Evaluation Method of the Spatial-Temporal Complementary Characteristics Between Power Source and Load Based on Power Big Data
Kuan Zheng, Xiaoqing Yan, Ya-Chun Li, Jie Yang · IOP Conference Series Earth and Environmental Science · 2019
Abstract 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.