Developments, Trends, and Challenges in Optimization of Ship Energy Systems
Abstract
:1. Introduction
“Because the goal is not the last, but the best.”
2. Concepts, Definitions, and Limits of the Present Article
2.1. The Energy System and Its Optimization Levels
- Synthesis optimization. The term synthesis refers to the components that appear in a system and their functional interconnections. With synthesis optimization, the optimal configuration of the system is determined and the flow diagram of the system can be drawn.
- Design optimization. The word design here is used to imply the technical characteristics (specifications) of the components and the properties of the substances entering and exiting each component at the design point (nominal load) of the system. One may argue that design includes synthesis too. However, in order to distinguish the three levels of optimization, the word “design” will be used with the particular meaning given here.
- Operation optimization. For a given system (i.e., one in which the synthesis and design are known) under specified conditions, the optimal operating state at each instant of time is requested, as it is defined by the operating properties of components and substances in the system (speed of revolution, power output, mass flow rates, pressures, temperatures, composition of fluids, etc.).
What is the synthesis of the system, the design specifications of the components, and the operating state at each instant of time that lead to an overall optimum?
2.2. Intertemporal Optimization
Intertemporal optimization is the optimization that takes into consideration the various operating conditions that a system encounters throughout its life time and determines the operating state at each instant of time that results in the overall minimum or maximum of the general objective function.
3. Mathematical Statement and Solution Methods of the Optimization Problem
3.1. The Static Optimization Problem
3.1.1. Mathematical Statement and Solution Methods of the Static Optimization Problem
- v
- set of independent variables for operation optimization (load factors of components, mass flow rates, pressures and temperatures of streams, etc.),
- w
- set of independent variables for design optimization (nominal capacities of components, mass flow rates, pressures and temperatures of streams, etc.),
- z
- set of independent variables for synthesis optimization; there is only one variable of this type for each component, indicating whether the component exists in the optimal configuration or not; it may be a binary (0,1), an integer, or a continuous variable such as the rated power of a component, with zero value indicating the nonexistence of a component in the final configuration.
3.1.2. Intertemporal Static Optimization
3.2. Dynamic Optimization
3.2.1. Mathematical Statement of the Dynamic Optimization Problem
- J
- scalar objective functional
- z
- differential state profile vector
- y
- algebraic state profile vector
- u
- control (independent variables) profile vector
- w
- time-independent variables vector
- tf
- final time.
3.2.2. Solution Methods of the Dynamic Optimization Problem
3.2.3. Intertemporal Dynamic Optimization
3.3. Multiobjective Optimization
4. Historical Development of Optimization of Ship Energy Systems
4.1. Articles Published in Journals
4.2. Type of Optimization Performed
4.3. Design Point Versus Optimization based on Operating Profile–Time in Optimization
4.4. Objective Functions
4.5. Optimization Algorithms
5. Further Research Needs and Challenges in Optimization of Ship Energy Systems
5.1. Synthesis Optimization
5.1.1. On the Possibility of Finding the Optimal Configuration
“Given the multitude of energy system types and the variations in each type, one may question whether it is ever possible to replace the experienced designer’s mental process with an algorithm consisting of a set of formulae and rules. On the other hand, in today’s complex world, this same multitude of types and variations makes it rather impossible even for an experienced designer to evaluate all possible alternatives. Consequently, an automated procedure, if properly used, can be of invaluable help to the designer.”
5.1.2. Methods and Applications of Synthesis Optimization of Ship Energy Systems–Challenges and Opportunities
5.1.3. SDO Optimization Including Synthesis Optimization of Working Fluids
5.2. SDO Optimization Including Transients
5.3. Optimization with Uncertainty Considerations
5.4. Optimization with Consideration of System Reliability
“Reliability is the probability that an item will perform a required function under stated conditions for a stated period of time.”
5.5. Optimization of Maintenance Schedule in Itself or Embedded in SDO Optimization
5.6. Optimization in Modeling and Modeling for Optimization
6. Functional and Physical Expansion of the System Boundary
6.1. Inclusion of Electrical and Control Engineering Aspects
6.2. Towards Holistic Ship Optimization
7. Closure
- Synthesis optimization is always a challenging endeavor: how to support and/or replace the designer’s experience, ingenuity, and inspiration with a computer code?
- Further development of both superconfiguration-based and superconfiguration-free methods for synthesis optimization is needed.
- Synthesis optimization of working fluids has appeared recently on land installations. Its incorporation in the synthesis optimization of the whole system is worth pursuing.
- There is need of further development of methods for SDO optimization of ship energy systems taking into consideration
- transients,
- uncertainties (e.g., in operating profile, cost of fuel, weather conditions, etc.),
- reliability–availability of equipment,
- combinations of the preceding.
- Interdisciplinary approach needs to be followed, so that aspects not only of mechanical engineering but also of other disciplines such as electrical and control engineering are taken into consideration in optimization.
- The border of the system to be optimized needs to be further and further expanded, with the aim of including the ship as a whole.
