动力能源差异下混合公交车队调度方案生成方法

Generation Method of Mixed Bus Fleet Scheduling Scheme Under Power and Energy Difference

摘要:

为了实现电动公交与燃油公交混合车队运营调度优化，提出了一种平衡电动公交与燃油公交环境成本和经济成本的混合公交车队调度方法。首先结合不同动力能源公交车辆的运行特性给定约束条件，将碳排放和分时电价融入电动公交和燃油公交运营能耗及成本分析中；然后考虑混合公交运营系统的经济成本与环境成本，建立综合运营成本最小化模型；最后提出基于嵌套禁忌搜索的改进遗传算法对所建模型进行求解，该算法不仅弥补了传统遗传算法收敛过早、易陷入局部最优解的缺陷，而且提高了模型求解的质量与精度。以云南省文山州1路公交运营数据为例对本研究所提出的模型和算法进行验证。结果表明，基于嵌套禁忌搜索的改进遗传算法相较于传统遗传算法求解精度提高了12%左右，并且生成的调度方案能够在减少碳排放和提高车辆利用率中取得最佳平衡，有效降低了综合运营成本，实现了电动公交与电价错峰执行任务的效果，为平稳实现公交车队“电动化”提供了绿色高效可靠的调度方法。

Abstract:

In order to optimize the operation of the mixed bus fleet， a scheduling method that balances the environmental and economic costs of electric buses and fuel buses is proposed. Firstly， the constraints are given by combining the operation characteristics of buses with different power energy， and the carbon emissions and time-of-use electricity prices are integrated into the energy consumption and cost analysis of electric buses and fuel buses， then， considering the economic cost and environmental cost of the public transport system， a comprehensive operation cost minimization model is established， finally， an improved genetic algorithm with nested tabu search is used to solve the model. The algorithm not only compensates for the shortcomings of traditional genetic algorithms that converge prematurely and fall into local optimal solutions easily but also improves the quality and accuracy of the model solution. Bus No. 1 in Wenshan City， Yunnan Province， was taken as an example for verification. The results show that the improved genetic algorithm with nested tabu search improves the solution accuracy by about 12% compared with the traditional genetic algorithm， and the scheduling scheme generated achieves the best balance between reducing carbon emission costs and improving vehicle utilization and effectively reduces the overall operation cost. In addition， the scheduling scheme realizes the effect of electric bus task execution period and electricity price peak staggering and saves the charging cost， which provides a green， efficient， and reliable scheduling method for the smooth realization of the “electrification” of the bus fleet.