协同优化智能电网与电动公共交通巴士系统
Co-optimizing the Smart Grid and Electric Public Transit Bus System
AI总结：
该研究针对电动公交巴士除常规功能外参与电力系统运行的场景，构建确定性与随机协同优化模型，对比不同补救措施功效，验证协同策略效益，为可持续智慧城市建设提供政策方向。
摘要：
随着气候变化推动对智慧城市的投资，结合电动公共交通系统，我们研究城市区域内的电动公共交通巴士，这类巴士除了满足公共交通需求的常规功能外，还在电力系统运行中发挥作用。我们的模型以社会规划者为核心，使交通管理部门与电力系统运营方协同优化电力系统，以最小化电网总运营成本，同时满足巴士的额外交通约束。我们提供确定性和随机协同优化系统的公式，每种随机公式针对可变可再生能源不确定性提供不同的 recourse actions（补救措施）：传统发电机的升/降负荷，或公交车队的充放电。我们验证了模型的能力及协同策略带来的效益，对比了这些补救措施的功效以提供管理见解，分析了不同定价策略对协同优化的影响，还在电力网络中开展拥堵分析，对比当电动车队规模随电池容量提升而增长时，我们的协同方法与非协同策略的效果。鉴于近期建设更智能城市和电气化交通系统的势头，我们的结果为可持续未来提供政策方向。我们使用修改后的MATPOWER案例文件测试模型，并在不同规模的电力网络中验证结果。本研究由与加利福尼亚某大型交通管理部门的项目驱动。
英文摘要：
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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