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| 引用本文: | 朱鹰屏,韩新莹,刘世立,王轶群.基于改进双中心粒子群算法的电动公交车运营数量优化策略研究[J].电力系统保护与控制,2017,45(8):126-131.[点击复制] |
| ZHU Yingping,HAN Xinying,LIU Shili,WANG Yiqun.Optimization of operation quantities of electric buses based on improved double center particle swarm optimization algorithm[J].Power System Protection and Control,2017,45(8):126-131[点击复制] |
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| 基于改进双中心粒子群算法的电动公交车运营数量优化策略研究 |
| 朱鹰屏,韩新莹,刘世立,王轶群 |
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| (广东技术师范学院自动化学院，广东 广州 510665;国家电网许继集团市场部，河南 许昌 461000;武汉理工大学国际教育学院,湖北 武汉 430070) |
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| 摘要: |
| 针对电动公交车在一定容量约束的馈线充电的情况，采用了一种改进的双中心粒子群算法对电动公交车充电进行优化调度，以获得最大的电动公交车运营数量。首先，建立公交车充电后馈线负荷曲线峰谷差最小的模型，设公交车运营初始数量，利用改进的双中心粒子群算法进行优化。然后，根据优化结果对比该馈线容量约束修改电动公交车运营数量，重新优化，逐步逼近并最终找到最优的运营数量。改进的双中心粒子群优化算法是在原算法的基础上，增加了5条粒子运动路线，扩大了搜索精度，抑制了粒子群的早熟。同时，为了提高寻优速度，粒子的初始化是根据日负荷曲线距离馈线约束容量的远近来确定。最后以南方某城市典型的馈电线路为例进行仿真计算，结果表明，该方法具有更优的调度效果。 |
| 关键词: 容量约束 电动公交车 双中心粒子群 初始化 优化 |
| DOI：10.7667/PSPC20170819 |
| 投稿时间：2016-04-14修订日期：2016-09-18 |
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| Optimization of operation quantities of electric buses based on improved double center particle swarm optimization algorithm |
| ZHU Yingping,HAN Xinying,LIU Shili,WANG Yiqun |
| (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China;Market Department of State Grid XuJi Group Corporation, Xuchang 461000, China;School of International Education, Wuhan University of Technology, Wuhan 430070, China) |
| Abstract: |
| For the case of electric buses charging under the constraint of feed line capacity, an improved Double Center Particle Swarm Optimization algorithm (DCPSO) which can optimize the charging is offered to get the maximum sum of electric buses operating. Firstly, a mathematical optimization model of the minimum value of difference between daily peak and valley load of feed line is established, the initial sum of electric buses operating is assumed and the improved DCPSO is applied to optimize the charging. Secondly, according to the optimization results and the capacity constraint of the feed line, the sum of electric buses on operating is modified to re-optimize the charging. The modified sum of electric buses on operating will make the load curve's peak approach the feed line capacity, which will find the maximum sum of electric buses on operating at last. The improved DCPSO makes particles increase 5 motion routes based on DCPSO, which improves search range and prevents particle from premature. In order to improve the optimization speed, the initialization of particles is determined according to the distance between the daily load curve and the feeder capacity constraint. An example of typical feed line of a southern city is applied to simulate in the end, the result indicates the scheduling is more optimal. |
| Key words: capacity constraint rechargeable bus DCPSO initialization optimization |
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