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Recent statistics indicate that as of 2023, the global renewable energy capacity has reached new heights, with Saudi Arabia significantly contributing to this growth through its strategic initiatives. The kingdom is particularly focusing on harnessing solar power, given its abundant sunlight, and is also exploring wind energy, leveraging its vast desert landscapes. These efforts are part of Saudi Arabia's broader strategy to become a global leader in renewable energy. This paper provides a detailed exploration of Saudi Arabia's ambitious journey from a fossil-fuel-dominated energy sector to a more sustainable, renewable-energy-driven future. Anchored in the broader context of global energy trends, the study emphasizes the critical shift toward renewable resources, with a particular focus on Saudi Arabia's unique position in this global movement. Key to this transition is Saudi Arabia's Vision 2030, a strategic framework that guides the country's renewable energy policies and initiatives. A comprehensive review of these policies, including other governmental and international collaborations that support renewable energy development, is offered. The current state of renewable energy in Saudi Arabia is assessed, including an overview of existing projects and production statistics. The paper also explores emerging technologies such as energy storage systems and smart grid solutions, emphasizing their role in Saudi Arabia's energy transition. An impact assessment is conducted to understand the environmental, economic, and social effects of this energy shift. The paper also includes a comparative analysis of other countries' renewable energy transitions, extracting lessons and best practices applicable to the Saudi context. Strategic recommendations and reflections on Saudi Arabia's future role in the global energy landscape are provided for stakeholders in energy policy, environmental planning, and sustainable development. [ABSTRACT FROM AUTHOR]

The use of certain electrical equipment connected to conventional power systems has raised concerns regarding the quality of the power system. Some equipment has been perceived to be unequipped to sustain the surges, faults, and distortions in traditional distribution systems. Common voltage issues exist, such as imbalance, harmonic distortion, sag, and swell. Such issues are usually contained using a dynamic voltage restorer (DVR). In this study, the determination of voltage references for DVR based on a robust and simple detection method of positive and negative sequence components is analyzed. This method detects the components in real time. With the proposed method, either the reference load voltages can be generated, or the reference DVR voltages can be generated, depending on which voltages are controlled. Moreover, contrary to other methods, the proposed technique avoids the use of phase-locked loop (PLL) and vector transformation. The voltage compensation technique controls the DVR in real time to prevent steady-state and disturbance conditions at the load side. The proposed system is evaluated by simulation and experimentation under several steady-state and transient cases. Finally, the proposed control is compared with common control methods. [ABSTRACT FROM AUTHOR]

Power generation for renewable energy sources increased rapidly during last few years. Similarly, the high gain dc–dc boost choppers are taking place of conventional power converters used for photovoltaic (PV) appliances. Researchers are developing different methods in order to provide high voltage gain, low ripple, reduced switch stress, low converter costs, and minimized variations of PV operating points. This study proposes a two-stage converter for a freestanding water pumping motor drive power by solar PV system. According to the proposed system, at first, a high gain (HG) cell and a DC-to-DC boost converter are combined to increase the PV voltage to high levels. Later on, the resulting dc voltage feds a three-phase synchronous reluctance motor drive that operates centrifugal pump load. The perturb and observe approach is utilized to get the maximum power out of the solar PV module. Moreover, indirect field-oriented control is implemented to accomplish smooth starting of synchronous reluctance motor. In order to validate the effectiveness of proposed technique, a MATLAB/Simulink environment-based simulation setup along with an experimental prototype is developed. Additionally, various cases are considered based on different operating conditions and irradiance levels to collect and analyse the results. [ABSTRACT FROM AUTHOR]

This paper presents a control method for a system composed of a photovoltaic (PV) array, five-phase impedance source inverter, five-phase induction motor and centrifugal pump. This method is based on controlling the motor speed to control the pump power as the insolation level or temperature change to attain the maximum power extraction from the PV-array. The motor speed is controlled by using artificial neural network (ANN) which is trained to provide the desired inverter frequency and modulation index at any insolation level and temperature to attain the maximum PV operating power. The data of the neural network are based on the operation of the induction motor at constant air gap flux and perturb and observe method for maximum power point tracking. Simulation results are obtained using MATLAB Simulink to verify the proposed control method. [ABSTRACT FROM AUTHOR]

