The static charges and induced voltages from extra-high-voltage alternating current transmission lines (EHVACTLs) on parallel oil pipelines (POPLs) raise the risk levels for people and animals. Thus, ...the objective of this paper was to reduce and/or mitigate the electric field which is concentrated on POPLs by using grounded shield wires under EHVACTLs. Three techniques are employed to reduce the electric field effects on POPLs of two distinct types of transmission lines (TLs), 500 kV and 220 kV. The first technique involves raising the tower’s height to improve the clearance space between the POPLs and the TL conductors. The second technique is increasing the horizontal distance between the POPLs and the nearest stressed conductors of the TL. The third technique involves placing shield wires beneath the stressed conductors of the EHVACTLs. The electric field under the EHVACTLs is calculated with and without the grounded shield wires using charge simulation method. The results of the first technique revealed that with increasing the tower height from 10 m to 15, 20, 25, and 30 m, the electric field decreased by 43.75%, 62.5%, 68.75%, and 75%, respectively. Herein, employing the second technique, the electric field intensity is reduced by 20% and 21% depending on the POPL placed at a distance from the right stressed conductor equal to the horizontal clearance between conductors of 500 kV and 220 kV, respectively. Besides, the results of the third technique proved that the shield wires under the EHVACTLs reduced the electric field intensity on the POPLs by 17.65% and 24.71% for 500-kV and 220-kV TLs, respectively.
As a key portion of renewable energy resources (RESs), wind energy penetration is rapidly deployed. The effects of grid faults on grid-connected wind turbines (WTs) are causing problems for wind ...energy producers. To meet the necessary requirements, additional resources and technical interventions are needed. One of these requirements is low voltage ride-through (LVRT) of doubly fed induction generator (DFIG)-based WTs. This means that DFIG-WTs must stay connected to the grid during transient grid faults and supply active and reactive power after the fault is cleared. Many techniques for improving the LVRT capability of DFIG-WTs have been developed and this paper examines them. The paper also evaluates how well they align with grid codes, and offers case studies and simulations of the selected key techniques. Lastly, this paper provides guidelines and suggested designs for the LVRT techniques for DFIG-WTs to ensure they meet local grid codes.
The rise in pollution levels, leading to the emission of greenhouse gas emissions and the subsequent phenomenon of global warming, is anticipated to stimulate the expansion of Electrical Vehicles ...(EVs). Consequently, EVs will establish a connection with the electrical grid within this timeframe. The implementation of this technology will significantly influence the voltage profiles and loads of grid components. The study centered on the modeling and analysis of the integration of renewable energy sources and EVs into a microgrid. The microgrid comprises four essential elements: a diesel generator functioning as the primary power supply, a combination of a Photovoltaic (PV) farm and a wind farm for generating electricity, and a Vehicle-to Grid (V2G) system positioned near the microgrid's load. The continuous increase in their energy production rate makes microgrids important. Microgrids can be designed to meet the energy needs of different establishments, including hospitals, universities, and EVs charging stations, as well as the energy demands of a district, town, or industrial site. Charging stations are essential for the purpose of replenishing the battery of an EVs. This study investigates the influence of EVs on the microgrid network. EVs integrate non-linear circuit components into their structures This study focuses on the modeling and analysis of the renewable energy sources and EVs integration into the microgrid. and this study reviews the analysis of the microgrid with EVs using Matlab / Simulink.
