Generator Contribution Based Congestion Management using Multiobjective Genetic Algorithm

Sawan Sen, Priyanka Roy, Abhijit Chakrabarti, Samarjit Sengupta


 Congestion management is one of the key functions of system operator in the restructured power industry during unexpected contingency. This paper proposes a method for generator contribution based congestion management using multiobjective genetic algorithm. In the algorithm, both real and reactive losses have been optimised using optimal power flow model and the contributions of the generators with those optimised losses are calculated. On second level, the congested lines are identified by the proposed overloading index (OI) during contingency and those lines are relieved with the new contribution of generators, which is the outcome of the developed algorithm. The planned method depicts the information related to congestion management to minimize the investment cost, without installing any external devices and to maximise the consumer welfare by avoiding any load curtailment without affecting the voltage profile of the system as well as the optimised total system loss. IEEE 30 bus system is used to demonstrate the effectiveness of the method.

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