Quantum Ant Colony Based Optimal Allocation Method of Multiple Energy Sources for Grid-Connected Shore Power of DC Network Ship

Abstract

The objective of this paper is to address critical challenges, including the complexity of multi-objective cooperative optimization, the suboptimal charging and discharging efficiency of energy storage systems, the inaccuracy of aligning dynamic load-demand characteristics, and the premature convergence of traditional meta-heuristic algorithms. The paper proposes an enhanced quantum ant colony optimization algorithm (QACA), leveraging a quantum computer system to construct a multi-objective optimization model for wind-solar-storage cooperative ship power systems. Firstly, a parallel computing framework based on quantum bit coding is established, and the pheromone updating mechanism is dynamically adjusted through a quantum revolving door. This effectively improves the global exploration ability of the algorithm in the complex solution space. Secondly, a coupling architecture is designed between the multi-scale dynamic load demand forecasting module and the renewable energy power dispatch strategy. The model is constructed based on the price-electricity total cost (PETC), carbon emission intensity (CEI), energy rate (ER), and system reliability (SR). The subsequent example illustrates the relationship between ship load demand and the comprehensive cost of electricity. The simulation and validation studies of the three modes (wind and sunlight, wind and no sunlight, and sunlight and no wind) are carried out in a Python environment. The experimental results demonstrate that, in comparison with the traditional classic ant colony optimization algorithm (CACA), QACA effectively circumvents the local optimal trap of the traditional method in the allocation of wind and light storage capacity through the path selection mechanism enhanced by the quantum tunneling effect. Furthermore, QACA enhances the utilization efficiency of renewable energy sources and reduces the system cost. The minimum cost in the three modes has been reduced by an average of 1.66%, and the convergence speed has been improved by an average of 23.24%.

Keywords

Quantum ant colony algorithm / New energy ship / Multi-energy systems / Optimizing configuration / Energy management

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