Funding
Conflicts of Interest
Symbols and Abbreviations
A | Heat transfer area |
AC | Alternating current |
AM-CCPS | Adaptive Multicontext Cooperatively Coevolving Particle Swarm Optimization Algorithm |
APC | Absorption Power Cycle |
AR | Absorption Refrigeration |
BOG | Boil-Off Gas |
COBYLA | Constraint Optimization BY Linear Approximation |
COGES | Combined Gas turbine, Electric generator and Steam turbine |
CPP | Controllable Pitch Propeller |
D | Design |
DC | Direct current |
DDP | Deterministic Dynamic Programming |
DE | Diesel engine |
DF | Dual fuel |
DG | Diesel-generator |
DSOx | Desulphurization |
DNOx | Denitrification |
total exergy supplied to the system | |
EEDI | Energy Efficiency Design Index |
EEOI | Energy Efficiency Operational Indicator |
EG | Exhaust gas |
EGR | Exhaust gas recirculation |
ESS | Energy storage system |
FC | Fuel cell |
fmincon | Find minimum of constrained nonlinear multivariable function |
FPP | Fixed pitch propeller |
GAMS | General Algebraic Modeling System |
GE | Genetic Algorithm |
GHG | Greenhouse gas |
GRG | Generalized Reduced Gradient |
GT | Gas turbine |
GWP | Global Warming Potential |
GUROBI | Zonghao Gu, Edward Rothberg, Robert Bixby |
HESS | Hybrid Energy Storage System |
HRSG | Heat Recovery Steam Generator |
IES | Integrated Energy System |
INTLAB | Interval Laboratory |
ISCA | Improved Sine Cosine Algorithm |
JCW | Jacket cooling water |
JW | Jacket water |
KC | Kalina cycle |
LNG | Liquefied natural gas |
MCFC | Molten Carbonate Fuel Cell |
MCR | Maximum Continuous Rating |
MIDACO | Mixed Integer Distributed Ant Colony Optimization Solver |
MINLP | Mixed Integer Nonlinear Programming |
MOPSO | Multiobjective Particle Swarm Optimization |
Nlopt | Nonlinear optimization |
NSGA | Nondominated Sorting Genetic Algorithm |
NPV | Net present value |
O | Operation |
ORC | Organic Rankine Cycle |
PEM | Proton exchange membrane |
PSO | Particle swarm optimization |
PT | Power turbine |
PV | Photovoltaics |
PWC | Present worth cost |
Ren | Renewable |
RO | Reverse osmosis |
S | Synthesis |
SCBC | Supercritical CO2 Brayton cycle |
SQP | Sequential Quadratic Programming |
StrGA | Struggle Genetic Algorithm |
U | Overall heat transfer coefficient |
Net power output | |
WHR | Waste heat recovery |
WJ | Water jet |
Appendix A. Main Characteristics of Journal Articles on Optimization of Ship Energy Systems
- [37] Dimopoulos, G.G.; Frangopoulos, C.A. International Journal of Thermodynamics 2008. System: superconfiguration of a COGES system of an LNG carrier with high-power gas turbines, moderate power gas turbines, HRSG units of single pressure, HRSG units of double pressure, one steam turbine. SDO optimization. Five operating states. Objective: maximize NPV. Service speed among the independent variables. Solution with two levels: A synthesis and design and (B) operation with time decomposition. Algorithm: a hybrid of Particle Swarm Optimization (PSO) and Struggle Genetic Algorithm (StrGA).
- [18] Dimopoulos, G.G.; Kougioufas, A.V.; Frangopoulos, C.A. Energy 2008. System: superconfiguration of a COGES system of a cruise liner with high-power gas turbines, moderate power gas turbines, HRSG units of single pressure, HRSG units of double pressure, and one steam turbine. SDO optimization. Solution with two levels: A synthesis and design and B operation with time decomposition. Three operating states. Objective: minimize total annualized cost. Algorithm: a hybrid of Particle Swarm Optimization (PSO) and Struggle Genetic Algorithm (StrGA).
- [38] Dimopoulos, G.G.; Stefanatos, I.C.; Kakalis, N.M.P. Energy 2013. System: steam methane prereformer for marine molten carbonate fuel cells (MCFC) fueled by liquefied natural gas (LNG). Design optimization at nominal operating conditions. Objective: minimization of the total irreversibility of the system subject to design, space, technical, and operational constraints. Algorithm: SQP implemented in gPROMS software.
- [39] Larsen, U.; Pierobon, L.; Haglind, F.; Gabrielii, C. Energy 2013. System: ORC with heat from exhaust gases of a diesel engine of MCR 72,240 kW. Synthesis and design (boiler pressure only) optimization: determine the optimum working fluid, boiler pressure, and Rankine process layout. Objective: maximize thermal efficiency. Algorithm: GA.
- [40] Larsen, U.; Nguyen, T.; Knudsen, T.; Haglind, F. Energy 2014. System: Kalina cycle with heat from exhaust gases of a large diesel engine. Design optimization (at nominal conditions) of four variations of the cycle: Reference cycle, Split cycle, Reference cycle with reheat, Split cycle with reheat. Objective: maximize net power output (equivalent to maximum efficiency, because the inlet and outlet temperatures of the exhaust gases are considered fixed). Algorithm: GA.
- [41] Baldi, F.; Larsen, U.; Gabrielii, C. Ocean Engineering 2015. System: initial—two diesel main engines driving one propeller and one shaft generator and two DG sets of a Panamax product/chemical tanker. Retrofit: ORC operating on exhaust gas heat of the main engines. Design and operation optimization. Discretized annual load profile. Objective: minimization of fuel consumption. Economic evaluation is performed after optimization, for the optimal system. Algorithm: GA.
- [19] Kalikatzarakis, M.; Frangopoulos, C.A. International Journal of Thermodynamics 2015. System: ORC with heat from the cooling circuit (charge air, cylinder cooling water, and lubricating oil). Evaluation criteria: technical, economic, environmental. Design and operation optimization at two levels. Four operating modes. Objectives: Operation—maximize the net savings in each one of the four modes of operation considered. Design—maximize NPV. Algorithm: at each level, GA determines a near optimal solution, while GRG2 determines the optimal solution.
- [42] Lan, H.; Wen, S.; Ying-Yi Hong, Y.-Y.; Yu, D.C.; Zhang, L. Applied Energy 2015. System: PV, DG set, and battery on an oil tanker. Electricity production only (propulsion by steam turbine). Design optimization (sizing of PV and battery, while the power of the DG set is predetermined so that it covers the loads completely). Five operating states (electric load) are considered. Biobjective: minimize total cost and minimize CO2 emissions. Algorithm: Multiobjective Particle Swarm Optimization (MOPSO) combined with elitist Nondominated Sorting Genetic Algorithm (NSGA-II).
- [43] Soffiato, M.; Frangopoulos, C.A.; Manente, G.; Rech S.; Lazzaretto, A. Energy Conversion and Management 2015. System: ORC operating on low-temperature waste heat of main engines of an LNG carrier. Design optimization of each of the alternative configurations and selection of the best one. Objective: maximization of the net power output. Method: HEATSEP for the heat exchangers part. Software: MATLAB Simulink and fmincon MATLAB function with SQP.
- [44] Solem, S.; Fagerholt, K.; Erikstad, S.O.; Patricksson, Ø. Journal of Marine Science and Technology 2015. System: a set of DG sets (diesel-electric machinery). Synthesis design and operation optimization considering lifetime of 20 years, divided in time periods and operational states. The number (configuration), the nominal power, and the load of each DG set throughout the lifetime are determined. Objective function: minimize (investment + operation costs). NOx tax is included, if applicable. Algorithm: commercial solver by means of the branch and bound technique.
