الفهرس 
Components of the Hybrid System
Wind Turbine Capacity Factor (????????????????????????): concept, derivation, and evaluationOnly 14 pages are availabe for public view
 |  | from | 254 |  |  |
| | | from | 254 | | |
Abstract
The use of renewable energy in electricity generation became of essential importance. This is due to the increase of environmental awareness and fossil fuel resources depletion. To face the intermittency nature of the renewable electricity generation, the concept of integrated renewable generation arises. This concept means that each generation facility based on renewable generation should use different types of generation units. However, the generation integration is site specific as it depends on the available renewable energies. In other words, not all renewable generation technologies can be used together. In this thesis, a basic combination of solar and wind resources are used together to mitigate the overall fluctuation of the generated power output.
The research work in this dissertation aims to size optimally a 100% renewable energy system with battery energy storage. The optimization was based on the statistical behavior of both the generation units as well as the input data. The work is divided into data gathering, site matching, system sizing and energy management. In data gathering phase, the input data obtained from two sites. The obtained data are wind speed, ambient temperature, solar irradiance, and loading requirements. For meteorological data, the technique of typical meteorological year (TMY) is used to obtain one year of data. In site matching phase, the capacity factor concept is used and two separate novel models are developed. Each model links the input meteorological site data with the manufacturer’s parameter of the generation unit. The models are then validated using Monte Carlo Estimation (MCE) technique. The developed models enable the decision maker to select the optimum generation unit among several available in market. In system sizing phase, the output power from each generation unit is converted to statistical form. The power conversion is done using the transformation theorem and expected value theorem. Then a model express the behavior of the battery energy storage is developed.
The optimization is done on the overall cost of generating (wind turbines and PV panels) and storage units. The cost function is constrained by the power balance equation. The power balance consists of, hourly-generated power and hourly stored data versus loading data. Therefore, the output is the number of generation and storage units. The loading requirements are divided into steps from 1% to 100% of the loading power using 1% step. For each loading step, a relative design is obtained as well as overall cost. The genetic algorithm is used to perform all the sizing optimization. The energy management is done to maneuver the power between different system units based on the operation conditions and loading requirements.
The obtained results from site matching phase matches the recorded capacity factor values. This proves the adequacy of the novel proposed models. In addition, concerning capacity factor of wind turbine, the relative capacity factor model show the inverse dependence of the capacity factor to the rated wind turbine speed. While, in the PV panel, the dependence between the capacity factor and the panel nominal capacity is direct. Finally, the sizing phase shows that site meteorological data can decide the type of technology that can be installed