Abstract
Network or transmission congestion is one of the technical challenges in context of power system operation. The congestion management have great impact in power system operations with the emerging technologies of large renewable sources. In Pakistan, the congestion issues are addressed in the area of National Grid of Pakistan. There are many techniques are proposed to relieve these issues. In this research paper, FACTS and ESS devices are proposed to curtail this issue in the PSS/E environment. It gives the reliable & encouraging results and cost-effective solution.
Highlights
In economically stressed situation of the world, the cost-effective methodology of the system is required
The coastal area is used for the wind power generation and the desert is used for the solar power generation
We have focused on congestion management issue which has been addressed using Flexible AC Transmission System (FACTS) devices (Ghahremani and Kamwa, 2014; Mohanty et al, 2012)
Summary
In economically stressed situation of the world, the cost-effective methodology of the system is required. In this perspective of electrical industry, uninterruptable power supply to the consumers is today era need so, the new emerging techniques are developed which are economic as well as environment friendly For this purpose, the renewable energy sources are introduced in the conventional networks such as wind and solar technologies. Causes of Congestion Congestion in the network is mainly due to the following reasons; Limits of lines Voltage Profile (Khanabadi et al, 2013) Thermal Limits (Zafran et al, 2014) Overloading (Pinheiro and Castro, 2015) Demand Response (Li et al, 2015) Generation Integration (Villumsen et al, 2013) Remedies of Congestion The literature review revealed that many methodologies are adopted to meet this challenge. Some methodologies such as re-dispatching of the generating units by back down or increase others units, curtailment of the load by achieving the willing-to-pay-to-avoid curtailment factor (Fang and David, 1999), genetic algorithm for optimal power flow (Ghahremani and Kamwa, 2014), dynamic thermal rating methodology by considering the actual conditions of line (Zafran et al, 2014), demand response model using
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