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Control of EV charging points for thermal and voltage management of LV networks

机译:电动汽车充电点的控制,用于低压网络的热和电压管理

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Summary form only given. High penetrations of domestic electric vehicles (EVs) in UK low voltage (LV) networks may result in significant technical problems. This paper proposes an implementable, centralized control algorithm, currently being trialed in 9 UK residential LV networks, that uses limited information to manage EV charging points to mitigate these technical problems. Two real UK LV networks are used to quantify the potential impacts of different EV penetration levels and to demonstrate the effectiveness of the control algorithm (using different control cycles) for simultaneous thermal and voltage management. Monte Carlo simulations (adopting 1-min resolution data) are undertaken to cater for domestic and EV demand uncertainties. Results for these LV networks show that problems may occur for EV penetrations higher than 20%. More importantly, they highlight that even for a 100% penetration and control cycles of up to 10 min, the control algorithm successfully mitigates problems on the examined LV networks. Crucially, to determine effects on the comfort of EV users, a metric is introduced and discussed. The results of different control settings are presented to analyze potential adaptations of the control strategy. Finally, a comparison with an optimization framework highlights that the proposed algorithm is as effective whilst using limited information.
机译:仅提供摘要表格。家用电动汽车(EV)在英国低压(LV)网络中的高渗透率可能会导致重大的技术问题。本文提出了一种可实施的集中控制算法,目前正在9个英国居民低压网络中进行试验,该算法使用有限的信息来管理EV充电点以缓解这些技术问题。两个真实的英国LV网络用于量化不同EV渗透水平的潜在影响,并演示控制算法(使用不同的控制周期)对同时进行热和电压管理的有效性。进行了蒙特卡洛模拟(采用1分钟分辨率的数据)来满足国内和电动汽车需求的不确定性。这些低压网络的结果表明,电动汽车普及率高于20%时可能会出现问题。更重要的是,他们强调,即使对于100%的渗透和长达10分钟的控制周期,控制算法也可以成功缓解所检查的LV网络上的问题。至关重要的是,为了确定对电动汽车用户舒适度的影响,引入并讨论了一种度量标准。提出了不同控制设置的结果,以分析控制策略的潜在适应性。最后,与优化框架的比较突出显示了所提出的算法在使用有限信息的情况下同样有效。

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