Load Profile Synthesis and Wind-Power-Generation Prediction for an Isolated Power System

This paper investigates the power load composition of an isolated power system using a load survey study and estimates wind power generation with a probabilistic network. The typical load pattern of various customer classes is derived, which are then used to derive the total power consumption of all customers within each class. A probabilistic neural network is used to solve the wind power generation based on the wind speed for an offshore island in Taiwan. With the hourly wind speed and load composition, the power generation of diesel generators was obtained. Results of this paper demonstrate that wind power generation can economically and effectively replace the power generation of the island's diesel power plant and provide partial power-supply capability for the net peak load requirement.