675 words - 3 pages

Congestion in the transmission network in a power system may occur due to lack of coordination between generation and transmission utilities or as a result of unexpected contingencies such as line outages, sudden increase of load demand, or failure of equipments. The power dispatch is a nonlinear programming problem. It is classified into two parts namely real and reactive power dispatch problems. The reactive power dispatch helps to minimize the real power loss in a transmission network. The real power dispatch is the most widely used control for network overload alleviation because of ease of control and require no additional reserves.

Optimal power flow (OPF) is an important tool for power system management. The aim of OPF problem is to optimize one or more objectives by adjusting the power system control variables while satisfying a set of physical and operating constraints such as generation and load balance, bus voltage limits, power flow equations and active and reactive power limits. A variety of optimization techniques have been applied to solve the OPF problem such as gradient method [1], linear programming method [2] and interior point method. In conventional optimization methods, identification of global minimum is not possible. To overcome the difficulty, evolutionary algorithms like genetic Algorithm [3], particle swarm optimization [4], differential evolution [5], gravitational search algorithm [6], harmony search method [7] and artificial bee colony optimization [8] have been proposed.

In [9], the authors’ proposed a fuzzy logic based approach to alleviate the network overloads by generation rescheduling. The generation shift sensitivity factor (GSSF) was used to decide the changes in generation. The approach removes the overloaded lines in the considered test cases but could not remove the overload completely. In [10], the authors’ proposed a static security enhancement through optimal utilization of thyristor-controlled series capacitors (TCSC). The ranking the system branches was based on determination of single contingency sensitivity (SCS) index which helps to decide on the best locations for the TCSCs. The objective of the optimization problem was to eliminate or minimize line overloads as well as the unwanted loop flows...

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