This work proposes a method based on a mixed integer nonlinear non-convex programming model to solve the multistage transmission expansion planning (TEP). A meta-heuristic algorithm by the means of differential evolution algorithm (DEA) is employed as an optimization tool. An AC load flow model is used in solving the multistage TEP problem, where accurate and realistic results can be obtained. Furthermore, the work considers the constraints checking and system violation such as real and power generation limits, possible number of lines added, thermal limits and bus voltage limits. The proposed technique is tested on well known and realistic test systems such as the IEEE 24 bus-system and the Colombian 93-bus system. The method has shown hig...
With an increasing demand for electric power, new transmission lines should be constructed with a ra...
The electric transmission expansion problem involves the timely addition of transmission system comp...
Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact...
This work proposes a method based on a mixed integer nonlinear non-convex programming model to solve...
Solving the Transmission Expansion Planning (TEP) problem using the Alternating Current (AC) network...
Transmission expansion planning has become a complicated procedure more than it was. The rapid growt...
Abstract Restructuring and deregulation have exposed the transmission planner to new objectives and ...
Transmission expansion planning (TEP) is a non-convex optimization problem that can be solved via di...
This paper shows a methodology for solving the Transmission Expansion Planning (TEP) problem when Mu...
This work presents a branch-and-bound algorithm to solve the multi-stage transmission expansion plan...
The complexity of current and future electricity networks demands the use of more accurate models to...
The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission...
A modified formulation of the transmission expansion planning (TEP) problem is proposed by revising ...
The transmission expansion planning problem in modern power systems is a large-scale, mixed-integer,...
This paper proposes a method for solving a large-scale multistage transmission expansion planning pr...
With an increasing demand for electric power, new transmission lines should be constructed with a ra...
The electric transmission expansion problem involves the timely addition of transmission system comp...
Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact...
This work proposes a method based on a mixed integer nonlinear non-convex programming model to solve...
Solving the Transmission Expansion Planning (TEP) problem using the Alternating Current (AC) network...
Transmission expansion planning has become a complicated procedure more than it was. The rapid growt...
Abstract Restructuring and deregulation have exposed the transmission planner to new objectives and ...
Transmission expansion planning (TEP) is a non-convex optimization problem that can be solved via di...
This paper shows a methodology for solving the Transmission Expansion Planning (TEP) problem when Mu...
This work presents a branch-and-bound algorithm to solve the multi-stage transmission expansion plan...
The complexity of current and future electricity networks demands the use of more accurate models to...
The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission...
A modified formulation of the transmission expansion planning (TEP) problem is proposed by revising ...
The transmission expansion planning problem in modern power systems is a large-scale, mixed-integer,...
This paper proposes a method for solving a large-scale multistage transmission expansion planning pr...
With an increasing demand for electric power, new transmission lines should be constructed with a ra...
The electric transmission expansion problem involves the timely addition of transmission system comp...
Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact...