Mohammad Abdul Baseer

The stability performance of smart grid power systems is critical and requires special attention. Additionally, the combination of Battery Energy Storage (BES) systems, Solar Photovoltaic (SPV), and wind systems in the intelligent grid model provides utilities with excellent efficiency and dependability. However, a coordination grid with PV and other resources frequently results in severe issues, such as outages or power disruptions. A power outage in the grid might result in a power loss in the delivery system. As a result, the distributed grid model’s dependable performance is intended for integrated wind energy, SPV arrays, and BE systems. This paper proposes a renewable intelligent grid model to sustain solar power generation. The model incorporates a boost converter to optimize the performance of solar panels by converting the DC power generated by the panels into AC power for use in the grid. The boost converter is optimized using a novel Horse Herd Optimization Algorithm (HOA) method. In this case, the HOA method is used to optimize the control parameters of the boost converter, such as the duty cycle and the inductor and capacitor values. According to the final results, the proposed method has reduced the Total Harmonic Deformation (THD) and power loss. Additionally, the proposed method outperformed existing strategies related to the Expected Energy Not Supplied (EENS), Loss of Load Probability (LOLP), and Loss of Load Expected (LOLE), indicating the sustainability of power generation.

Water is the most important resource of the Earth and is significantly utilized for agriculture, urbanization, industry, and population. This increases the demand for water; meanwhile, the climatic condition decreases the supply of it. A rise in temperature of 1 degree Celsius might dry up 20% of renewable water resources, and to circumvent the water scarcity, it is necessary to reuse, create, and consume less water without wasting it. Water desalination is the process used to reuse the used or saline water by promptly extracting the salt or unwanted minerals and producing fresh consumable water. Based on the International Desalination Association, around 300 million people rely on desalination and the people of the Middle East region rely the most upon it. Around 7% of desalination plants are located in countries such as Saudi Arabia, Bahrain, Kuwait, and the United Arab Emirates. Reverse osmosis (RO) is the relevant desalination process in this type of area however, the conventional methods include more complexities, and hence to address this issue we proposed a novel approach known as Hybrid Capuchin and Rat swarm algorithm (HCRS) for effective water desalination technology using conventional sources and renewable energy in the middle east region. Moreover, a hybrid reverse osmosis plant model is developed for identifying renewable sources such as wind and solar energy. The proposed optimization can be used to mitigate the life cycle cost and enhances the reliability of the hybrid schemes. The experiment is conducted in a MATLAB simulator and compared the results with state-of-art works over the metrics such as relative error, system cost, and reliability. Our proposed method outperforms all the other approaches.

Over the past two decades, the integration and generation of photovoltaic power plants have risen to a huge level. The reason is increased pollution, which tends to cause ozone damage. The input source is a wind turbine, in which wind converts mechanical energy into electrical energy. The grid and solar array links have been optimized using the Maximum Power Point Tracker (MPPT), demonstrating the DC to DC converter. The deep learning framework could improve the performance and efficiency of MPPT. The RLC filter has been utilized for the specialized deduction in the current harmonic and voltage. The complexity, voltage fluctuation, and load imbalance were the causes to reduce power quality performance. Therefore, the proposed method has been designed to get error-free output. Furthermore, the MPPT based on a novel Vulture-based convolution neural model (VbCNM) is utilized to improve the efficiency and extract the power. The proposed method has resolved the open issues and technical implementation using the VBCNN technique. Furthermore, the precise error-free output has been determined by simulating MATLAB. The results obtained are more efficient, fast convergence, and clear. Finally, the simulation output was compared with various conventional methods and has attained a lower power loss as 0.1 kW and less sample times as 9 µs.

In this work optimization of the parabolic trough (PT) based concentrated solar power (CSP) system is analyzed for solar energy export from Saudi Arabia to European and Asian countries based on their peak load hours. Initially, Al-Ahsa, in east, and Tabuk, in the west, are compared for a PT based CSP plant for electrical energy generation as these locations are used to export solar energy from Saudi Arabia to Pakistan, India, and China in east and Greece, Germany and UK in the west. The analysis is performed to sell electrical energy generated by the CSP plant to the customers in their peak load hours to reduce their load factor and keep the capital cost minimum. A high voltage direct current (HVDC) transmission system is employed for this study as an undersea cable for Asian or European countries. The feasibility of 2 GW solar energy export by PT based CSP system transmitted by 2 GW bipolar 660 kV HVDC transmission line is based on the net present value of the project (NPV), real levelized cost of energy (LCOE), and the payback period of the project where Karachi, Pakistan has the highest NPV of 1866 MUSD with LCOE of 10.76 cents/kWh and a payback period of 12.7 years.

