具有时间约束的电梯节能调度算法

针对电梯节能问题, 提出电梯能耗损失计算方法, 构建具有时间约束的电梯节能调度模型, 应用粒子群算法(particle swarm optimization, PSO)分别对已知目标楼层和预测目标楼层两种情况的电梯节能调度问题进行建模和求解. 通过数值仿真分析, 从等待时间和能耗两方面比较了三种算法(最近服务原则(nearest car, NC)、已知目标楼层的粒子群算法和预测目标楼层的粒子群算法)的性能. 研究结果表明, 与NC算法相比, 在保证80%以上 乘客等待时间小于60s的情况下, 已知目标楼层的PSO算法可以实现系统节能18.2%; 预测目标楼层的PSO算法可以实现系统节 能9.6%. 随着等待时间约束的放宽, PSO算法可获得的节能比例显著增加. 目标楼层的准确性对节能调度具有重要影响, 已知目 标楼层的PSO算法会比预测目标楼层的PSO算法约多节能10%.

Energy saving of elevator group control system with waiting time restriction

An energy saving problem of elevator system is studied with the restriction of each passenger's waiting time. In order to simplify the optimal problem, the energy consumption model is modified according to energy loss theory. And the energy saving elevator group control algorithm is constructed based on particle swarm optimization algorithm, which is applicable to both known and predicted target floor situation. In addition, a numerical simulation is implemented and, by which, effectiveness of the new algorithm is verified under different waiting time restriction and different passenger arrival rate. The simulation results demonstrate that, compared with the traditional nearest car strategy, the new algorithm can achieve energy conservation without service level decline significantly. Due to the drawback of the used prediction method, the new algorithm behaves better under known target floor situation than predicted target floor situation.