ENERGY FLOW MANAGEMENT IN HYBRID RENEWABLE ENERGY SYSTEMS

Abstract

The proposed methodology provided a statistical framework to size a hybrid renewable energy system (HRES). Starting from the raw hourly meteorological data of (wind speed, solar irradiance, ambient temperature, and loading requirements). The input data were used in innovative statistical power generation models. The presented framework gives the decision maker the methods and tools to select the proper HRES size regarding the site meteorological parameters and loading requirements. The conclusions from simulation results could be summarized as follows: • The intermittency of input meteorological data has an obvious impact on number of HRES units needed to cover the loading percentage. • Moreover, some of the collected seasonal meteorological data might not be smooth. In other words, the variations are high. This also affects the statistical behavior of the HRES design. • In addition, the sites with high wind speed values and high solar irradiance time (i.e. site I) can help to reduce the size of HRES. Along with that, this can make the system cheaper than sites with low wind speed and less irradiance time (i.e. site II).