- [45] Baldi, F.; Ahlgren, F.; Melino, F.; Gabrielii, C.; Andersson, K. Energy Conversion and Management 2016. System: four diesel engines connected to two propeller shafts, four diesel-generator sets, exhaust gas boilers, two auxiliary boilers, and one compression chiller of a cruise ship. Operation optimization. In addition to the current system, the installation of a shaft generator/motor on each of the two propeller shafts is studied. Objective: minimization of fuel consumption. The engines are grouped in engines of equal size and performance. In each group, the operating engines are equally loaded. Independent variables: the number of operating engines in each group (integer) and the total load of each group (continuous). Algorithm: the problem is of MINLP. SQP of MATLAB is used for the nonlinear part and a branch-and-bound method is applied for handling the integer variables.
- [46] Benvenuto, G.; Trucco, A.; Campora, U. Journal of Engineering for the Maritime Environment 2016. System: WHR system consisting of a power turbine and double-pressure Rankine cycle of a Suezmax crude oil tanker. Design optimization at nominal conditions and performance evaluation at off-design conditions. Objective: maximize total efficiency (including the power of the diesel engine). Method: systematic variation of certain variables, in a range of values of each one. One variable at a time. No optimization algorithm was used.
- [47] Dimopoulos, G.G.; Stefanatos, I.C.; Kakalis, N.M.P. Energy Conversion and Management 2016. System: molten carbonate fuel cell (MCFC) simple or combined with steam cycle cogeneration system of an offshore supply vessel. Design optimization. Objective: maximization of exergetic efficiency. Algorithm: SQP implemented in gPROMS software.
- [48] Wang, K.; Yan, X.; Yuan, Y.; Li, F. Transportation Research-Part D 2016. System: main engine of a cruise ship on the Yangtze river. Speed optimization only in real time. Before applying optimization, wavelet neural network is used to predict wind speed and water depth for a short time ahead. Objective: minimize fuel consumption of main and auxiliary engines, as well as cost of sailing time from port A to port B, converted to equivalent fuel consumption. Method: dynamic optimization (details of algorithm are not given).
- [49] Wen, S.; Lan, H.; Hong, Y.-Y.; Yu, D.C.; Zhang, L.; Cheng, P. Applied Energy 2016. Experimental work in the laboratory: performance evaluation of PV on a swinging platform. System: PV modules, diesel generator, and a NaS battery as ESS. Five loading conditions are considered pertinent to an oil tanker. Design (battery sizing only) and operation optimization. Objective: minimize cost of fuel + cost of battery. Independent variables are the power output of the diesel generator and the capacity of the battery. Method: interval optimization. Algorithm: INTLAB-Version 5.5 with MATLAB.
- [50] Yang, M.-H. Energy 2016. System: transcritical ORC using heat from exhaust gas, cylinder cooling water, scavenge air cooling water, and lubricating oil from a marine diesel engine of 68,640 kW in a merchant ship. Various fluids are investigated. Design optimization. Objective: minimize levelized energy cost (LEC). Independent variables: pressure and temperature of the working fluid at the inlet of the expander. Solution procedure: first derivative of LEC with respect to each independent variable equal to zero.
- [51] Kalikatzarakis, M.; Frangopoulos, C.A. Journal of Engineering for the Maritime Environment 2017. System: ORC with heat from the cooling circuit (charge air, cylinder cooling water, and lubricating oil) of a containership. SDO optimization: four operating states. Partial load performance of the ME. The HEATSEP method is used for optimization of the heat exchanger network. Objective: maximize NPV. Algorithm: GA determines a near optimal solution, while GRG2 determines the optimal solution.
- [52] Kyriakidis, F.; Sørensen, K.; Singh, S.; Condra, T. Energy Conversion and Management 2017. System: combination of EGR with Rankine cycle using heat from the cooling circuits and exhaust gases of the two-stroke ME (MCR 23,000 kW). Two configurations are studied: (1) with double-pressure steam turbine and (2) with triple-pressure steam turbine. Design optimization. No partial load performance. Objective: maximize net power output. Algorithms: SQP for configuration 1 and GA for configuration 2.
- [53] Nemati, A.; Sadeghi, M.; Yari, M. Desalination 2017. System: two-stage ORC using heat only of the exhaust gases of the diesel engine and reverse osmosis desalination unit. Design optimization at nominal point. Biobjective: maximize exergetic efficiency and minimize total unit product cost. Algorithm: GA.
- [54] Rech, S.; Zandarin, S.; Lazzaretto, A.; Frangopoulos, C.A. Applied Energy 2017. System: three configurations of ORC operating on waste heat of Marine Diesel Engines. Modeling: design and off-design, steady-state, and dynamic models. Design and operation optimization taking partial load into consideration. Alternative configurations are compared. Four operating states. Objective: maximization of the net power output of the ORC. The system is so designed that it reaches stable operation after transient conditions (change of ship speed and, consequently, power output of the main engines) in a reasonable length of time.
- [55] Sharma, O.P.; Kaushik, S.C.; Manjunath, K. Thermal Science and Engineering Progress 2017. System: supercritical CO2 regenerative recompression Brayton cycle (RRCBC) with heat from marine gas turbine. Energy and exergy analysis. Optimization: only the pressure ratio for maximizing efficiency at design point is determined by drawing related diagrams.
- [56] Wen, S.; Lan, H.; Yu, D.C.; Fu, Q.; Hong, Y.-Y.; Yu, L.; Yang, R. Energy 2017. System: diesel generator and PV with hybrid energy storage comprising lead-acid battery, Li-ion battery, and supercapacitor in an oil tanker. The motion of the ship, including rolling, is taken into consideration in calculating the electric power produced by the PVs. Method of analysis: a frequency analysis of the imbalanced power, i.e., the power not covered by the PVs, is performed by Discrete Fourier Transform (DFT). Three types of storage technologies are considered for the HESS: lead-acid battery, which has high energy density, is suitable for mitigating the relatively slowly varying component of imbalanced power. Lithium ion batteries respond faster and exhibit high cycle efficiencies. Therefore, they can be used to accommodate the component of variation of imbalanced power corresponding to the middle-frequency range. Owing to its excellent performance and rapid response, a supercapacitor is employed to smooth out the most dramatic power surges. Design optimization. Profile continuous in time. Objective: minimize total cost of the ESS. Independent variables: the cut-off frequencies, which affect the rated power and capacity of each storage technology. Algorithm: PSO.
