Track: Energy

Abstract

In Saudi Arabia, more than 15% of the domestic oil production is used to generate electricity, and another 50% is consumed by electric power plants. KAPSARC reported that in 2018 around 50% of the total electricity consumption in the building's stock comes from the energy consumption of residential buildings. In its vision 2030, Saudi Arabia set an ambitious target in its national transformation program on renewable energy and redirect the oil and gas exploration to other higher-value uses. This goal is being achieved by setting an energy roadmap with the aim to supply 10% of its energy demand from renewable sources. As reported in National Renewable Energy Program, Saudi Arabia significantly increased its renewable energy targets and long-term visibility. The Renewable Energy Project Development Office of Saudi Arabia’s Ministry of Energy, Industry and Mineral Resources announced a substantial increase in the renewable energy share to generate 40 GW of solar energy and 16 GW of wind power over the next decade. The revised five year-target was increased to a total capacity of 27.3 GW with 20 GW solar energy and 7.0 GW wind energy by 2024, and a total of 58.7 GW generated from solar and wind energy by 2030. This paper attempts to study wind speed distribution and variability which represent one of the most important parameters in the design of wind turbine machines. Saudi Arabia is characterized by significant wind energy potential with high wind speed regions mostly in the Arabian Gulf and the red sea coastal regions, south east, and northern regions. This potential can therefore be used for electricity generation or irrigation (water pumping). The objective of this paper is to statistically analyse the wind speed distribution at different heights and different cities in Saudi Arabia, predict the monthly or annual wind power generation, and identify the most prospects site for wind turbine installation. The methodology used in this study is based on wind speed characteristics and variability using Weibull distribution function. The wind speed data for this study consists of wind speed distribution as a function of multiple local meteorological measurement data provided by KACARE. Data and monitoring station description are first described, then the wind speed statistics, the power density, frequency distribution, wind shear, capacity factor are presented. Probability density functions are used to calculate the wind power density at different location. Results show that the average wind speed in Turaif city was the highest compared to other locations with a value of 7.4868 m/s. The significance of this study relies on its capability to use statistical analysis to identify the most prospects windy site for wind turbine installation and help in decision-making process for possible investment in future wind turbine farm projects.