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Energy mix optimization is considered an essential tool for assessing the efficacy of renewable energy system (RES). Countless approaches have been employed by grid planning engineers and scientists to optimally size grid-connected photovoltaic (PV) systems. Optimizing grid-connected PV systems is beneficial since it causes a reduction in the energy produced by the conventional power plants, and as a result, a reduction in the harmful emissions that are released as by-products. In this study, a large commercial load in the city of Makkah in Saudi Arabia is connected to an optimally designed grid-connected PV systems with the support of a battery storage system (BSS). First, using HOMER software, the system components are chosen by considering the electrical and economic variables. A comparative investigation is carried out for three configuration systems along with the detailed technical study for the proposed systems. Simulation results recommend utilizing a battery system and RES with a minimum cost of energy (COE) of $0.11. Using both HOMER and MATLAB, the PV, converter, and battery system optimal size are estimated by considering the net present cost (NPC), COE, excess electricity and energy savings, emissions, and renewable penetration ratio to find the most economical and environmental-friendly combination. The results show that 1.60 MW is the optimal capacity of each PV, converter, and battery BSS elements to achieve the minimum targeted emission and COE, which resulted in a reduction in the overall NPC of the scheme. When the optimal design is configurated, it is found that the unmet load is zero. [ABSTRACT FROM AUTHOR]

Remote areas electrification is usually achieved through costly and eco-unfriendly diesel generator (DG) systems with additional cost-obstacles arising through diesel–fuel transportation. Hybridization of renewable energy systems (RESs) with DG units offers a dynamic approach of electrification for the remote areas. A techno-economic study is required to assess the viability of RES in such locations. This research work examines the case of Al-Shumaisi cluster, located in the west of Makkah Province, Kingdom of Saudi Arabia (KSA). This is where the Makkah transport company (MTC) is located. A techno-economic investigation is performed for a commercial load profile of MTC with a hybrid wind/DG/fuel cell/battery/electrolyzer system. In this study, two hybrid configurations are proposed: 1) wind/DG/FC/battery system; 2) wind/FC/battery system. Moreover, another configuration that uses only DG units is also investigated. A comparative study is achieved for the most optimal combination, depending on the gross present cost (GPC) and levelized cost of energy (LCOE) as the essential parameters. Furthermore, other financial parameters like operation and maintenance (O&M) costs, capital costs, and replacement costs are investigated. The well-recognized simulation tool, HOMER (Hybrid Optimization of Multiple Energy Resources) developed by the National Renewable Energy Laboratory, is used to fulfill this study. The sensitivity investigation is carried out by optimally scaling the configuration capacities as well as by monitoring the future changes like weather, load demand, and fuel price uncertainties. The results of the sensitivity investigation and HOMER simulation confirm that the wind/DG/FC/battery hybrid off-grid configuration is the most economic and eco-friendly solution with a total GPC of 7.045 million USD, COE of 0.271 USD/kWh, and CO2 emissions of 566 tons. This signifies the 13.84% reduction of net present cost compared to DG-based units, with a 64.2% decrease of pollutants and zero unmet load. [ABSTRACT FROM AUTHOR]

In this paper, the potential of solar and wind energy-based distributed generation (DG) in Saudi Arabia is simultaneously analyzed with the aim of maximizing the utilization of available resources. It begins with an analysis of DG application potential for wind and solar energy resources in various regions of Saudi Arabia. The progress of DG applications in terms of research, planning, and exploitation of wind and solar energy resources is then presented. An assessment of the DG contribution to the energy sector of Saudi Arabia has been conducted and the barriers and challenges for the implementation of DG systems in the country are discussed with suggested measures to overcome the challenges. The main findings are that with the huge potential of wind and solar resources for DG applications the country has targeted 50 GW of wind and solar capacity by the year 2040. The Saudi government is expected to provide full support in the form of financial incentives for solar and wind energy projects in order to boost renewable energy development. [ABSTRACT FROM AUTHOR]

