Optimal Planning of Integrated NuclearRenewable Energy System for Marine Ships Using Artificial Intelligence Algorithm
Abstract
:1. Introduction
2. System Modeling
2.1. Ship, Route Description, and Energy Estimation
2.2. Diesel Generator
2.3. Solar Energy
2.4. Wind Power
2.5. vSMR/MR
2.6. Electrochemical Energy Storage
3. Key Performance Indicators
3.1. Net Present Cost (NPC)/Net Present Value (NPV)
3.2. Levelized Cost of Energy (LCOE)
3.3. Generation Reliability Factor (GRF)
3.4. Loss of Power Supply Probability (LPSP)
3.5. CO_{2} Gas Emissions
3.6. Power to Weight Ratio (PWR)
4. Problem Formation
4.1. Objective Function
4.2. Constraints
4.3. Decision Variables
4.4. Implementation of Optimization Algorithm: Differential Evolution
Step 1:  Read the following input data of the proposed energy systems:

Step 2:  Initialize the parameters of DE and required system components:

Step 3:  Generate the initial solutions randomly within the boundary of the decision variables. The number of the randomly generated solution is equal to the population size. These solutions are called target solutions. 
Step 4:  Generate the donor solutions by mutating the target solutions. 
Step 5:  Generate the trail solutions by recombing the donor the solutions (crossover). 
Step 6:  Bound the trial solutions within the boundary of the decision variables. 
Step 7:  Evaluate the objective function for the target solutions and trial solutions. The solution that gives the lower value (minimization problem) of the objective function is selected as the target solution for the next iteration. Store the best cost value (lowest value) in every iteration. 
Step 8:  When the number of iteration reaches the maximum limit of the iteration, then stop. Otherwise, continue from Step 3 to Step 7. After completing all the iteration, the best objective function value is obtained, and the associated solution is the global optimal solution. 
4.5. Adaptive Differential Evolution
4.6. Implementation of Optimization Algorithm: Particle Swarm Optimization (PSO)
Step 1:  Read the following input data of the proposed energy systems:

Step 2:  Initialize the parameters of DE and required system components:

Step 3:  Generate the initial solutions randomly within the boundary of the decision variables. 
Step 4:  Use the individual particle position to determine the objective function value. 
Step 5:  Update the best position of the individual particle by comparing it with all other populations. 
Step 6:  Compare the personal best with the global best and update the global best with the individual particle’s personal best that has the minimum value of the objective function. 
Step 7:  Update the velocity of the individual particle. 
Step 8:  Update the position of the individual particle. 
Step 9:  Repeat step 3 to step 8 till all particles are evaluated in each iteration. Store the best cost value. 
Step 10:  Stop the simulation if it reaches the maximum number of iterations. 
5. Results
5.1. Comparison among the Proposed Hybrid Energy Systems
5.2. Comparison and Validation of Optimization Techniques
6. Sensitivity Analysis
6.1. Sensitivity Assessment of Discount Rate on NPC
6.2. Sensitivity Assessment of Inflation Rate on NPC
6.3. Sensitivity Assessment of Project Lifetime on NPC
6.4. Sensitivity Assessment of Electrical Power Requirement on NPC
6.5. Sensitivity Assessment of Solar Irradiance on NPC
6.6. Sensitivity Assessment of Wind Speed on NPC
7. Conclusions, Contribution and Future Scope of Work
 ➢
 A comprehensive literature review on the impact of maritime transportation on the environment, renewable and fossil fuelbased hybrid energy systems, nuclear propulsion, development of NR HES in landbased applications to identify the problem and gaps.
 ➢
 Estimate the energy requirement of an oil tanker with the collaboration of industry (FleetMon) to address a realworld problem in this study.
 ➢
 Four energy systems are introduced to determine the most feasible energy system for maritime transportations. The feasibility is assessed based on technical, economical, and environmental KPIs. To ensure the reliability of the energy system, certain constraints are added.
 ➢
 The economical model of all the system components is developed mathematically. This mathematical model is then optimized in the popular and versatile simulator MATLAB. The Differential Evolution (DE) algorithm is used to optimize the energy systems.
 ➢
 The performance of the DE algorithm is compared to another optimization technique, PSO. Also, the impact of the control parameters of the DE algorithm is measured by the Adaptive Differential Algorithm (ADE).
 ➢
 To address the variability of the parameters like discount rate, inflation rate, project lifetime, electrical power requirements, solar irradiance, and wind speed, a sensitivity analysis is conducted to reinforce the findings.
 ➢
 In this study, average solar irradiance, temperature, and wind speed are used while optimizing the systems. In the future, the actual solar irradiance, temperature, and wind speed can be used to make the study more realistic.
 ➢
 In this study, a specific ship route is considered. However, analysis can be extended to different major international shipping routes to assess the most feasible route for NR HES.
 ➢
 The safety of the NR HES can be assessed in the future to check the feasibility in maritime transportation.
 ➢
 The licensing procedure and port approval of nuclearpowered merchant ships need to be addressed.
 ➢
 In this study, no secondary commodities are considered to utilize the excess heat of the nuclear reactor. In the future, the excess energy can be utilized to produce secondary commodities like seawater desalination or utilize it by using in heating and cooling system.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
AIS  Automatic Identification System 
FFG  Fossil Fuelbased Generator 
GHG  Greenhouse Gas 
GRF  Generation Reliability Factor 
HES  Hybrid Energy System 
IAEA  International Atomic Energy Agency 
KPI  Key Performance Indicator 
LBP  Length Between Perpendiculars 
MR  Microreactor 
MT  Metric Ton 
NASA  National Aeronautics and Space Administration 
NPC  Net Present Cost 
NPP  Nuclear Power Plant 
NPV  Net Present Value 
NR HES  NuclearRenewable Hybrid Energy System 
NR HES  NuclearRenewable Hybrid Energy System 
RES  Renewable Energy Source 
RINA  Royal Institution of Naval Architects 
SMR  Small Modular Reactor 
Appendix A
SL. No  Parameter/Assumption  Category  Notation  Value  Reference 

