Multi Objective Genetic Algorithm for Congestion Management in Deregulated Power System Using Generator Rescheduling and Facts Devices

The problem of congestion management is more pronounced in deregulated environment as the participants of the energy market are market oriented rather than socially responsible-as exhibited by the government operated bundled system. Customers would like to purchase the electricity from the cheapest available sources. The seller in energy market would like to derive more benefit out of their investments, engages with contracts that may lead to overloading of the transmission elements of the power system. An Independent System Operator (ISO) who has no vested interest in the energy market, coordinates the trades and make sure that the interconnected power system always operates in a secure state at a minimum cost by meeting the all the load requirements and losses. In this proposed study, Congestion is mitigated by Generator Rescheduling and implementation of FACTS devices. Minimization of rescheduling costs of the generator and minimization of the cost of deploying FACTS devices are taken as the objectives of the given multi-objective optimization problem. Non-dominated sorting genetic algorithm II is used to solve this problem by implementing the series FACTS device namely TCSC and shunt FACTS device namely SVC. The proposed algorithm is tested on IEEE 30 bus system.