The tilt angle of photovoltaic (PV) panels is a major factor affecting the amount of solar radiation falling on the panel surface. The optimum tilt angle depends on the position of the sun, latitude and local geographical characteristics. This paper investigates PV panel optimum tilt angles for various cities in the Kingdom of Saudi Arabia. For simulation purposes, horizontal solar radiation data for the study cities was obtained from NASA. MATLAB software was used to optimize tilt angle by maximizing solar radiation. Experiments were conducted to validate the theoretical requirement for negative tilt angles during summer. Results demonstrate that adjusting tilt angles six times per year harvests 99.5% of the solar radiation that could be attained with daily PV panel adjustment. [ABSTRACT FROM AUTHOR]

The potential of hybrid wind/solar energy system in the west coast area of Saudi Arabia is analyzed in this paper. The investigation puts emphasis on the energy production and cost of energy from both wind turbine and photovoltaic (PV) in the hybrid system. Unmet electric load and excess electricity are taken into consideration. The annual average solar irradiation and wind speed considered in this study are 5.95 kWh/m 2 /day and 3.53 m/s, respectively. MATLAB and HOMER software are used to perform the technical and economic analyses of the hybrid system. As indicated from the simulation results, the PV array shares more electricity production than the wind turbine generator if both wind turbine and PV array are utilized in the wind/solar hybrid system with the same sizes. The wind levelized cost of energy is $0.149/kWh, which is more expensive than the solar energy of $0.0637/kWh. The energy cost of the hybrid system is dominated by battery and wind turbine expenses. [ABSTRACT FROM AUTHOR]

In this paper, investigation of the performance of a support vector machine (SVM) and artificial neural networks (ANN) in predicting solar radiation on PV panel surfaces with particular tilt angles was carried out on two sites in Saudi Arabia. The diffuse, direct, and global solar radiation data on a horizontal surface were used as the basis for predicting the radiation on a tilted surface. The amount of data used is equivalent to 360 days, averaged from the 5-min basis data. By solving the tilt angle equation, an optimum value of solar radiation was obtained using a tilt angle of 16° and 37.5° for Jeddah and Qassim locations, respectively. The evaluation of performance and comparison of results of ANN as well as SVM and the measured/calculated data are made on the basis of statistical measures including the root mean square error (RMSE), coefficient of correlation (CC), and magnitude of relative error (MRE). The speed of computation of the algorithms is also considered for comparison. Results indicate that for Jeddah, the CC for SVM is between 0.918 and 0.967 for training and in the range of 0.91981–0.97641 for testing while that of ANN is in the range of 0.517–0.9692 for training and 0.0361–0.0961 for testing. For Qassim, the results are even better with CC of 0.999 for training and 0.987 for testing ANN showed higher values of MRE ranging between 0.19 and 1.16 and SVM is between 0.33 and 0.51 for training and testing respectively. In terms of speed of computation, it is observed that SVM is faster than ANN in predicting solar radiation data with a lower speed of 2.15 s compared to 4.56 s for ANN during training. Moreover, SVM has lower values of RMSE indicating that it is robust and has the capability to minimize errors during computations. Therefore, SVM has significantly higher accuracy, robust during computation and is faster in predicting the radiation on the tilted surfaces in comparison with ANN. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of renewable energy feed-in tariffs (REFIT) for the selected regions of the globe is done with the aim of drawing lesson for Saudi Arabia. A brief background and status of renewable energy sources (RES) utilization in Saudi Arabia is given at the beginning of this paper, followed by a review of the feed-in tariff (FIT) design models reported in literature. The application of the policy in Europe, the United States, Australia, Asia and Africa is then reviewed. Some observations from the analysis of the FIT application in different countries have been discussed and finally, lessons have been drawn from the study. Some policy recommendations have been given for Saudi Arabia. The analysis of the lessons identified shows that the application of FIT scheme in Saudi Arabia is likely to speed up the development of renewable energy resources within the area. [ABSTRACT FROM AUTHOR]