An application of reference point‐based NSGA‐II for power system congestion management ensuring system stability

Abstract

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.

Multi-Objective Optimization and Test of a Tractor Drive Motor

“…The NSGA-II algorithm has better diversity than PSO [19,20], and it more greatly reduces the complexity of the algorithm compared with the non-dominated genetic algorithm by adding an elite strategy, density value estimation strategy, and fast unsupported sorting strategy [21,22]. The NSGA-II algorithm has been widely used in engineering problems such as power grid system planning, vehicle path optimization, and hybrid vehicle drive system matching [23][24][25].…”