TY - GEN
T1 - A differential evolution based method for power system planning
AU - Dong, Zhao Yang
AU - Lu, Miao
AU - Lu, Zhe
AU - Wong, Kit Po
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - Power system planning is a complex multi-objective optimization problem. It aims at locating the minimum cost of additional transmission lines that must be installed to satisfy the forecasted load in a power system. A number of different methods for power system planning have been investigated over the past decades. In this paper, a Differential Evolution (DE) based approach is proposed as an optimization tool to solve the power system planning problem. A comparison between Genetic Algorithms, Evolutionary Strategy (ES), and five different DE schemes are carried out on two benchmark power systems. The results shown that, as a relatively new heuristic optimization method, DE is able to provide robust and efficient solution to power system planning problems. © 2006 IEEE.
AB - Power system planning is a complex multi-objective optimization problem. It aims at locating the minimum cost of additional transmission lines that must be installed to satisfy the forecasted load in a power system. A number of different methods for power system planning have been investigated over the past decades. In this paper, a Differential Evolution (DE) based approach is proposed as an optimization tool to solve the power system planning problem. A comparison between Genetic Algorithms, Evolutionary Strategy (ES), and five different DE schemes are carried out on two benchmark power systems. The results shown that, as a relatively new heuristic optimization method, DE is able to provide robust and efficient solution to power system planning problems. © 2006 IEEE.
UR - https://www.scopus.com/pages/publications/34547256125
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34547256125&origin=recordpage
U2 - 10.1109/cec.2006.1688646
DO - 10.1109/cec.2006.1688646
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780394879
SN - 9780780394872
T3 - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
SP - 2699
EP - 2706
BT - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
T2 - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
Y2 - 16 July 2006 through 21 July 2006
ER -