Open AccessStudy on the evaluation model of new energy real-time consumption capacity considering dynamic constraints of network sources
Author(s) -
Bojun Kong,
Huichao Wang,
Yan Chen,
Jiajia Wu,
Le Wang,
Y. Su Y. Fan
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/514/4/042053
Subject(s) - ac power , dynamic demand , computer science , energy consumption , node (physics) , transformer , real time computing , voltage , engineering , power (physics) , electrical engineering , physics , structural engineering , quantum mechanics
In order to accurately evaluate the real-time consumption capacity of new energy considering the dynamic coordination of network sources in real-time scale, a new energy real-time consumption capacity evaluation model considering the dynamic constraints of network sources is established. Firstly, according to the operation plan of thermal power unit, the transmission plan of tie line, the regulation margin of transformer ratio and the capacity of each node’s reactive device, the dynamic constraint of network source is set. Then, according to the grid parameters, the real-time prediction of new energy station operation and the real-time prediction of each node load demand, the line power flow security, node power balance, node voltage security and new energy station operation constraints are set. Finally, two optimization objectives of new energy active power maximum and minimum consumption are set up, and a new energy real-time consumption capacity evaluation model considering the dynamic constraints of network sources is established, which takes the active and reactive power of thermal power unit, the active and reactive power of tie line, the transformer transformation ratio, the reactive capacity of reactive device switching, and the active and reactive power of new energy as decision variables. The validity of the model is verified by 4-node system.