# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## Appendix A Main Characteristics of Journal Articles on Optimization of Ship Energy Systems
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. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _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.
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _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. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Energy_**2018**. System: small-scale boil-off gas relatigefaction plant on an LNG-fueled ship. Design optimization at nominal conditions. Objective: minimization of the work required per unit mass of BOG relatigefied. Algorithm and software: GA in MATLAB. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _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. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] _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. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]. _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). * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] _Energy_**2018**.