The proton exchange membrane fuel cell (PEMFC), which is accounted as a reliable fuel cell (FC) technology, is mainly famous for its low operating temperature and expanded lifespan while also being highly efficient. Such merits have turned the PEMFC into an interesting option to be employed in a wide range of applications, comprising those for backup power, portable power, and vehicle power sources. Due to the output power's dependence on temperature and membrane water content, the PEMFC, however, faces a number of difficulties. The FC system's maximum output power can only be achieved at one specific operating point under varying operating circumstances. Therefore, maximizing the power obtained from the PEMFC is essential for improved operation and effective exploitation. It is noteworthy that a combinatorial technique using the developed modified manta ray foraging optimization (DMRFO) method and enhanced adaptive neuro-fuzzy inference system (ANFIS)-based incremental conductance (INC) has been deployed in this paper for the maximum power point tracking (MPPT) to optimally increase the power extraction capacity of an FC-resourced energy system. This method consists of two steps. In the first stage, the DMRFO algorithm is used to specify the best power levels while taking temperature and membrane water content fluctuations into account. This information would be utilized as the input–output training dataset in the ANFIS. The INC approach would initialize at this value in the second stage and seek the highest maximum power point (MPP). The dataset with the shrunk size given by the proposed DMRFO method for training the ANFIS in contrast to prevalent ANFIS is one of the benefits of adopting the ANFIS-based INC technique. This approach is also very quick, stable, and efficient. By taking into account a PMFC with a capacity of 7,000 W, the recommended technique is simulated and the results are compared with other methods to validate the performance. [ABSTRACT FROM AUTHOR]

Incorporating photovoltaic (PV) inverters in power distribution systems via static synchronous compensators (PV-STATCOM) during the nighttime has lately been described as a solution to improve network performance. Hunter prey optimization (HPO) is introduced in this study for efficient PV-STATCOM device allocation in distribution systems. HPO generates numerous scenarios for how animals could act when hunting, some of which have been expanded into stochastic optimization. The PV-STATCOM device allocation issue in distribution networks is structured to simultaneously minimize the electrical energy losses and improve the voltage profile while accounting for variable 24 h loadings. The impacts of varying the number of installed PV-STATCOM devices are investigated in distribution systems. It is tested on two IEEE 33-node and 69-node distribution networks. The effectiveness of the proposed HPO is demonstrated in comparison to the differential evolution (DE) algorithm, particle swarm optimization (PSO), artificial rabbits algorithm (ARA), and golden search optimizer (GSO). The simulation results demonstrate the efficiency of the proposed HPO in adequately allocating the PV-STATCOM devices in distribution systems. For the IEEE 33-node distribution network, the energy losses are considerably decreased by 57.77%, and the voltages variance sum is significantly reduced by 42.84%. The energy losses in the IEEE 69-node distribution network decreased by 57.89%, while voltage variations are reduced by 44.69%. Additionally, the suggested HPO is highly consistent than the DE, PSO, ARA, and GSO. Furthermore, throughout the day, the voltage profile at all distribution nodes surpasses the minimum requirement of 95%. [ABSTRACT FROM AUTHOR]

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective Grey Wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination Algorithm (MFPA), Multi-objective Lichtenberg Algorithm (MOLA), and Multi-objective Grasshopper Optimization Algorithm (MGOA). The outcomes illustrate that the developed MGWO provides an average improvement of 73.46%, 19.07%, and 15.15% compared to MFPA, MOLA, and MGOA, respectively. The validation of the developed MGWO is extended for a multi-objective form of welded beam design (WBD) by simultaneously minimizing the deflection and the manufacturing costs. Similar findings are obtained with different reference points, the developed MGWO provides an average improvement of 2.8%, 0.7%, and 3.04% compared to MFPA, MOLA, and MGOA, respectively. [ABSTRACT FROM AUTHOR]

Due to uncertain photovoltaic (PV) power generation, analyzing the voltage stability of transmission networks with a large PV plant is challenging. The stability of PV output power is a critical factor in establishing PV penetration levels in active transmission networks when assessing loading capabilities. Therefore, this article contributes to enhancing the understanding of how a static synchronous compensator (STATCOM) could be used as part of the power system network. STATCOMs are often used to improve the static and transient voltage, maintain transmission limits, and reduce low-frequency disturbances. With the help of a STATCOM unit, a proper investigation and analysis of dynamic voltage stability in a transmission system are presented. The validity of the study is evaluated by the Saudi Arabia grid code (SAGC). The system being tested is comprised of a 300 MW PV plant in Sakaka, Saudi Arabia, integrated into a 17-bus transmission power network and a STATCOM, which is employed at one of the buses. PSAT software is used to evaluate a number of case studies to determine the transient voltage stability with and without the STATCOM and the PV plant. The simulation findings demonstrate how STATCOM functions to improve the quality of the power system in accordance with the SAGC. In addition, the analysis of voltage stability demonstrates that the power network's resilience to short circuits at the PV system's grid connection point significantly impacts the voltage's dynamic behavior. [ABSTRACT FROM AUTHOR]

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller's design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. [ABSTRACT FROM AUTHOR]