Original Article
Transformation of Generated Electricity by Renewable Energies to Grid
Ling Tan; Bin Wang; Julong Chen; Yongqing Zhu; Junqiu Fan; Jiang Hu
Abstract
Medium- and long-term PV power forecasting is of great significance for the planning and management of new energy grids, and the existing medium- and long-term PV power forecasting methods generally suffer from the problems of insufficient processing means and low forecasting efficiency. Aiming at the ...
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Medium- and long-term PV power forecasting is of great significance for the planning and management of new energy grids, and the existing medium- and long-term PV power forecasting methods generally suffer from the problems of insufficient processing means and low forecasting efficiency. Aiming at the challenges of weak spatial and temporal correlation of medium- and long-term PV power data, as well as data redundancy and low forecasting efficiency brought about by long-time forecasting, this paper proposes a medium- and long-term PV power forecasting method based on the Transformer, SP-Transformer (Spatiotemporal-ProbSparse Transformer), which aims to effectively capture the spatio-temporal correlation between meteorological and geographical elements and PV power. The method embeds the geographic location information of PV sites into the model through spatio-temporal location coding and designs a spatio-temporal probabilistic sparse self-attention mechanism, which reduces model complexity while allowing the model to better capture the spatio-temporal correlation between input data. To further enhance the model's ability to capture and generalize potential patterns in complex PV power data, this paper proposes a feature pyramid-based self-attention distillation module to ensure the accuracy and robustness of the model in long-term forecasting tasks. The SP-Transformer model performs well in the PV power forecasting task, with a medium-term (48 hours) forecasting accuracy of 93.8% and a long-term (336 hours) forecasting accuracy of 90.4%, both of which are better than all the comparative algorithms involved in the experiment.
Original Article
Electricity Generation by Green Energy Sources
Magdi G. Muftah; Mohamed Salem; Mahmood Swadi; Khlid Ben Hamad; Mohamad Kamarol
Abstract
This paper proposes a new modified P–Q control scheme with a simple design using Static Quadratic Optimization (SQO) concept for a grid-connected hybrid system of photovoltaic (PV) and Fuel Cell (FC) sources. Contrary to traditional design practices involving voltage-oriented control (VOC) employing ...
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This paper proposes a new modified P–Q control scheme with a simple design using Static Quadratic Optimization (SQO) concept for a grid-connected hybrid system of photovoltaic (PV) and Fuel Cell (FC) sources. Contrary to traditional design practices involving voltage-oriented control (VOC) employing proportional-integral (PI) controllers or existing predictive strategies involving quadratic optimization by iterative computation, this proposed design of SQO directly computes an analytical expression of dq-axis current references as the optimal solution of a static-quadratic cost minimization problem. The proposed design enables optimal real and reactive power control simultaneously in a single step. The design of an efficient voltage-oriented current controller effectively utilizes measured values of grid current and voltage, as well as reference powers, which allows optimal bidirectional reactive controlled supply or absorption of reactive powers according to grid needs. The simulation of the grid-connected system has been performed in a MATLAB/Simulink environment. The simulation outcome verified the proposed P-Q voltage-oriented current controller design with a power factor of 0.998, phase displacement of 0.12°, total harmonic distortion (THD) levels of 1.2% for current and 0.39% for voltage, strictly within the IEEE-519 standards.
Original Article
Wind Energy
E.Yu. Rakhimov; N.R. Avezova; F.Z. Jamoldinov; Samad Emamgholizadeh; M. Ziaii
Abstract
This study analyzes wind speeds across various regions of the Republic of Uzbekistan to assess wind potential at 10 meters above ground level. Utilizing meteorological data from 77 ground-based stations collected between 2000 and 2022 at three-hour intervals, wind power densities were calculated to evaluate ...
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This study analyzes wind speeds across various regions of the Republic of Uzbekistan to assess wind potential at 10 meters above ground level. Utilizing meteorological data from 77 ground-based stations collected between 2000 and 2022 at three-hour intervals, wind power densities were calculated to evaluate wind potential, with average wind speeds determined monthly and annually. Data analysis from 13 regions identified locations with high wind energy potential, computing wind power densities and Weibull distribution parameters for wind speeds. Maps of average annual wind speed and power distribution, along with wind rose diagrams, illustrated predominant wind directions essential for optimal wind turbine placement. The highest average wind speeds were recorded in the Republic of Karakalpakstan, Navoi, Bukhara, Dehkanabad district (Kashkadarya), and Bekabad city (Tashkent region). Notable wind potential was found in Jaslyk district (Karakalpakstan) at 202.01 W/m², Navoi city (94.05 W/m²), and Dehkanabad district (85.33 W/m²). These results suggest that regions with high wind potential offer significant opportunities for efficient wind energy use. A comparison with previous studies on Uzbekistan's wind potential confirmed the accuracy and reliability of the data, indicating a high degree of consistency. This information can guide optimal planning and strategic placement of wind energy installations, furthering the development of “green energy” and enhancing Uzbekistan’s energy security