Title
A branch-and-cut method for computing load restoration plan considering transmission network and discrete load increment
Abstract
This paper proposes an optimization model to compute optimal load restoration plan for the transmission network. The objective of this model is to maximize the load pickup subject to power flow constraints and discrete load increment constraints. This mix-integer optimal power flow model (MIOPF) is solved via a branch-and-cut (B&C) framework. The nodes in the B&C tree are solved by the interior point method. In particular, three cutting planes, namely, the Gomory rounding cut, the knapsack cover cut, and the fixing variable cut, are incorporated into each node of the B&C tree to reduce the scale of the sub-tree. This model can be used to aid the system operators to conduct load restoration actions or draw up a restoration plan. The load restoration plan is obtained by solving a number of co-related MIOPF models till all load increments are restored. The RTS 24-bus test case with 170 load increments (i.e., with 170 binary variables) is used to illustrate complexity of the proposed model and the computational efficiency improvement stemming from the cutting planes.
Year
DOI
Venue
2014
10.1109/PSCC.2014.7038386
PSCC
Keywords
Field
DocType
integer programming,load flow,power transmission planning,tree searching,b&c tree framework,miopf model,rts 24-bus test,branch-and-cut method,discrete load increment constraint,interior point method,load pickup,mix-integer optimal power flow model,optimal load restoration plan computing,power flow constraint,transmission network,branch-and-cut,cutting plane,load restoration,mix-integer nonlinear programing,optimal power flow,computational modeling,generators,mathematical model,linear programming
Cutting-plane method,Mathematical optimization,Computer science,Branch and cut,Rounding,Linear programming,Knapsack problem,Pickup,Interior point method,Binary number
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
zhijun qin111.36
Yunhe Hou211422.07
shanshan liu300.34
jie yan401.01
dahu li500.34