An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

Authors: RAJAMANICKAM MANICKARAJ SASIRAJA, VELU SURESH KUMAR, SANKARALINGAM PONMANI

Abstract: After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments. However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

Keywords: Transmission congestion, real power flow performance index, line flow sensitivity index, distributed generation, genetic algorithm

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