- [57] Ancona, M.A.; Baldi, F.; Bianchi, M.; Branchini, L.; Melino, F.; A. Peretto, A.; Rosati, J. Energy Conversion and Management 2018. System: four diesel engines connected to two propeller shafts, four diesel-generator sets, exhaust gas boilers, two auxiliary boilers, and one compression chiller on a cruise ship. Operation optimization. In addition to the current system, alternative configurations have been considered: (a) mechanical propulsion with thermal storage, (b) electric propulsion, (c) electric propulsion with thermal storage, and (d) electric propulsion with thermal storage and absorption chiller. Objective: maximization of efficiency (minimization of fuel consumption). Independent variables: the load of each engine. In case of thermal storage, the volume of the storage tank is an independent variable too. Algorithm: Energy Grids Optimizer (EGO) using a GA.
- [58] Gao, D.; Wang, X.; Wang, T.; Wang, Y.; Xu, X. Energies 2018. System: two diesel generators, AC/DC, DC/DC, DC/AC converters, lithium batteries, DC bus, propeller, and propulsion controller. Experimental setting only. Examples of ships mentioned: ferries and water buses. Multiobjective operation optimization. The optimal load allocation among the two diesel generators and the batteries is determined for a single operation cycle of 120 min of the ship including start-up, acceleration, full speed ahead, deceleration, and stop. Objectives: minimize fuel consumption, minimize emissions, and maximize endurance (as it is expressed by the difference between the initial and final state of charge of the batteries: minimum difference). Algorithm: improved NSGA-II Algorithm.
- [59] Kwak, D.-H.; Heo, J.-H.; Park, S.-H.; Seo, S.-J.; Kim, J.-K. Energy 2018. System: small-scale boil-off gas reliquefaction plant on an LNG-fueled ship. Design optimization at nominal conditions. Objective: minimization of the work required per unit mass of BOG reliquefied. Algorithm and software: GA in MATLAB.
- [60] Nour Eddine, A.; Chalet D.; Faure X.; L. Aixala, L.; Chess, P. Energy 2018. System: experimental set up with automotive engine, simulating performance of a marine engine. Two thermoelectric materials were tested. The clamping pressure was changed in a certain region, and the point of maximum power was determined. The effect of load resistance and voltage of the thermoelectric generator were investigated. Not a formal, mathematical optimization.
- [7] Sakalis, G.N.; Frangopoulos, C.A. Applied Energy 2018. System: superconfiguration including diesel engines, bottoming Rankine cycle, exhaust gas boiler, auxiliary boilers, and DG sets. SDO optimization. Four operating states. Objective: minimize PWC. Algorithm: GA. One-step solution of the problem.
- [61] Tang, R.; Li, X.; Lai, J.J.A.E. Applied Energy 2018. System: photovoltaic/battery/diesel/ cold-ironing hybrid energy system. Operation optimization: optimal power flow dispatching. Hourly load profile for 3 days. Objective: minimize total cost of electric energy. Algorithm: Adaptive Multicontext Cooperatively Coevolving Particle Swarm Optimization Algorithm (AM-CCPSO).
- [62] Tang, R.; Wu, Z.; Li, X. Energy 2018. System: photovoltaic/battery/diesel/cold-ironing hybrid energy system. Operation optimization: optimal power flow dispatching during cold ironing. Hourly load profile for 3 days. Objective: minimize total cost of electric energy. Method: optimal control and model predictive control (MPC) methods.
- [63] Trivyza, N.; Rentizelas, A.; Theotokatos, G. Energy Conversion and Management 2018. System: main and auxiliary engines of various types and fuels, boilers of various fuels, MCFCs, WHR, emission abatement technologies in an Aframax tanker. SD optimization. Operation by predetermined rules. Operation profiles for propulsion, electric, and thermal loads are considered in the form of frequency of occurrence. Four objectives: (i) minimize life cycle cost, (ii) minimize life cycle CO2 emissions, (ii) minimize life cycle SOx emissions, (ii) minimize life cycle NOx emissions. Decision variables: (a) type of fuel, type of engine, and nominal power of each engine; for both main and auxiliary engines; (b) type of boilers, number of boilers, and fuel type; (c) existence of energy efficiency technologies; and (d) existence of emission abatement technologies. Algorithm: Nondominated Sorting Genetic Algorithm NSGA-II.
- [64] Wang, K.; Yan, X.; Yuan, Y. Journal of Engineering for the Maritime Environment 2018. System: two dual fuel main engines with shaft generators and two DG sets in a bulk carrier. Design and operation optimization. Single-point optimization for four different power requirements. Three objectives: (i) fuel consumption, (ii) CO2 emissions, and (iii) power safety margin: (P-Q)/P, where P the available electric power and Q the electric load. Nominal power output of the main engines and the DG sets are design-independent variables, while the speed of the vessel, rotational speed of the main engines, and the load of the generators are operation-independent variables. Algorithm: PSO.
- [65] Wang, K.; Yan, X.; Yan, Y.; Jiang, X.; Lin, X.; Negenborn, R.R. Transportation Research-Part D 2018. Further development of previous work (No. 14). System: main engine in a cruise ship on the Yangtze river. Speed optimization only in real time. A whole route is divided into n legs with n time steps. The optimal speed is determined for each step. Changes in the environmental conditions at each step are taken into consideration. Constraint on the whole sailing time from port A to port B is considered. Objective: minimize EEOI. Method: model predictive control and dynamic optimization. Algorithm: PSO.