In a smart grid power system, reliability performance plays a crucial factor and requires additional focus. Moreover, the integration of Battery Energy Storage (BES) scheme, Solar Photovoltaic (SPV) and wind system in the smart grid system provide significant proficiency and reliability to the utilities. However, the grid coordination with the PV and other resources tends to cause major problems such as power interruption or else power outage. The outage of the power in the grid can cause power loss to the distribution system. Therefore, the novel reliability valuation of the smart grid system is developed for exaggeration of the SPV, wind and BES utilities based on the grid incorporation in Saudi Arabia. Furthermore, a novel Hobbled Shepherd Optimization (HSO) for boost converter control and Multi-Objective Based Golden Eagle (MOGE) algorithm for inverter control is proposed. The execution of this work has been done in MATLAB/Simulink. The simulation outcomes show that the projected method has attained the finest Total Harmonic Distortion (THD) and power loss. Also, the optimal reliability improvement has achieved by the projected methods while compared with the conventional methods in terms of Loss of Load Expected (LOLE), Loss of Load Probabilities (LOLP) and Expected Energy Not Supplied (EENS).

Introduction of photovoltaic (PV) systems in grid causes some complex power quality problems such as harmonics, voltage fluctuation, and load imbalance that can affect the grid coordinated PV system performance. Therefore, herein, a novel normalized reasoning-based fuzzy neural adaptive (NRFNA) control technique is proposed for the double-stage grid coupled solar PV system. In addition, the utmost power in a double-stage PV system is tracked at dissimilar environmental states by the proposed novel hybrid krill herd spider monkey (HKHSM)-based maximum power point tracking (MPPT) algorithm. The current computation control method preserves the grid current in unity power factor as sinusoidal for the system at point common coupling (PCC). The simulation of this proposed work is done on MATLAB/Simulink. Subsequently, the performance of the developed replica results is validated under dissimilar conditions of power quality. Furthermore, the proposed HKHSM-based MPPT along with a normalized reasoning herd spider monkey (NRHSM) control technique in a grid coordinated solar PV system has achieved 1.70% load current harmonics and 3.7% grid current harmonics. Consequently, the simulation outcomes are compared with the various conventional methods for proving the significance of the proposed system.

Installation of solar PV arrays at utility scale is gaining popularity nowadays because of the significant reduction in the cost of components as well as the global push towards clean energy. Solar PV plants along with Parabolic Trough Collector Solar thermal plants has the highest potential among the available Renewable Energy (RE) technologies existing in the world. The objective of this paper is to optimize the performance of commercial Solar PV and PTC power plant for a potential location and hence to arrive on a most feasible configuration for the site. A representative site located in the Abudhabi region of UAE considered for the study. This paper also details on the annual performance of the proposed plant along with its technical aspects. PVSYST 6.7.7 and SAM software is used to design the optimal size and its specifications of a 100MW PV grid connected system at Abu Dhabi (UAE) region. The design and arrangements of the system verified using simulation results. The annual energy generated from the designed utility-scale solar PV plant from PVSYST 6.7.7 calculated as 161198MWh/year with a performance ratio (PR) of 74.8% per year where as for PTC it has calculated as 157152MWh/year by using SAM. The STC (Standard Testing Condition) for the specification of PV modules are normalized operating conditions when testing the module. Design parameters such as module orientation, array yield, reference yield, final yield, global horizontal irradiation (GHI), and ambient temperature and loss factors evaluated. To evaluate the economic feasibility of proposed plant, the levelized cost of electricity (LCOE) is determined as $0.04404/kwh for Solar PV and as $0.01533/kwh for PTC, which is used to calculate lifecycle cost and energy production.