1  Ship beam  Parameter  B  60 m  [72] 
2  Volume displacement  Parameter  v  344,649.08 m^{3}  [72] 
3  Draught  Parameter  D  21.6 m  [15] 
4  Extreme breadth (Beam)  Parameter  Bex  60.04 m  [72] 
5  Average draught  Parameter  D_avg  16.15 m  [15] 
6  Length between perpendiculars  Parameter  LBP  324 m  [72] 
7  Gravitational acceleration  Parameter  g  9.81 m/s^{2}  
8  Seawater density at 30 °C temperature  Parameter  ρ_{w}  1021.7 kg/m^{3}  [17] 
9  Seawater viscosity at 30 °C temperature  Parameter  γ_{w}  0.84931 × 10^{−6} m^{3}s^{−1}  [17] 
10  Average speed  Parameter  Vs_avg  11.94 kn or 6.1424 ms^{−1}  [15] 
11  Incremental resistance coefficient due to surface roughness of ship  Assumption  C_{A}  0.0004  [17] 
12  Maximum speed  Parameter  Vs_max  17.9 kn or 9.2185 ms^{−1}  [15] 
Appendix B
Appendix C
Appendix D
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Parameter  Case01  Case02  Case03  Case04 

NPC ($)  877,605,291.05  875,903,862.16  538,049,423.31  532,112,182.13 
LCOE ($/MWh)  277.96  276.12  165.64  164.45 
Generator/MMR (MW)  28.15  28.14  28.96  28.00 
Solar PV (kW)  0.00  396.79  0.00  0.0885334416 
Wind (kW)  0.00  0.00  0.00  3050.30 
Battery (kWh)  2341.38  3907.92  637.17  1351.92 
Solar ratio  N/A  0.10  N/A  0.000022 
Energy System Weight (kg)  1,546,864.99  1,545,210.58  445,816.43  779,799.66 
LPSP  0.080  0.079  0.080  0.08 
CO2 Emissions (ton/year)  144,714.33  144,655.35  967.73  935.57 
Wratio  0.005  0.005  0.001  0.0025 
GRF  141.31  141.98  145.38  144.82 
Capital Cost ($)  26,083,959.77  25,882,395.10  313,231,568.67  308,562,984.45 
Replacement Cost ($)  131,028,335.96  129,934,105.16  651,048.07  3,710,762.34 
Operation and Maintenance Cost ($)  16,414,843.54  16,310,161.71  154,912,891.56  152,358,576.04 
Fuel Cost ($)  637,771,484.30  637,511,569.11  32,482,148.04  31,402,690.09 
Decommissioning Cost ($)  N/A  N/A  16,241,074.02  15,701,345.05 
Refuel Cost ($)  N/A  N/A  20,087,311.63  20,087,311.63 
Carbon Penalty ($)  66,302,976.09  66,275,955.20  443,381.32  428,646.72 
Salvage Value ($)  0.00  10,324.12  0.00  140,134.20 
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Gabbar, H.A.; Adham, M.I.; Abdussami, M.R. Optimal Planning of Integrated NuclearRenewable Energy System for Marine Ships Using Artificial Intelligence Algorithm. Energies 2021, 14, 3188. https://doi.org/10.3390/en14113188
Gabbar HA, Adham MI, Abdussami MR. Optimal Planning of Integrated NuclearRenewable Energy System for Marine Ships Using Artificial Intelligence Algorithm. Energies. 2021; 14(11):3188. https://doi.org/10.3390/en14113188
Chicago/Turabian StyleGabbar, Hossam A., Md. Ibrahim Adham, and Muhammad R. Abdussami. 2021. "Optimal Planning of Integrated NuclearRenewable Energy System for Marine Ships Using Artificial Intelligence Algorithm" Energies 14, no. 11: 3188. https://doi.org/10.3390/en14113188