Abstract This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the stochastic methodology behind this application. To achieve this, first, deterministic models are developed to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for disturbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed application estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed application is evaluated by using empirical charging data of public EVCS in Helsinki area.

中文翻译：

---

FCR 市场电动汽车充电站的最佳日前投标配置文件

摘要 本研究为电动汽车充电站 (EVCS) 开发了一个应用程序，用于估计频率控制储备 (FCR) 市场中的最佳日前投标配置文件，本文介绍了该应用程序背后的随机方法。为了实现这一点，首先，开发了确定性模型来计算电动汽车 (EV) 的每个充电事件（循环）可以提供的最大 FCR。这些模型是根据北欧柔性市场中 FCR 的技术要求建立的，即正常运行的频率遏制储备（FCR-N）和扰动的频率遏制储备（FCR-D）。在下一步中，这些确定性模型将结合 EVCS 中的充电记录的历史数据来计算 FCR 曲线的概率密度函数。最后，所提出的应用程序通过执行随机优化来估计最佳 FCR 配置文件，从而最大化 EVCS 参与日前灵活性市场的预期利润。通过使用赫尔辛基地区公共 EVCS 的经验充电数据来评估拟议应用程序的性能。