An Optimization of New Energy Hybrid Configuration Parameters Based on GA Method

Abstract

Configuration parameters of vehicular hybrid power systems (HPSs) are critical to their economy, weight, and fuel consumption. Many marine vehicles have parameters often set based on engineering experience when designing them, which often leads to excess power from power sources, increased costs, and increased emissions. In this paper, a multi-objective optimization model, which includes the economic cost, weight, and fuel consumption, is proposed to evaluate the performance of configuration parameters. To optimize the objective optimization model, this paper adopts a genetic algorithm (GA) method to iteratively calculate the globally optimal configuration parameter results. Finally, three sets of different weight coefficients are used to verify the configuration optimization results when considering different optimization objectives. To verify the advantage of the multi-objective optimization method, the three sets of optimized results are compared to a specific configuration parameter of a marine vehicle. From the simulation results, compared with the original configuration scheme, the total economic cost of Scheme 1 is reduced by 37.25 × 104 $, the total weight is reduced by 213.55 kg, and the total fuel consumption is reduced by 163.64 t; the total economic cost of Scheme 2 is reduced by 12.2 × 104 $, the total weight is increased by 393.36 kg, and the total fuel consumption is reduced by 271.89 t; the total economic cost of Scheme 3 is reduced by 36.89 × 104 $, the total weight is reduced by 209.2 kg, and the total fuel consumption is reduced by 162.35 t.

The Optimization of a Subsea Pipeline Installation Configuration Using a Genetic Algorithm

“…Ghigo et al [29] conducted platform optimization and a cost analysis in a floating offshore wind farm. Yin et al [30] optimized static and discrete berth allocation for large-scale marine-loading problems using an iterative variable grouping GA. Zhang et al [31] employed GA optimization to optimize configuration parameters of a new energy hybrid system. Sun Q. et al [32] performed optimization of the anchor chain design of a catenary anchor leg mooring system.…”