THE COMPLEMENTARITY OF WIND AND SOLAR ENERGY IN KARABRUN, VLORA, ALBANIA

Abstract

In Albania, only hydropower has a major role in meeting the electricity load. Due to recent economic development and the growth of year round tourism in the region Vlora, Albania, there has been a surge in the electricity demand. Renewable energy, such as wind and solar sources appears to be a good solution to support the electrical system. Also, it is well known that wind and solar sources are cost competitive and environment friendly compared to other sources used to produce electricity. Renewable energies can be used combined to generate electricity. The advantages in this case have been related to decreased electricity storage and increased production´s electric load. Many studies have observed the variable nature of renewable sources and how they can complement each other making the total electricity production more sustainable than each source independently. Complementarity of renewable sources specifically solar and wind can be determined through statistical methods. The correlation as the measurement between two randomly distributed variables helps us estimate how linearly they are related. This study evaluates the complementarity between renewable energy sources, solar and wind based on the correlation coefficients calculated according to Pearson and Spearmen in the peninsula of Karabrun, the largest peninsula of Albania, located near the city of Vlora, southwestern Albania, where the Adriatic Sea meets the Ionian Sea. Pearson's product moment coefficient (r) is the simple correlation coefficient. This metric quantifies the association strength between two bivariate variables. Spearman's rank correlation coefficient (ρS) known as Spearman's Rho, its simple definition can be stated as the Pearson correlation utilizes the ranks instead of the real values, allowing us to assess the relationship between variables even if this is non linear. Through the analysis of monthly average wind speed and solar radiation data, a prediction of electricity production for each month of a calendar year was made. The forecast electricity from wind sources is based on the operation of the wind turbine rated as the most efficient for this area. The solar radiation data provided by the Global Solar Atlas served to select the adequate photovoltaic system. The interpretation of the results was done through the statistical program from IBM SPSS statistics. The analysis shows that both energy sources have a high negative value in complementarity. Correlation values represent that if one of the sources has low generation potential, the other complements the electric energy. It is going to increase the overall reliability of the system.