Pro-pulsion Passenger Ship in Inland River,” in 2019 5th International Conference on Transportation Information and Safety (ICTIS), 1453–1462.

  • Google Scholar

• 100

  YuanY.WangJ.YanX.LiQ.LongT. (2018). A Design and Experimental Investigation of a Large-Scale Solar Energy/diesel Generator Powered Hybrid Ship. Energy165, 965–978. 10.1016/j.energy.2018.09.085

  • CrossRef
  • Google Scholar

• 101

  ZacconeR.OttavianiE.FigariM.AltosoleM. (2018). Ship Voyage Optimization for Safe and Energy-Efficient Navigation: A Dynamic Programming Approach. Ocean Eng.153, 215–224. 10.1016/j.oceaneng.2018.01.100

  • CrossRef
  • Google Scholar

• 102

  ZapałowiczZ.ZeńczakW. (2021). The Possibilities to Improve Ship's Energy Efficiency through the Application of PV Installation Including Cooled Modules. Renew. Sust. Energ. Rev.143, 1–16. 10.1016/j.rser.2021.110964

  • CrossRef
  • Google Scholar

• 103

  ZhenL.ShenT.WangS.YuS. (2016). Models on Ship Scheduling in Transshipment Hubs with Considering bunker Cost. Int. J. Prod. Econ.173, 111–121. 10.1016/j.ijpe.2015.12.008

  • CrossRef
  • Google Scholar

• 104

  ZhenL. (2015). Tactical Berth Allocation under Uncertainty. Eur. J. Oper. Res.247, 928–944. 10.1016/j.ejor.2015.05.079

  • CrossRef
  • Google Scholar