- [66] Yang, S.; Chagas, M.B.; Ordonez, J.C. Applied Thermal Engineering 2018. System: laboratory experimental facility of an integrated energy system (IES) cooling network of a notional all-electric ship. Graph theory was used to represent the network. The network model was formulated and solved in MATLAB. The equations were solved using the adaptive backward differentiation formula for transient cases and trust-region Newton method for steady-state solutions. First optimization: building the model of the system. Performance parameters were estimated using experimental data. Objective: maximize the Index of Agreement (IA) and modeling efficiency (ME) and minimize the root-mean-square error (RMSE). Algorithm: Mixed Integer Distributed Ant Colony Optimization solver (MIDACO). The design space towards global optimum is updated by the oracle penalty method. The case also was investigated wherein all three metrics were considered simultaneously by solving a multiobjective optimization problem with Utopia–Nadir Balance concept. Second optimization: integrative thermodynamic optimization: the system is considered as a whole. The total heat exchanger inventory allocation throughout the IES was optimized by minimizing the maximum temperature in the system. Parametric sweeps were conducted instead of using sophisticated optimization techniques to obtain clear physical insights into the evolution of system architecture towards the optimum with respect to the parameters of interest.
- [67] Al-Falahi, M.D.A.; Jayasinghe, S.D.G.; Enshaei, H. Energy 2019. System: two diesel-generator sets and one battery pack in all-electric short-haul ferry. Operation optimization. Four operating states during a round trip of 60–100 min. Objective function: minimization of the operating cost, as expressed by minimization of fuel consumption. Independent variables: power output of each DG set and charging power of the battery pack. Algorithm: hybrid of Grey Wolf Optimizer and Fuzzy Logic expert system. GWO determines the power output of each DG set, while FL determines the charging power of the battery pack. Iteration between GWO and FL is performed until convergence.
- [68] Bordin, C.; O. Mo, O. Journal of Energy Storage 2019. System: battery for electricity storage operating with diesel or gas generators in all-electric ship. Design and operation optimization in terms of battery choice, sizing, and energy flows management among the different energy units. A load profile is considered (hour, day, week, etc.) that is repeated throughout the typical year. Objective function: minimize total cost (investment + operation). Algorithm: Mixed Integer Linear Programming.
- [69] Dolatabadi, A.; Ebadi, R.; Mohammadi-Ivatloo, B. Journal of Operation and Automation in Power Engineering 2019. System: HESS—hybrid power system with energy storage (ESS), photovoltaics (PV), and diesel generator in oil tanker. The stochastic character of solar radiation is taken into consideration. Design optimization (sizing of the HESS). The load profile is approximated with five periods of steady-state operation. The operation is rule-based. Objective: minimize the annualized cost of the system that includes capital, fuel, maintenance, and emissions cost. Only the size of PV (kW) and battery (kWh) are determined. Algorithm: MINLP problem implemented in GAMS 23.6 software and solved using the SBB solver. GAMS: General Algebraic Modeling System.
- [70] Esmailian, E.; Gholami, H.; Røstvik, H.N.; Menhaj, M.B. Energy Conversion and Management 2019. System to be optimized: hull, water jet, and building integrated photovoltaic (BIPV) system including a battery of a planing craft with water jet propulsion operating in a river. Design optimization. Six optimization problems are formulated and solved. Objectives: (i) lifetime fuel consumption, (ii) overall propulsive efficiency, (iii) total ship resistance, (iv) GHG emissions across the entire operating range, and (v) total cost. Two of the five objectives are selected for each optimization problem in a biobjective optimization. Algorithm: Nondominated Sorting Genetic Algorithm (NSGA-II).
- [71] Jaurola, M.; Hedin, A.; Tikkanen, S.; Huhtala, K. Journal of Marine Science and Technology 2019. System: diesel-mechanical with shaft generator or hybrid electric power system including a battery, with the shaft generator being able to operate as motor in fishing boat. Operation optimization. Series of six steady-state operation modes. Objective: minimize fuel consumption. Independent variables: the power output of each component. Algorithms: combined use of NLopt and COBYLA.
- [72] Jaurola, M.; Hedin, A.; Tikkanen, S.; Huhtala, K. Journal of Marine Science and Technology 2019. Extension of the previous work that includes comparison among different options of propeller (FPP and CPP) and propulsion control (pitch, speed, or both).
- [73] Jianyun, Z.; Li, C.; Lijuan, X.; Bin, W. Energy 2019. System: plug-in hybrid electric propulsion systems (HEPSs), including the diesel engines, motors, battery modules, and gearboxes in tug ship. Design optimization: determine the size of major components of the system. Rule-based energy management. Two operating modes are considered: transit mode and loading mode. Biobjective: (i) minimize fuel consumption and (ii) minimize GHG emissions. Algorithm: NSGA-II.
- [74] Kim, D.; Hwang, C.; Gundersen, T.; Lim, Y. Energy 2019. System: alternative BOG reliquefaction systems in LNG carrier. The ship has high pressure gas injection engines for propulsion and duel fuel DG sets. Design optimization. Comparison of alternative configurations of the BOG reliquefaction systems. Objective: minimization of the total annual cost. Algorithm: PSO.
- [75] Koo, J.; Oh, S.-R.; Choi, Y.-U.; Jung, J.-H.; Park, K. Energies 2019. System: ORC exploiting the low temperature of the LNG (“cold” energy) for condensing the working fluid in LNG-powered ship with two alternatives: high-pressure DF engine and medium-pressure DF engine. Jacket cooling water is the heat source. Three alternative configurations are studied for each alternative (high-pressure DF engine and medium-pressure DF engine) with nine alternative working fluids. Design optimization. Objective: where the net power output of the ORC system and the total exergy supplied to the ORC system by the LNG streams. Algorithm: PSO.
- [76] Marques, C.H. Belchior, C.R.P.; Caprace, J.-D. Ocean Engineering 2019. Theory. System: slow speed diesel engines and propellers in LNG carrier. Constant weather conditions are considered. SDO optimization. Strength and cavitation of propeller blades is estimated along the way. Objective: maximize NPV. Algorithm: not clearly specified.
- [77] Marques, C.H. Belchior, C.R.P.; Caprace, J.-D. Ocean Engineering 2019. Application. System: slow speed diesel engines and propellers in LNG carrier. Constant weather conditions are considered. SDO optimization. Strength and cavitation of propeller blades is estimated along the way. Objective: maximize NPV. Algorithm: Differential Evolution Optimization Algorithm.
