Investigating Power Grid Frequency Stability With Wind Energy Using Particle Swarm Optimisation Algorithm.

Abstract

Title: Investigating Power Grid Frequency Stability with Wind Energy using Particle Swarm Optimization Algorithm. Reg. No: F56/83018/2015.

Renewable energy sources (RES) have become a key area of concern and interest world-wide, including in Kenya where recently a 310 MW Wind Power plant was commissioned. They are clean energy technologies, in some cases occur in abundance and can mitigate against rising cost of fossil-fuels. Wind energy conversion technologies, in particular, have witnessed one of the highest growth rates in the energy sector in recent years. However, their continued integration into the utility poses serious challenges with respect to the stability of the electrical power system. They do not inherently provide system inertia from rotating masses of the rotor of the turbine. In fact, it is well documented that large scale integration of wind energy sources into the grid by displacement of the conventional sources of energy leads to frequency instability. This thesis investigated power grid frequency stability with wind energy using particle swarm optimization (PSO) algorithm. The investigation employed MATLAB/Simulink environment, in conjunction with power system analysis toolbox, on an IEEE 39 Bus Test System. The optimization employed PSO algorithm and load flow was conducted using Newton Raphson algorithm. Results obtained show that the voltage profile and frequency response profile improved significantly as the percentage wind penetration increased in the grid. The active and reactive power injections remained constant because the load was assumed constant. For the test system considered, the maximum wind power penetration level was established to be 32.1%. Notably, as the percentage of wind power penetration increased, the rate of change of frequency worsened from 0 % to 33.33 %. This is due to the intermittent nature of wind energy source.