الفهرس 
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Abstract
In this work, new modeling techniques for photovoltaic (PV) and horizontal wind turbine (HWT) systems are proposed based on the graphical user interface (GUI) with the lookup table and the artificial neural network (ANN) methods. The developed models are characterized by great features of simplicity and generality that help the designer select the suitable PV and HWT units based on only one parameter which is the power. Thus, the user does not need extensive knowledge of complicated equations or correlations of aerodynamics or solar beam characteristics. Furthermore, the model is accurate enough to cover a wide range of power ranging from 0.5 to 300 W for PV module and from 0.5 to 8000 kW for HWT. The neural network modules are implemented using the back-propagation learning algorithm because of their benefits in obtaining the ability to predict values in-between learning values, as well as the interpolation of learning curve data. This is done with a suitable number of network layers and neurons at the minimum error and precise manner. This modular model has great capabilities to overcome previous programming problems and limitations such as the recycle streams and differential equation problems. The developed models are validated and compared with the actual data from the manufacturing manuals for wind turbines and PV performance sheets. The results reveal that the actual data exactly matches the results of the developed model. The PV model results show that the open circuit voltage ranges from 20 to 50 V, and the short circuit current does not exceed 9 A according to the 300 W module. The module efficiency ranges from 14 to 17%. Furthermore, the results of the HWT model show that the cut-in wind speed does not exceed 4 – 4.5 m/s as the maximum value while the rated wind speed does not exceed 20 – 25 m/s. For the power range from 1 to 8000 kW, the rotor diameter and hub height are found in the ranges of 50 and 140 m, respectively. A case study of the Zafarana-5 wind farm is simulated using the HWT model, which provides a very good matching between the simulated data and the real data. Moreover, the performance of reverse osmosis (RO) desalination plant in Ain Sokhna is investigated experimentally as a case study under different operating conditions. The frequency of the high-pressure pump motor is changed from 35 Hz to 50 Hz using a variable frequency drive (VFD). In addition, the feed water temperature and the reject control valve are controlled. The work investigates the effect of VFD frequency control, feed water temperature variations and the reject control valve opening on specific energy consumption (SEC), water production cost (Cw), fresh water salinity and plant productivity. The results show that both SEC and Cw are reduced by increasing the frequency up to 45 Hz before rising thereafter. Also, the SEC and Cw decrease with increasing feed temperature and reduced reject valve opening. As a result, the optimum values of SEC and Cw are found at 45 Hz and 40˚C with 25% reject valve opening, representing 4.5 kWh/m3 and 0.55 $/m3, respectively. Then, a new dynamic multi-input and output (MIMO) model has been proposed for the RO process using the system identification method. The input variables are the VFD frequency and the reject control valve opening while the output variables are the permeate flow rate and the permeate salinity. The temperature of the feed water is supposed to be a disturbance in this process. The MIMO model is validated by comparing the step response of the model with the experimental data that provides good results for evaluation and verification. The comparison results show fit ratios of 89.7 and 92.19% for the permeate flow rate at feed temperatures of 15˚C and 40˚C, respectively. Also, 89.2% and 94.39% fit ratios can be observed for the permeate salinity at feed temperatures of 15˚C and 40˚C, respectively. As a result, the obtained dynamic model can be easily used for a process control loop implementation in order to ensure optimum operating conditions and reduce the water production cost. Finally, a steady-state model of the RO plant is well presented based on physical laws of the process which can be used to be coupled with the proposed PV and HWT models. A technoeconomic study of the PV-HWT-RO model is developed. The model consists of five subsystems including RO plant model, PV model, HWT model, battery model, and the control room unit. The process is optimized to have the lowest SEC under different operating conditions and variable fresh water productivity ranging from 100 to 10000 m3 per day. The PV-HWTRO model can determine the optimal sizing of PV modules, batteries and wind turbines based on average solar radiation, average wind speed, and plant productivity. The model also can provide the total annual costs of the system and the water production cost. It has a splitter control ratio to regulate the load distribution between PV and wind turbines relative to changes in climatic conditions. The model is simulated using Matlab/Simulink and presented good results.