- [78] Ouyang, T.; Su, Z.; Huang, G.; Zhao, Z.; Wang, Z.; Chen, N.; Haozhong, H. Energy Conversion and Management 2019. System: dual loop ORC system, absorption refrigeration unit, Exhaust gas purification unit (desulfurization and denitrification) based on photocatalytic oxidation in bulk carrier. Design optimization. Objective: maximize the equivalent power output of the combined system (net power of the dual loop ORC + equivalent power of the absorption refrigeration (electric power that would be required by a compression refrigeration unit). Decision variables: mixture ratios of the high temperature and low temperature ORC, evaporation pressure, and condensation temperature of the high temperature ORC. Algorithm: GA. EEDI, EEOI, energy and exergy efficiencies, and reduction of SOx and NOx are calculated after optimization.
- [8] Sakalis, G.N.; Tzortzis, G.J.; Frangopoulos, C.A. Energies 2019. Static problem. System: superconfiguration including gas turbines, bottoming Rankine cycle, exhaust gas boiler, auxiliary boilers, and DG sets. SDO optimization. Four operating states. Objective: minimize PWC. Algorithm: GA. One-step solution of the problem. Dynamic problem. System: superconfiguration including gas turbines, 2- and 4-stroke diesel engines, bottoming Rankine cycle, auxiliary boiler, and DG sets. Dynamic SDO optimization. Initially continuous and then discretized load profile. Mixed Integer Dynamic Optimization Problem (MIDO). Objective: maximize NPV. Algorithm: Direct sequential method combined with NLP solver implanted in gPROMS software.
- [79] Trivyza, N.L.; Rentizelas, A.; Theotokatos, G. Energy 2019. System: DG sets, MCFCs, thermal boilers, WHR, and carbon capture technology in cruise ship with electric propulsion. Operation profiles for electric and thermal loads are considered. SD optimization. Load profiles are considered in the form of frequency of occurrence. Operation with predetermined rules. Two objectives: (i) minimize life cycle cost and (ii) minimize life cycle CO2 emissions. Four scenarios are examined regarding the policy on CO2. Algorithm: Nonsorting Genetic Algorithm NSGA-II.
- [23] Tzortzis, G.J.; Frangopoulos, C.A. Journal of Engineering for the Maritime Environment 2019. System: superconfiguration with 4-stroke diesel engines, DG sets, HRSG, steam turbine, and auxiliary boiler in LNG carrier. Dynamic SDO optimization. The speed of the ship and, consequently, the propulsion power, is under optimization. Electric and thermal loads are calculated as functions of the brake power of the main engines during the travel, while they are considered constant and known in port. Additionally, the weather conditions along the route are taken into consideration. A mixed integer-nonlinear programming problem is formulated. Initially continuous and then discretized load profile. Objective: minimize PWC. Solution method: direct sequential. Algorithm: CVP-SS software: gPROMS.
- [80] Yan, Y.; Zhang, H.; Long, Y.; Wang, Y.; Liang, Y.; Song, X.; Yu, J.J.Q. Journal of Cleaner Production 2019. System: superconfiguration including diesel engine with exhaust gas after treatment facilities, gas turbine, dual-fuel engine, EGBs, wind turbines and PV panels with battery, and compression chiller in cruise ship. SDO optimization. Objectives: (i) minimize total annual cost and (ii) minimize size (space occupied and weight). They are considered in separate or together (multiobjective optimization). Operation profile: operation state is divided into three types: (i) navigation, (ii) port stay and sea stay, and (iii) maneuvering, which accounts for 59%, 33%, and 8% of the operation period, respectively. Three typical days to represent winter, summer, and mid-season with a duration of 182, 62, and 121 days, respectively. Hourly profile of each load in each typical day. Algorithms: GUROBI 8.1.0 for the single-objective problem and augmented ε-constraint method for the biobjective problem.
- [81] Bolbot, V.; Trivyza, N.L.; Theotokatos, G.; Boulougouris, E.; Rentizelas, A.; Vassalos, D. Energy 2020. System: alternative systems with diesel or dual fuel engines and various fuels in cruise ship. Each combination is optimized in separate. Design optimization: the operating profile in the form of frequency of occurrence of loads is considered. Objectives: (i) life cycle cost and (ii) lifetime CO2 emissions. Independent variables for each alternative configuration: number of DG sets and nominal power output of each set. The blackout frequency and the unavailability of a generator (safety metrics) are estimated for the optimal plant. Algorithm: Nonsorting Genetic Algorithm II (NSGA-II).
- [82] Chen, H.; Zhang, Z.; Guan, C.; Gao, H. Energy 2020. System: hydrogen fuel cell stack with battery and supercapacitors for electric energy storage (HESS: hybrid energy storage system) in passenger boat. Design and operation optimization: sizing of the system and power allocation (power flow between the fuel cell and the storage system). Objective function: weighted summation of the battery degradation, power quality as expressed by voltage fluctuation, useful energy losses (in place of energy efficiency), and device cost. Algorithm: Whale Optimization Algorithm.
- [83] Feili, M; Ghaebi, H.; Parikhani, T.; Rostamzadeh, H. Thermal Science and Engineering Progress 2020. System: absorption power cycle (APC), i.e., Kalina cycle, and a humidification- dehumidification desalination (HDH) unit. APC uses heat from the cooling water and HDH heat from exhaust gases of the main engine. Multiobjective design optimization. Objectives: maximize energy efficiency, maximize exergy efficiency, and minimize unit cost of fresh water. Finally, one objective function is formulated as a properly weighted summation of the individual objectives. Algorithm: GA.
- [84] Feng, Y.; Du, Z.; Shreka, M.; Zhu, Y.; Zhou, S.; Zhang, W. Energy Conversion and Management 2020. System: supercritical CO2 Brayton cycle (SCBC) combined with Kalina cycle (KC) with heat from the exhaust gas of MAN 8S90ME-C10.2 low-speed two-stroke diesel engine. At load of the diesel engine lower than 85%, only the KC recovers energy from the exhaust gas. Design optimization at nominal point of the engine. Single objective: maximize net power output of each cycle in separate. Biobjective: (i) maximize efficiency and (ii) maximize Wnet/UA for each cycle converted in a single objective with the weighted sum function. Instead of formal optimization, each objective is drawn as a function of various parameters. Certain graphs reveal an optimal point.
