Congestion of transmission lines is a key challenge for the system operator in the open access regime and is usually managed by rescheduling generators and demands. The changes in schedule due to congestion management may cause stability margins to shrink to unacceptable levels. Stability issues must, therefore, be considered during congestion management. This article presents a multi-objective formulation of the congestion management problem in a pool-based electricity market with the competing objective functions of minimizing congestion management cost and maximizing voltage and transient stability margins. The non-dominated sorting genetic algorithm II is employed to achieve the best trade off among the three objectives. A fuzzy decision maker is used to extract the best-compromise non-dominated solution.The proposed method has been tested on the New England 39-bus system, and results are compared with those of other reported multi-objective mathematical programming based approaches. The proposed non-dominated sorting genetic algorithm II based congestion management algorithm outperforms the previously reported method. The quality of the results establishes the efficacy of the proposed approach in solving the multi-objective congestion management problem.

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In this paper, we formulate the power transmission congestion problem as a multiobjective optimization problem and solve it using the reference point NSGA‐II (R‐NSGA‐II) technique. Restructuring of the electric power industry has led to intensified use of the transmission grid, thereby causing more frequent power transmission congestion. Congestion threatens the power system security and reliability and is therefore a crucial issue in the unbundled power system scenario, which is usually managed by rescheduling the generators and the demand. Stability considerations should also be incorporated within the congestion management (CM) methodology so as to ensure secure stability margins post CM. In this paper, we formulate the CM problem in a pool model as a true multiobjective optimization problem with the three conflicting objectives of minimizing CM cost and maximizing the voltage stability margin and the transient stability margin. The multiobjective CM problem is solved using the R‐NSGA‐II method, which is a modification of the well‐known non‐dominated sorting genetic algorithm II (NSGA‐II) optimization algorithm. R‐NSGA‐II makes use of decision maker (DM)‐supplied preference information to guide the search for better solutions corresponding to the DM's preferences. The R‐NSGA‐II‐based CM approach is tested and verified on the IEEE‐39 bus system model, and its performance is compared with those of NSGA‐II and other reported method. The results obtained demonstrate that the proposed method is capable of obtaining Pareto‐optimal solutions as per the user's choice with fewer iterations, and therefore could be effectively used for solving the multiobjective CM problem. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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