Open AccessEvaluation Method of the Spatial-Temporal Complementary Characteristics Between Power Source and Load Based on Power Big Data
Author(s) -
Zheng Kang,
Xiaoqing Yan,
Yachun Li,
Jie Ye
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/371/5/052020
Subject(s) - randomness , complementarity (molecular biology) , renewable energy , computer science , wind power , volatility (finance) , electrical engineering , econometrics , engineering , mathematics , statistics , genetics , biology
With the rapid development of the intermittent renewable energy power generations such as wind power and solar power and the new random load such as EV, the double randomness become a great threat the safe and stable operation of power system. It has become a hot issue raised increasingly concern from the academic and engineering fields that how to reduce the randomness and volatility of the two sides of the system by utilizing the spatial-temporal complementary characteristics of different energy resources and loads. In this paper, the evaluation model of complementary characteristics between power and load across time and space based on the Power Big Data theory is proposed. By simulating the complementarity of the power supply and load between Northwest China and European load centres from three dimensions: load-load complementarity, source-source complementarity and source-load complementarity, it proves there are great complementary benefit by interconnecting the grids with different regions across continents, which provides a theoretical basis for the next implementation of the Global Energy Interconnection.