Spatial Load Forecasting of Distribution Network Based on Artificial Intelligent Algorithm

被引：0
|
作者：
Shao Yuying [1 ]
Peng Peng [1 ]
Liu Weiyang [2 ]
Wei Lingyan [2 ]
Wang Bing [2 ]
机构：
[1] State Grid Shanghai Elect Power Co, Shanghai 200437, Peoples R China
[2] Nanjing Kuanta Informat Technol Co Ltd, Nanjing 211100, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
关键词：
load forecasting; fuzzy comprehensive evaluation; rooftop photovoltaic; electric vehicle; spatial and temporal distribution; urban distribution network planning;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
For the influence of the popularity of rooftop photovoltaic (PV) and the use of large-scale electric vehicles (EVs) on the power grid load, a space load forecasting method of urban distribution power grid was proposed, which took into accounts the spatial and temporal distribution of PV and EVs. Through analytic hierarchy process and fuzzy comprehensive evaluation method, various factors affecting the rooftop distributed PV output power were fully considered, and the rooftop PV output power of each planning area was forecasted by combining the least squares support vector machine and particle swarm optimization. Based on the time-space transfer probability matrix of EVs in different planning areas, Monte Carlo algorithm was used to simulate the time-space distribution of charging load with high probability. Taking an urban area as an example, the forecasted rooftop PV output power, EV charging load and traditional power load are superimposed on different planning areas to obtain the forecasted spatial load values.
引用
收藏
页码：5483 / 5488
页数：6
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