- [85] Letafat, A.; Rafiei, M.; Sheikh, M.; Mosayeb Afshari-Igder, M.; Banaei, M.; Boudjadar, J.; Khooban, M.H. Journal of Energy Storage 2020. System: PEM fuel cells and battery in ferry boat. Cold-ironing at harbor. Optimization of sizing of fuel cells and battery and operation optimization (power from fuel cells, from cold-ironing, and from/to battery for a 24-h round trip). Propulsion and electric load for each hour of a 24-h round trip. Objective: total daily cost (capital and operation). Algorithm: Improved Sine Cosine Algorithm (ISCA), while Harmony Search is used to prevent SCA from being trapped in local optima.
- [86] Tian, Z.; Yue, Y.; Zhang, Y.; Gu, B.; Gao, W. Energies 2020. System: three ORC cycles combined. They use heat from the JCW and exhaust gas for evaporation and LNG to be burned in the main engine for condensation of the fluids. Design optimization at full load of the main engine. Only two independent variables: pressures of evaporator and condenser. Various fluids are investigated. Two objectives: (i) maximize energy and exergy efficiencies and (ii) minimize (system investment/payback period). The standard Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method attempts to choose alternatives that have the shortest distance from the ideal solution. Algorithm: NSGA-II.
- [87] Trinklein, E.H.; Parker, G.; McCoy, T. Energy 2020. System: gas turbine-generator, power electronics, cooling circuit, and thermal and electric energy storage. Operation optimization: optimal control. Objective: minimize exergy destruction of the whole system, excluding the gas turbine-generator at each control update. Method: model predictive control. The optimization executes the ship simulation within a call to the nonlinear optimization algorithm fmincon. A multiphysics-based model is used: the electric power system is modeled along with the cooling system and interactions between the two shipboard systems are considered.
- [88] Wu, P.; Bucknall, R. International Journal of Hydrogen Energy, 2020. System: fuel cell and battery propulsion system in coastal ferry. Voyage duration: 1 h. Shore connection is available in all ports. Design and operation optimization. Electric power profile for 3500 s. Solution: inner layer solves the optimal power split problem for given sizing of components. The external layer optimizes the average voyage cost and GWP emissions concurrently. The decision variables of the external layer are a vector of the fuel cell and battery module sizing parameters. The trade-off between the two objectives needs to be determined manually based on the Pareto front. Biobjective: (i) minimize average voyage cost and (ii) minimize GWP emissions. Algorithms: Deterministic Dynamic Programming (DDP) for the inner layer and Nondominated Sorting Genetic Algorithm II for the external layer.
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No. | Reference | Year | System | Optimization | |||
---|---|---|---|---|---|---|---|
S | D | O | Dynamic | ||||
1 | [37] | 2008 | COGES | • | • | • | |
2 | [18] | 2008 | COGES | • | • | • | |
3 | [38] | 2013 | MCFC prereformer | • | |||
4 | [39] | 2013 | ORC | • | • | ||
5 | [40] | 2014 | Kalina—4 variations | • | |||
6 | [41] | 2015 | ORC | • | • | ||
7 | [19] | 2015 | ORC | • | • | ||
8 | [42] | 2015 | PV/diesel/battery | • | |||
9 | [43] | 2015 | ORC | • | |||
10 | [44] | 2015 | DG sets | • | • | • | |
11 | [45] | 2016 | Total | • | |||
12 | [46] | 2016 | PT + Rankine | • | |||
13 | [47] | 2016 | MCFC + steam cycle | • | |||
14 | [48] | 2016 | Main engine | • | • | ||
15 | [49] | 2016 | DG/PV/battery | • | • | ||
16 | [50] | 2016 | Transcritical ORC | • | |||
17 | [51] | 2017 | ORC | • | • | • | |
18 | [52] | 2017 | EGR + steam Rankine | • | |||
19 | [53] | 2017 | RO desalination + ORC | • | |||
20 | [54] | 2017 | ORC | • | • | • | |
21 | [55] | 2017 | Supercritical CO2 (GT) | • | |||
22 | [56] | 2017 | DG/PV/battery/supercapacitor | • | |||
23 | [57] | 2018 | Various | • | |||
24 | [58] | 2018 | Hybrid power | • | • | ||
25 | [59] | 2018 | Boil-off reliquefaction | • | |||
26 | [60] | 2018 | Thermoelectric generator | • | |||
27 | [7] | 2018 | Diesel total | • | • | • | |
28 | [61] | 2018 | PV/battery/DG/cold ironing | • | |||
29 | [62] | 2018 | PV/battery/DG/cold ironing | • | |||
30 | [63] | 2018 | Total with MCFC | • | • | ||
31 | [64] | 2018 | DEs and DG sets | • | • | ||
32 | [65] | 2018 | Main engine | • | • | ||
33 | [66] | 2018 | IES cooling network | • | |||
34 | [67] | 2019 | Hybrid | • | |||
35 | [68] | 2019 | Battery | • | • | ||
36 | [69] | 2019 | HESS | • | |||
37 | [70] | 2019 | Hull, WJ, PV, ESS | • | |||
38 | [71] | 2019 | Diesel or hybrid | • | |||
39 | [72] | 2019 | Diesel or hybrid | • | |||
40 | [73] | 2019 | DE + battery | • | |||
41 | [74] | 2019 | Reliquefaction systems | • | |||
42 | [75] | 2019 | ORC: JCW-LNG cold | • | |||
43 | [76] | 2019 | DF engines + propeller | • | • | • | |
44 | [77] | 2019 | DF engines + propeller | • | • | • | |
45 | [78] | 2019 | ORC, AR, DSOx, DNOx | • | |||
46 | [8] | 2019 | GT total, DE, and GT total | • | • | • | • |
47 | [79] | 2019 | Total with MCFC | • | • | ||
48 | [23] | 2019 | Total | • | • | • | • |
49 | [80] | 2019 | DE, GT, EES, Ren., etc. | • | • | • | |
50 | [81] | 2020 | Diesel or DF engines | • | |||
51 | [82] | 2020 | FC + electric energy storage | • | • | ||
52 | [83] | 2020 | APC (KC), HDH | • | |||
53 | [84] | 2020 | SCBC, KC | • | |||
54 | [85] | 2020 | FC/battery/cold ironing | • | • | ||
