基于量子遗传算法的冷藏集装箱功率平衡调度方法

现有的集装箱船对各冷藏集装箱的控制相互独立，且单个冷藏集装箱的电力需求是随机的，造成总电力需求峰谷差较大，进而影响船舶电站的功率配置.为解决上述问题，需在保证温度安全的前提下对冷藏集装箱集群进行统一调度，本文提出一种基于量子遗传算法的功率平衡调度方法寻找冷藏集装箱集群的最优调度策略.首先，对冷藏集装箱优化调度问题建立数学模型，确定其约束条件及优化目标；然后，分别采用遗传算法（GA）及量子遗传算法（QGA）对优化目标求解，并比较经两类算法调度前后的冷藏集装箱实际功率变化情况及各项指标，评价两类算法的优化调度能力.实验结果表明：GA及QGA均能实现冷藏集装箱的优化调度，减小总电力需求的峰谷差，使负载功率趋于平衡，但QGA的寻优速度比GA快，平衡电力需求的能力及优化电站配置能力更强.

Scheduling Algorithm for Power Balancing in Refrigerated Containers Based on Quantum Genetic Algorithm

The current reefer container ship controls the refrigerated containers individually, such mechanism lacks of unified dispatch management of the refrigerators. The power demand of single refrigerator is random, resulting in large peak-valley difference of the total electric power demand, which further affects the power allocation and efficiency of ship power station. In order to solve the above problems, a reasonable dispatch of the refrigerated containers should be carried out with the prerequisite of ensuring the safety of temperature. This study proposes a scheduling algorithm based on quantum genetic algorithm for power balancing to find the optimal scheduling strategy for refrigerated containers. Firstly, this study establishes a mathematical model for the optimal scheduling of refrigerated containers, determining the optimization targets and constraint conditions. Secondly, it uses the Genetic Algorithm (GA) and Quantum GA (QGA) to solve the objective function, followed the comparison of their actual power changes before and after the scheduling and evaluation of the optimal scheduling capability of the two algorithms. The experimental results show that both QGA and GA can realize the optimal scheduling of refrigerated containers and reduce the peak-valley difference of total power demand, thus balance the power load. Nevertheless, QGA converges faster than GA, and its ability is stronger than that of QA in terms of balancing power demand and optimizing power station.