Overload minimization approach for Transmission Expansion Planning using Genetic Algorithm

Broadly two solution approaches are used in solving the Transmission Expansion Planning (TEP) problem namely, Load curtailment minimization approach and Overload minimization approach. Overload minimization approach involves lesser computational efforts but has problem of non convergence of loadflow in presence of isolated nodes. In literature, isolated nodes are dealt with modeling system Z-bus with fictitious lines of high impedance from reference to new nodes. This paper incorporates this technique in Genetic Algorithms (GA) to solve TEP problem. The fitness function is defined as inversely proportional to sum of weighted investment cost and overloads on the lines. The plans with high fitness and zero overloads are chosen, which are then tested for their utility by a Utilization Index (UI). The congestion costs for all chosen plans are also estimated. Multiple plans are analyzed across different attributes by trade off approach to help the decision maker. The complete algorithm implementation is described. The results for a practical Brazilian 87-bus test case are obtained and compared with results in literature.