基于随机AC-OPF的电动汽车充电池风险感知灵活区间估算
Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF
AI总结：
针对配电网电动汽车充电池灵活性管理的不确定性问题，提出基于随机AC-OPF的方法，定义并计算含风险参数的充电池灵活区间，实现DSO与充电池多方获益。
摘要：
本文提出一种随机AC-OPF（SOPF，随机交流最优潮流）方法，用于不确定性环境下配电网中电动汽车（EV）充电池的灵活性管理。该SOPF将来自充电池的离散效用函数作为对其充电任务最终未供能的补偿机制。本文描述了所提SOPF的一种应用场景：配电系统运营商（DSO）在日前时间框架内向各充电池需求灵活性，在保障技术约束的同时最小化灵活性成本。本文为每个充电池定义了灵活区间，并将其计算为涉及解的不确定性的风险参数的函数。结果表明，所有参与方均可从该方法中获益：DSO可获得风险感知的解决方案，而充电池/充电任务则因提供灵活性服务而实现总能源支出的降低。
英文摘要：
This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the proposed SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the solution's uncertainty. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.
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。
