一种面向电动航空网络航班调度与能源管理的模型预测控制方案
A Model Predictive Control Scheme for Flight Scheduling and Energy Management of Electric Aviation Networks
AI总结：
针对电动航空网络的航班调度与能源管理问题，本文提出MPC方案，联合建模调度与能源管理问题并滚动求解，实验证明其可降低10%-37%电网能耗，保障网络高效运行。
摘要：
本文提出了一种用于电动航空网络航班调度与能源管理的模型预测控制（MPC）方案，该网络中电动飞机在配备可持续能源与电池储能的电气化机场间运送乘客，目标是最小化对电网的依赖。具体而言，我们首先将飞机航班与充电调度问题和机场能源管理问题联合建模，明确纳入当地天气预报因素。其次，我们将最小化电网能耗的运行问题构建为混合整数线性规划，并以滚动时域方式求解，其中每架飞机的航线分配与充电决策可动态调整，以缓解运行扰动。我们采用美国东北部传统航班网络的真实数据与天气条件对所提MPC方案进行验证。与无改道的基准方法相比，所提框架可降低10%至37%的电网能耗需求。结果表明，MPC可通过高效重新分配航班、调整飞机充电调度，有效保障网络运行，同时最大化就地能源系统的利用效率。
英文摘要：
This paper presents a Model Predictive Control (MPC) scheme for flight scheduling and energy management of electric aviation networks, where electric aircraft transport passengers between electrified airports equipped with sustainable energy sources and battery storage, with the goal of minimizing grid dependency. Specifically, we first model the aircraft flight and charge scheduling problem jointly with the airport energy management problem, explicitly accounting for local weather forecasts. Second, we frame the minimum-grid-energy operational problem as a mixed-integer linear program and solve it in a receding horizon fashion, where the route assignment and charging decisions of each aircraft can be dynamically reassigned to mitigate disruptions. We showcase the proposed MPC scheme on real-world data taken from a conventional flight network and weather conditions in the US American North East. The proposed framework saves between 10 and 37% of grid energy requirements when compared to a baseline without re-routing. Hence, results show that MPC can effectively guarantee operation of the network by efficiently re-assigning flights and rescheduling aircraft charging, while maximizing the efficiency of the on-site energy systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
