Bi-objective optimization for a plug-in PV/battery system of an unmanned patrol boat

Abstract

Plug-in photovoltaics (PV)/battery power systems attract increasing interest due to their merits in zero fuel consumption and zero local emission. However, current optimization for the plug-in PV/battery power system only considers a single objective of minimizing lifecycle cost, which may result in increased lifecycle greenhouse gas (GHG) emission due to electricity generation from coal in some areas. The paper therefore proposes a bi-objective optimization methodology to find out an optimal trade-off between lifecycle cost and GHG emission. Non-dominated sorting genetic algorithm II is developed to explore the Pareto solution sets. An unmanned patrol boat is considered as a study case. Simulation results show that the optimal design from the bi-objective optimization gains a 12.6% reduction in lifecycle cost and a 53.8% reduction in GHG emission when compared with the conventional power system consisting of diesel engines and generators. Moreover, the optimal design achieves a 46.3% reduction in GHG emission compared with the single-objective one aimed at minimum lifecycle cost, which at the same time increases the lifecycle cost by just 0.6%. Besides, two variables (the number of PV array and the number of battery modules) are found to be the most sensitive to the contradiction between lifecycle cost and GHG emission.

Research on intelligent patrol system of photovoltaic power station based on lidar

“…In this environment, the patrol system of photovoltaic power station is required to be optimized. In the past, the patrol inspection of photovoltaic power station mostly used artificial ground inspection or helicopter manned patrol inspection, the two forms of patrol inspection work is intensive, and the working environment is difficult, resulting in low labor efficiency [4]. It is impossible to effectively and accurately troubleshoot the hidden dangers and faults of transmission lines [5].…”