Genetic Algorithm Used for Load Shedding Based on Sensitivity to Enhance Voltage Stability

Abstract

This paper presents an algorithm to calculate optimum load shedding with voltage stability consideration based on sensitivity of proximity indicator using genetic algorithm (GA). Schur's inequality based proximity indicator of load flow Jacobian has been selected, which indicates system state. Load flow Jacobian of the system is obtained using Continuation power flow method. If reactive power and active rescheduling are exhausted, load shedding is the last line of defense to maintain the operational security of the system. Load buses for load shedding have been selected on the basis of sensitivity of proximity indicator. The load bus having large sensitivity is selected for load shedding. Proposed algorithm predicts load bus rank and optimum load to be shed on load buses. The algorithm accounts inequality constraints not only in present operating conditions, but also for predicted next interval load (with load shedding). Developed algorithm has been implemented on IEEE 6-bus system. Results have been compared with those obtained using TeachingLearning-Based Optimization (TLBO), particle swarm optimization (PSO) and its variant. Keywords Schur's inequality Á Sensitivity Á Load shedding Á Genetic Algorithm Á Voltage stability List of symbols J Objective function J 0 Jacobian of load flow solution ls i Total load (real and reactive power) shed at ith load bus NG Total number of generator buses NL Number of load buses NLS Number of load buses selected for loadshedding P gk ,Q gk Lower bound on active and reactive power generation at kth bus " P gk , " Q gk Upper bound on active and reactive power generation at kth bus P gk o , Q gk o Active and reactive power generation at kth bus under current operating condition accounting load shed P gk p , Q gk p Active and reactive power generation at kth bus under predicted load condition accounting load shed V i o Load bus voltage at ith load bus under current operating condition accounting load shed V i p Load bus voltage at ith load bus under predicted load condition accounting load shed V i , " V i Lower and upper bound on ith load bus voltage k min Minimum Eigen value of load flow Jacobian s Schur's inequality proximity indicator value of load flow Jacobian s o Schur's inequality proximity indicator value of load flow Jacobian under current operating condition accounting load shed

A new specular reflection optimization algorithm

“…Intelligence Optimization Algorithm (such as Genetic Algorithm [1][2][3] (GA), Particle Swarm Optimization [4][5][6][7] (PSO), and Simulated Annealing 8,9 (SA)), which stems from the simulation of biological behavior, physical process, or chemical properties, is an effective method for solving complex optimization problems because it not only solves objective function regardless of continuity conditions but also has simple calculation principle and high-efficiency computing power.…”