TY - CHAP
T1 - A Hybrid Multi-objective Evolutionary Approach for Power Grid Topology Design
AU - Bi, Xiaowen
AU - Tang, Wallace K.S.
PY - 2018
Y1 - 2018
N2 - Power grid is one of the critical infrastructures in human society. It is highly complex in both structure and dynamics. In order to study its performance, different models, such as Kuramoto oscillator network model, power flow model, cascading load model and so on, have been suggested. In this chapter, it is to demonstrate how an evolutionary algorithm can be applied to effectively solve the topological design problem in power grid based on the Kuramoto oscillator network model. Recognizing that multiple criteria are commonly confronted in practice, a multiobjective evolutionary algorithm is developed. Two objectives, namely the network synchronizability and the cost, are considered in this work. In addition, since the design problem is complex and nonlinear, a dedicated local searching mechanism is embedded to enhance the searching capability of the algorithm. Finally, the effectiveness of the proposed algorithm is confirmed by extensive numerical simulations.
AB - Power grid is one of the critical infrastructures in human society. It is highly complex in both structure and dynamics. In order to study its performance, different models, such as Kuramoto oscillator network model, power flow model, cascading load model and so on, have been suggested. In this chapter, it is to demonstrate how an evolutionary algorithm can be applied to effectively solve the topological design problem in power grid based on the Kuramoto oscillator network model. Recognizing that multiple criteria are commonly confronted in practice, a multiobjective evolutionary algorithm is developed. Two objectives, namely the network synchronizability and the cost, are considered in this work. In addition, since the design problem is complex and nonlinear, a dedicated local searching mechanism is embedded to enhance the searching capability of the algorithm. Finally, the effectiveness of the proposed algorithm is confirmed by extensive numerical simulations.
U2 - 10.1007/978-3-662-55663-4
DO - 10.1007/978-3-662-55663-4
M3 - 12_Chapter in an edited book (Author)
SN - 9783662556610
T3 - Emergence, Complexity and Computation
SP - 265
EP - 284
BT - Evolutionary Algorithms, Swarm Dynamics and Complex Networks
A2 - Zelinka, Ivan
A2 - Chen, Guanrong
PB - Springer-Verlag Berlin Heidelberg
ER -