论文标题

优化智能电网和电动公共交通巴士系统

Co-optimizing the Smart Grid and Electric Public Transit Bus System

论文作者

Yetkin, Mertcan, Augustino, Brandon R., Lamadrid, Alberto J., Snyder, Lawrence V.

论文摘要

随着气候变化为在智能城市进行投资的动力,通过电气化公共交通系统,我们考虑在城市地区的电动公共交通巴士,这些公共交通巴士在电力系统运营中发挥作用,除了它们为公共交通需求提供服务的典型功能。我们的模型考虑了一个社会规划师,因此,电力系统的运输管理局和电力系统的运营商将电力系统进行优化,以最大程度地降低电网的总运行成本,同时满足公交车上的额外运输约束。我们提供确定性和随机配方以合作化系统。每个随机配方都提供了不同的追索权,以管理可变的可再生能源不确定性:向上/向下升级/向下传统发电机,或对过境机队的充电/排放。我们证明了模型的功能以及通过协调策略获得的收益。我们比较了这些追索权的效力,以提供其他管理洞察力。我们分析了不同定价策略对合作的影响。当我们假设电动车队尺寸增长时,我们还将我们的合作方法与非合作策略进行了比较,将我们的合作方法与非合作策略进行了比较。鉴于最近朝着建造更智能的城市和电动运输系统的势头,我们的结果为可持续的未来提供了政策指导。我们使用修改后的MATPOWE CASE文件测试我们的模型,并使用不同尺寸的电源网络验证我们的结果。这项研究是由一个在加利福尼亚州拥有大型公交管理局的项目的动机。

As climate change provides impetus for investing in smart cities, with electrified public transit systems, we consider electric public transportation buses in an urban area, which play a role in the power system operations in addition to their typical function of serving public transit demand. Our model considers a social planner, such that the transit authority and the operator of the electricity system co-optimize the power system to minimize the total operational cost of the grid, while satisfying additional transportation constraints on buses. We provide deterministic and stochastic formulations to co-optimize the system. Each stochastic formulation provides a different set of recourse actions to manage the variable renewable energy uncertainty: ramping up/down the conventional generators, or charging/discharging of the transit fleet. We demonstrate the capabilities of the model and the benefit obtained via a coordinated strategy. We compare the efficacies of these recourse actions to provide additional managerial insights. We analyze the effect of different pricing strategies on the co-optimization. We also conduct congestion analysis in the power network, comparing our cooperative approach to a non-cooperative strategy when we assume electrified fleet sizes grow with greater battery capacities. Given the recent momentum towards building smarter cities and electrifying transit systems, our results provide policy directions towards a sustainable future. We test our models using modified MATPOWER case files and verify our results with different sized power networks. This study is motivated by a project with a large transit authority in California.

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