Economic Assessment for Battery Swapping Station Based Frequency Regulation Service
Economic Assessment for Battery Swapping Station Based Frequency Regulation Service
复制标题
DOI：
10.1109/tia.2020.2986186
复制
发表时间：
2020-09
影响因子：
4.4
通讯作者：
Xinan Wang;Jianhui Wang
中科院分区：
文献类型：
--
作者：
Xinan Wang;Jianhui Wang
电池交换站（BSS）由于其大的电池存储容量，在提供快速频率调节服务（FFRS）方面具有很大的潜力。然而，与常规BSS相比，提供FFRS的BSS面临以下财务风险：第一，用于支持车辆到电网服务的更高的基础设施投资;第二，由于FFRS导致的更高的电池老化成本;第三，FFRS导致电池充电成本的不确定性。在这种背景下，在本文中，我们提出了一个经济风险评估模型的BSS为基础的FFRS通过比较其经济与常规BSS。在这个模型中，每日收入的风险价值（VaR）和长期投资回报率（ROI）的定期BSS和提供FFRS的BSS进行了比较。评估结果通过三个步骤得到：首先，我们建立了基于BSS的FFRS的运营和经济模型。其次，建立了VAR和ROI分析的数学模型。最后，通过对大量情景的统计分析，比较了日收益的VaR。ROI比较由基于策略梯度的强化学习算法进行，该算法可以处理电动汽车访问带来的非凸性和随机动态。通过使用来自公用事业公司的真实的辅助市场数据和来自现场交通传感器的交通计数数据，证明了所提出的框架的实用性。
Battery swapping stations (BSSs) have great potential in providing fast frequency regulation service (FFRS) owing to their large battery storage capacity. However, compared to a regular BSS, a BSS providing FFRS faces the following financial risks: first, higher infrastructure investment to support the vehicle-to-grid services; second, higher battery aging costs due to FFRS; third, FFRS causes uncertainties to batteries’ charging costs. Under such a context, in this article, we propose an economic risk assessment model for the BSS-based FFRS by comparing its economics with a regular BSS. In this model, the value at risk (VaR) of daily revenue and the long-term return on investment (ROI) of a regular BSS and a BSS providing FFRS are compared. The assessment results are obtained in three steps: first, we develop the operation and economic models for the BSS-based FFRS. Next, the mathematical models of the VAR and ROI analyses are formulated. Finally, the VaR of daily revenue is compared through a statistical analysis of a large number of scenarios. The ROI comparison is conducted by a policy gradient based reinforcement learning algorithm, which can handle the nonconvexity and stochastic dynamics brought by the electric vehicle visits. The practicality of the proposed framework is demonstrated by using the real ancillary market data from utilities and the traffic count data from onsite traffic sensors.