Probabilistic Load Flow methods with high integration of Renewable Energy Sources and Electric Vehicles - case study of Greece
Probabilistic Load Flow methods with high integration of Renewable Energy Sources and Electric Vehicles - case study of Greece
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DOI：
10.1109/ptc.2011.6019380
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
A. Anastasiadis;E. Voreadi;N. Hatziargyriou
中科院分区：
文献类型：
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作者：
A. Anastasiadis;E. Voreadi;N. Hatziargyriou
本文采用概率潮流（PLF）技术对希腊可再生能源（RES）和电动汽车（EV）高度集成的大型配电系统进行了研究。这些技术提供了分支功率流、节点电压和有功电力线损耗的累积密度函数（CDF），其中RES和EV积分具有大的变化。当电动汽车采用不同的充电策略（转储充电、双费率政策和混合充电）时，会导致节点注入的显著不确定性。为了解决这个问题，它是采用和增强PLF方法，以科普EV节点负荷。本文研究了两种计算RES产生的概率密度函数的方法：Gram - Charlier（G-C）和康沃尔语Fisher（C-F）。所有的PLF结果与蒙特卡罗（MC）方法进行了比较。
This paper exams the large distribution power system of Greece with high integration of Renewable Energy Resources (RES) and Electrical Vehicles (EV) using Probabilistic Load Flow (PLF) techniques. These techniques provide Cumulative Density Functions (CDF) of branch power flows, node voltages and active power line losses with large variations of RES and EV integration. When different charging strategies of EV (dump charging, dual tariff policy and mixed charging) take place then lead to significant uncertainty in node injections. In order to tackle this issue, it is adopted and enhanced PLF methods to cope with EV nodal loads. Two methods are investigated for the calculation of Probabilistic Density Function (PDF) of RES' production, Gram - Charlier (G-C) and Cornish Fisher (C-F). All the PLF results are compared with Monte Carlo (MC) method.