The increase in electricity demand places its focus on renewable energies as sustainable energy resources. Wind energy is one of the most important green energy sources. The doubly fed induction ...generator (DFIG)‐based wind farm has now gained prominence due to its many advantages, such as variable speed operation and autonomous control of active and reactive power. When the DFIG stator windings are directly connected to the power grid, when a grid fault occurs, some unwanted high current may be produced in the rotor windings, and the protection system will prevent the rotor side converter (RSC) from operating. Therefore, voltage stability is a significant factor in maintaining the DFIG‐based wind farm in operation during grid faults and disturbances. This paper applies a static synchronous compensator (STATCOM) to restore the voltage levels of the Egyptian power grid connected to Al Zafarana‐5th stage wind farm, which is made of 100 Gamesa G52/850 kW DFIG machines. In this paper, the STATCOM is controlled by a proportional integral (PI) and is compared with a STATCOM controlled by fuzzy logic control (FLC). For simulation, the MATLAB/SIMULINK environment is used. Moreover, the simulation results show that STATCOM devices with fuzzy logic controllers improve the effects of grid faults and disturbances such as a single line to ground fault, a line to line fault, voltage sag, and voltage swell as compared with STATCOM with PI controllers. Also, STATCOM devices based on FLC improve the stability and power quality of the system and the power system restoration procedures for the existing and future‐planned wind farms.
The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system ...(BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO
2
) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $.
Abstract In the present day, there is widespread acceptance of autonomous hybrid power systems (AHPSs) that rely on renewable energy sources (RESs), owing to their minimal adverse effects on the ...environment. This paper evaluates and compares three various AHPS configurations comprising photovoltaic (PV) modules, wind turbines (WTs), batteries, and diesel generators (DGs), using a recent optimization approach. A new optimizer 'Dandelion-Optimizer' (DO) is applied to tackle design problems. Real-time meteorological data from Siwa Oasis in northwest Egypt was utilized to determine an optimum design of system components for the purpose of providing sustainable power to this remote region. The system configurations are effectively modelled and optimized to achieve the minimum cost of energy (COE), while also minimizing the loss of power supply probability (LPSP) and carbon dioxide (CO 2 ) emissions. As per the results, the last configuration (PV with both backup equipment) is the most optimal one in terms of the lowest cost, whereas the first configuration (PV and WT with both types of backup equipment) is the most optimal one with regards to the lowest carbon emissions.
Abstract Hybrid energy system (HES) is considered a solution to the energy supply issue, particularly in rural areas to achieve their sustainable development goals. The rise in energy consumption has ...increased the appeal of renewable resources, because of their potential to supply consumers with competitive, carbon-free electricity. This paper suggests strategies for managing energy and the most recently published optimizers for designing a stand-alone HES positioned in a remote region of southwest Egypt. This HES includes two green energy sources (wind and solar) and a storage system for energy (battery) as the first backup in addition to a second backup (diesel). The most recent sizing techniques employing the Chernobyl disaster optimizer, dynamic control cuckoo search (DCCS), and gold rush optimizer have been suggested to obtain the optimal design of the utilized HES. Furthermore, an in-depth evaluation of the applied optimization approaches has been achieved based on a comparative study. A detailed analysis of the studied algorithms aims to identify the optimum algorithm that provides the lowest possible cost at the highest level of reliability for the proposed HES. The simulation results verified that, the DCCS algorithm outperformed other algorithms, indicating its potential for achieving promising solutions.
Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several Machine ...Learning (ML) algorithms in predicting solar power generation through a detailed performance comparison. This paper analyzes six algorithms: CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF). Using a dataset of 4213 sets of solar power generation data, each model was trained and tested, with performance evaluated based on R-squared (R²) scores for the whole dataset, training set, and test set. Also, this study examined the mean and standard deviation of test set predictions to gauge how consistent each model was. The results showed that RF had the highest overall R² score of 0.940 and a training set score of 0.971. XGBoost demonstrated exceptional performance on the test set, attaining a high R² score of 0.822. CatBoost and GBMs exhibited strong performance, albeit with slightly lower R² values of 0.786 and 0.829, respectively. Although the MLP regressor and SVMs exhibited high training scores, they encountered difficulties in generalizing to unfamiliar data. This paper highlights the effectiveness of combining XGBoost and RF techniques in improving the accuracy of solar power forecasts. The investigation focuses on enhancing the precision and reliability of renewable energy projections through a comprehensive comparison of various contemporary ML techniques.
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