Predicting the technical impacts of high levels of small-scale embedded generators on low-voltage networks
Predicting the technical impacts of high levels of small-scale embedded generators on low-voltage networks
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预测高水平小型嵌入式发电机对低压网络的技术影响
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
R. Hair
中科院分区：
文献类型：
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作者：
P. Trichakis;P. Taylor;P. Lyons;R. Hair
预计小型嵌入式发电机（SSEG）在公共低压（LV）配电网上的高渗透率可能会给配电网运营商（DNO）带来许多与电能质量、配电系统效率和潜在设备过载相关的技术影响。需要使用合适的案例研究网络进行影响研究，以评估SSEG对低压配电网的影响，并量化允许的SSEG渗透水平。其目的是提出一种方法来预测SSEG对LV网络的技术影响，而不需要开发一个详细的基于计算机的电力系统模型和模拟一系列的操作场景。该方法是从对决定低压电网对SSEG的响应的关键电气特性的分析中得出的，重点关注以下技术方面：（i）电压调节，（ii）电压升高，（iii）电压不平衡，（iv）电缆和Transformer热极限以及（v）网络损耗。英国通用和欧洲通用LV网络和模拟结果进行了分析，这两个网络的介绍和讨论。所提出的方法，然后应用到现有的公共英国低压网络运营的E.ON英国中央网络，表明预测和仿真结果之间的良好协议。
The anticipated high penetrations of small-scale embedded generators (SSEGs) on public low-voltage (LV) distribution networks are likely to present distribution network operators (DNOs) with a number of technical impacts relating to power quality, distribution system efficiency and potential equipment overloads. Impact studies need to be performed using suitable case study networks in order to evaluate the effects of SSEGs on LV distribution networks and quantify allowable SSEG penetration levels. The aim is to propose a methodology for predicting the technical impacts of SSEGs on LV networks without the need for developing a detailed computer-based model of the power system and simulating a range of operating scenarios. This methodology is drawn from an analysis of the key electrical characteristics that determine the response of LV networks to the addition of SSEGs, focusing on the following technical aspects: (i) voltage regulation, (ii) voltage rise, (iii) voltage unbalance, (iv) cable and transformer thermal limits and (v) network losses. The analysis is carried out on a UK generic and a European generic LV network and simulation results for both networks are presented and discussed. The proposed methodology is then applied to an existing public UK LV network operated by E.ON UK Central Networks, indicating a good agreement between predicted and simulation results.