Open AccessThe optimized allocation of emergency resources for power system restoration
Author(s) -
Haifeng Liang,
Y. Li,
X. M. Wang,
Y. P. Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1449/1/012018
Subject(s) - blackout , node (physics) , computer science , particle swarm optimization , electric power system , process (computing) , reliability engineering , transmission (telecommunications) , power (physics) , operations research , mathematical optimization , engineering , telecommunications , mathematics , physics , structural engineering , quantum mechanics , machine learning , operating system
In recent years, there have been frequent blackout accidents in foreign countries. For power system, the configuration of emergency resources affects the process of the power system restoration. It is the key to ensure the smooth restoration of power system that the emergency generating vehicle and the operations staff arrive at the substation in time after the blackout. In this paper, the optimal allocation scheme of the emergency generating vehicle considering the comprehensive importance of load nodes and traffic network constraints is proposed. The comprehensive importance degree of nodes is calculated by considering the spectral importance degree, the importance degree of load amount and the comprehensive importance degree of load of nodes. The blackout economic loss of node is evaluated by node’s comprehensive importance degree. In addition, the cost of configuration and maintenance of the emergency generating vehicle and the transmission cost under the constraint of traffic network are considered to establish the optimal configuration model of the emergency generating vehicle. Finally, the Discrete Binary Particle Swarm Optimization (DBPSO) is used to obtain the optimal configuration location of emergency generating vehicle and the optimal route for recovery. The validity and feasibility of the proposed method are verified by the results of IEEE39.