# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## Appendix A Main Characteristics of Journal Articles on Optimization of Ship Energy Systems
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. * [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] _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 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'. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] _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. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _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 EEOL Method: model predictive control and dynamic optimization. Algorithm: PSO. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] _Applied Thermal Engineering_**2018**. System: laboratory experimental facility of an integrated energy system (IES) cooling network of a national 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. * [PERSON] et al.