High energy utilization per capita and the country's gross domestic product (GDP) dependence on oil exports are the major problems of the Kingdom of Saudi Arabia (KSA). Abundant solar energy resources available in the country can help KSA to diversify its GDP. In this work, the photovoltaic (PV) energy outputs of KSA are compared with the potential PV energy customer such as European Countries, China, India, and Pakistan based on the levelized cost of energy (LCOE) and the net present cost (NPC). The PV energy is analyzed by a 4 GW grid connected PV system placed in the capital of each country. The grid sale price of PV energy is taken as half of the grid purchase energy price for each respective country. The high voltage direct current (HVDC) transmission of solar energy generated by the 4 GW PV system in KSA exported to potential customers is analyzed based on the NPC, LCOE, and payback period. Gwadar (Pakistan), (Antalya) Turkey, Karachi (Pakistan), and Ahmedabad (India) are economically feasible options with an LCOE of 5.2 ¢/kWh, 5.5 ¢/kWh, 6.2 ¢/kWh, and 7.5 ¢/kWh, respectively. The European countries are infeasible for PV energy export from KSA based on their load curves and NPC. The megacity of Karachi can be the first customer of KSA solar energy transmitted by HVDC.

In this article, an experimental study was performed to assess the potential thermal application of a new nanofluid comprising carbon nanoparticles dispersed in acetone inside an evacuated tube solar thermal collector. The effect of various parameters including the circulating volumetric flow of the collector, mass fraction of the nanoparticles, the solar irradiance, the tilt angle and the filling ratio values of the heat pipes on the thermal performance of the solar collector was investigated. It was found that with an increase in the flow rate of the working fluid within the system, the thermal efficiency of the system was improved. Additionally, the highest thermal performance and the highest temperature difference between the inlet and the outlet ports of the collector were achieved for the nanofluid at wt. % = 0.1. The best tilt angle and the filling ratio values of the collector were 30° and 60% and the maximum thermal efficiency of the collector was 91% for a nanofluid at wt. % = 0.1 and flow rate of 3 L/min.

The Middle East is one among the areas of the world that receive high amounts of direct solar radiation. As such, the region holds a promising potential to leverage clean energy. Owing to rapid urbanization, energy demands in the region are on the rise. Along with the global push to curb undesirable outcomes such as air pollution, emissions of greenhouse gases, and climate change, an urgent need has arisen to explore and exploit the abundant renewable energy sources. This paper presents the design, performance analysis and optimization of a 100 MWe parabolic trough collector Solar Power Plant with thermal energy storage intended for use in the Middle Eastern regions. Two representative sites in the Middle East which offer an annual average direct normal irradiance (DNI) of more than 5.5 kWh/m2/day has been chosen for the analysis. The thermodynamic aspect and annual performance of the proposed plant design is also analyzed using the System Advisor Model (SAM) version 2017.9.5. Based on the analysis carried out on the initial design, annual power generated from the proposed concentrating solar power (CSP) plant design in Abu Dhabi amounts to 333.15 GWh whereas that in Aswan recorded a value of 369.26 GWh, with capacity factors of 38.1% and 42.19% respectively. The mean efficiency of the plants in Abu Dhabi and Aswan are found to be 14.35% and 14.98% respectively. The optimization of the initial plant design is also carried out by varying two main design parameters, namely the solar multiple and full load hours of thermal energy storage (TES). Based on the findings of the study, the proposed 100 MW parabolic trough collector solar power plant with thermal energy storage can contribute to the sustainable energy future of the Middle East with reduced dependency on fossil fuels.

The energy demand of the middle east region is on the rise with an urgent need to tap the abundant renewable energy sources in the region. This is essential for the future development of the Middle-East region as it reduces the dependency on fossil fuels and eliminates the problems associated with air pollution and greenhouse gases. This paper presents the design, performance analysis and optimization of a 100 MWe parabolic trough based Concentrated Solar Power Plant with thermal energy storage for a location in Abudhabi which falls in middle east region. The thermodynamic aspect and annual performance of the proposed plant design is also analyzed using the SAM software. The annual energy generated from the CSP plant is found to be 333.15GWh with a gross to net conversion factor of 81.1%. The mean efficiency of the plant is found to be 14.35%. The performance of the proposed CSP plant is further optimized by varying the solar multiple and full load hours of TES. The proposed parabolic trough based CSP plant with thermal energy storage is found to be feasible for the location in Abudhabi and encourages further development of solar thermal power plants in the region.