Mitigating Congestion in Restructured Power System using FACTS Allocation by Sensitivity Factors and Parameter Optimized by GWO

Abstract

In modern deregulated power industry, private sector has invested a lot to supply for extended power demand using the preexisting power system framework. This resulted into increased loading of transmission lines which has to work now to hit their thermal limits. The overloading of transmission line resulted in congestion and hence increase in loss of power in the system. One of the efficient ways to reduce congestion is by enhancing the available transfer capacity (ATC) of the power system. ATC enhancement can be achieved by application of FACTS devices. This paper presents an innovative method to mitigate congestion by locating TCSC in the IEEE 30 bus system. The allocation of TCSC is done by using ACPTDF sensitivity factors while the parameter setting is done by applying Grey Wolf Optimization (GWO) method. The effective application of GWO is demonstrated in this paper to reduce active power loss, enhancement of ATC value with reduction of reactive power loss and to optimize TCSC size through a multi objective function. The suitability of algorithm is established through concerned figures and tables.

Optimal Congestion Management with FACTS Devices for Optimal Power Dispatch in the Deregulated Electricity Market

“…The desired generation and load of all the groups Subsequently, the efficacy of the proposed approach is being tested for an IEEE 30bus system. 𝑅𝑒𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝐶𝑜𝑠𝑡 = ∑(𝐼 𝑘 𝑛 𝑔 𝑖=2 ∆𝑃 + + 𝐷 𝑘 ∆𝑃 − ) $/ℎ (36) where, 𝐼 𝑘 and 𝐷 𝑘 are the increment and decrement bid prices submitted by GENCOs.…”

“…Various optimization approaches, such as the Firefly algorithm [32], the adaptive particle swarm optimization algorithm [33], the satin bowerbird optimization algorithm [34], and particle swarm optimization [35], were utilized for managing congestion situations in different systems. To reduce transmission line overloading, congestion management has been carried out using gray-wolf optimization techniques [36].…”