55 | [86] | 2020 | ORC: JW+EG-LNG cold | • | |||
56 | [87] | 2020 | Electronics, cooling, ESS | • | • | ||
57 | [88] | 2020 | FC/battery/shore connection | • | • |
Type of Optimization | Articles | |
---|---|---|
Number | Percentage | |
SDO | 10 | 17.55 |
SD | 3 | 5.26 |
DO | 9 | 15.78 |
D | 24 | 42.11 |
O | 11 | 19.30 |
Total | 57 | 100.00 |
Operating Conditions | Articles | |
---|---|---|
Number | Percentage | |
Design point | 20 | 35.09 |
Two–six operating states | 15 | 26.32 |
More than six operating states | 3 | 5.26 |
Hourly load profile for a certain period of time | 6 | 10.52 |
Frequency of occurrence of various discrete loads | 4 | 7.02 |
Speed optimization in real time | 2 | 3.51 |
Continuous load profile for a certain period of time | 7 | 12.28 |
Total: | 57 | 100.00 |
Objective Function | Articles |
---|---|
Thermodynamic objectives–single objective optimization | |
Total irreversibility (min) | 3 |
Energy efficiency (max) | 4, 12, 21, 23 |
Exergetic efficiency (max) | 13 |
Exergy destruction (min) | 56 |
Power output (max) | 26 |
Net power output (max) | 5, 9, 18, 20 |
Equivalent power output (max) | 45 |
Fuel consumption (min) | 6, 11, 14, 34, 38, 39 |
Work required for a process (min) | 25 |
Maximum temperature of the system (min) | 33 |
(max) | 42 |
Environmental objectives–single objective optimization | |
EEOI (min) | 32 |
Economic objectives–single objective optimization | |
Net present value (max) | 1, 7, 17, 43, 44, 46 |
Total cost of the system (min) | 2, 22, 35, 41, 54 |
Total cost of electric energy (min) | 28, 29 |
Investment cost + operation cost + NOx tax (min) | 10 |
Capital + fuel + maintenance + emissions cost | 36 |
Cost of fuel + cost of battery (min) | 15 |
Levelized energy cost (min) | 16 |
Present worth cost (min) | 27, 46, 48 |
Multiobjective optimization | |
(i) Total cost (min), (ii) CO2 emissions (min) | 8 |
(i) Exergetic efficiency (max), (ii) total unit product cost (min) | 19 |
(i) Fuel consumption (min), (ii) emissions (min), (iii) difference between initial and final state of charge of the batteries (min) | 24 |
Life cycle quantities: (i) cost (min), (ii) CO2 emissions (min), (iii) SOx emissions (min), (iv) NOx emissions (min) | 30 |
(i) Fuel consumption (min), (ii) CO2 emissions (min), (iii) power safety margin (max) | 31 |
(i) Fuel consumption (min), (ii) GHG emissions (min) | 40 |
Life cycle quantities: (i) cost (min), (ii) CO2 emissions (min) | 47, 50 |
(i) Total annual cost (min), (ii) Size of system (min) | 49 |
Weighted summation of (i) battery degradation, (ii) voltage fluctuation, (iii) energy losses (iv) device cost | 51 |
Weighted summation of (i) energy efficiency, (ii) exergetic efficiency, (iii) unit cost of fresh water | 52 |
Weighted summation of (i) energy efficiency, (ii) Wnet/UA | 53 |
(i) Energy and exergy efficiencies (max), (ii) system investment/payback period (min) | 55 |
(i) Average voyage cost (min), (ii) GWP emissions (min) | 57 |
Biobjective optimization: two of the following objective functions are selected for each one of six optimization problems: (i) lifetime fuel consumption, (ii) overall propulsive efficiency, (iii) total ship resistance, (iv) GHG emissions across the entire operating range, (v) total cost | 37 |
Algorithm | Articles |
---|---|
Mixed-integer linear programming algorithms | |
Branch and Bound | 10, 11 |
Mixed Integer Linear Programming | 35 |
Constrained nonlinear programming algorithms | |
Generalized Reduced Gradient in GRG2 Software | 7, 17 |
Sequential Quadratic Programming (SQP) | 3, 9, 11, 13, 18 |
Interval arithmetic implemented in INTLAB-Version 5.5 | 15 |
MINLP problem implemented in GAMS 23.6 software | 36 |
Combined use of NLopt and COBYLA | 38, 39 |
GUROBI 8.1.0 | 49 |
fmincon | 56 |
Dynamic optimization algorithms | |
Sequential direct method combined with SQP | 46, 48 |
Deterministic Dynamic Programming (DDP) | 57 |
Stochastic or evolutionary algorithms | |
Particle Swarm Optimization (PSO) | 1, 2, 22, 31, 32, 41, 42 |
Adaptive Multicontext Cooperatively Coevolving Particle Swarm Optimization algorithm (AM-CCPSO) | 28 |
Genetic Algorithm (GA) | 4, 5, 6, 7, 17, 18, 19, 23, 25, 27, 45, 46, 52 |
Struggle Genetic Algorithm (StrGA) | 1, 2 |
Nondominated Sorting Genetic Algorithm (NSGA-II) | 8, 30, 37, 40, 47, 50, 55, 57 |
Improved NSGA-II | 24 |
Differential evolution optimization algorithm | 44 |
Mixed Integer Distributed Ant Colony Optimization solver (MIDACO) | 33 |
Whale Optimization Algorithm | 51 |
Hybrid algorithms | |
Improved Sine Cosine Algorithm (ISCA), combined with Harmony Search | 54 |
Hybrid of Grey Wolf Optimizer and Fuzzy Logic Expert System | 34 |
Algorithms dedicated to multiobjective optimization | |
Multiobjective Particle Swarm Optimization (MOPSO) | 8 |
ε-constraint method | 49 |
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Frangopoulos, C.A. Developments, Trends, and Challenges in Optimization of Ship Energy Systems. Appl. Sci. 2020, 10, 4639. https://doi.org/10.3390/app10134639
Frangopoulos CA. Developments, Trends, and Challenges in Optimization of Ship Energy Systems. Applied Sciences. 2020; 10(13):4639. https://doi.org/10.3390/app10134639
Chicago/Turabian StyleFrangopoulos, Christos A. 2020. "Developments, Trends, and Challenges in Optimization of Ship Energy Systems" Applied Sciences 10, no. 13: 4639. https://doi.org/10.3390/app10134639
APA StyleFrangopoulos, C. A. (2020). Developments, Trends, and Challenges in Optimization of Ship Energy Systems. Applied Sciences, 10(13), 4639. https://doi.org/10.3390/app10134639