The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 689 - 689

Published: Feb. 2, 2025

Although the impact of integrating solar and wind sources into power system has been studied in past, chaos caused by energy generation not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts using prediction models have developed real-time monitoring variability machine learning predictive algorithms contrast to conventional methods studying variability. This study focused on causes types variability, challenges, strategies used minimize grids worldwide. A summary top ten cases countries that successfully managed electrical presented. Review shows most success embraced advanced storage, grid upgrading, flexible mix as key technological economic strategies. seven-point conceptual framework involving all stakeholders for managing networks increasing variable renewable (VRE)-grid integration proposed. Long-duration virtual plants (VPPs), smart infrastructure, cross-border interconnection, power-to-X, flexibility are takeaways achieving a reliable, resilient, stable grid. review provides useful up-to-date information researchers industries investing energy-intensive

Language: Английский

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Optimizing deep neural network architectures for renewable energy forecasting

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: Nov. 12, 2024

An accurate renewable energy output forecast is essential for efficiency and power system stability. Long Short-Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Gated Recurrent Unit(GRU), Convolutional Neural Network-LSTM(CNN-LSTM) Deep Network (DNN) topologies are tested solar wind production forecasting in this study. ARIMA was compared to the models. This study offers a unique architecture Networks (DNNs) that specifically tailored forecasting, optimizing accuracy by advanced hyperparameter tuning incorporation of meteorological temporal variables. The optimized LSTM model outperformed others, with MAE (0.08765), MSE (0.00876), RMSE (0.09363), MAPE (3.8765), R2 (0.99234) values. GRU, CNN-LSTM, BiLSTM models predicted well. Meteorological time-based factors enhanced accuracy. addition sun data improved its prediction. results show deep neural network can predict energy, highlighting importance carefully selecting characteristics fine-tuning model. work improves estimates promote more reliable environmentally sustainable electricity system.

Language: Английский

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A Comprehensive Review of Artificial Intelligence Approaches for Smart Grid Integration and Optimization

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 24, P. 100724 - 100724

Published: Oct. 1, 2024

Language: Английский

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Geophysics and Geochemistry Reveal the Formation Mechanism of the Kahui Geothermal Field in Western Sichuan, China

Minerals, Journal Year: 2025, Volume and Issue: 15(4), P. 339 - 339

Published: March 25, 2025

This study investigated the formation mechanism of Kahui Geothermal Field in Western Sichuan, China, using geophysical and geochemical approaches to elucidate its geological structure geothermal origins. employed a combination 2D 3D inversion techniques involved natural electromagnetic methods (magnetotelluric, MT, audio magnetotelluric, AMT) along with analysis hydrogeochemical samples achieve comprehensive understanding system. Geophysical revealed three-layer resistivity within upper 2.5 km area. A interpretation was conducted on model, identifying two faults, Litang Fault Fault. The suggested that shallow part is controlled by Hydrochemical showed water chemistry HCO3−Na type, primarily sourced from atmospheric precipitation. deep heat source attributed partial melting middle crust, driven upwelling mantle fluids. process provides necessary thermal energy for Atmospheric precipitation infiltrates through tectonic fractures, undergoes circulation heating, interacts host rocks. heated fluids then rise faults mix cold water, ultimately emerging as hot springs.

Language: Английский

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Turning Trash into Treasure: Silicon Carbide Nanoparticles from Coal Gangue and High-Carbon Waste Materials

Molecules, Journal Year: 2025, Volume and Issue: 30(7), P. 1562 - 1562

Published: March 31, 2025

To reduce solid waste production and enable the synergistic conversion of into high-value-added products, we introduce a novel, sustainable, ecofriendly method. We fabricate nanofiber nanosheet silicon carbides (SiC) through carbothermal reduction process. Here, calcined coal gangue, converted from serves as source. The carbon sources are carbonized tire residue tires pre-treated kerosene co-refining residue. difference in source results alteration morphology SiC obtained. By optimizing reaction temperature, time, mass ratio, purity as-made products with nanofiber-like nanosheet-like shapes can reach 98%. Based on influence synthetic conditions calculated change Gibbs free energy reactions, two mechanisms for formation proposed, namely intermediate SiO CO to form SiC-nuclei-driven nanofibrous SiO-deposited surface nuclei-induced polymorphic (dominant nanosheets). This work provides constructive strategy preparing nanostructured SiC, thereby achieving “turning trash treasure” broadening sustainable utilization development wastes.

Language: Английский

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Smart Attention (SAB-LSTM): A Revolutionary Model for Advanced Solar Energy Forecasting

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 624, P. 04004 - 04004

Published: Jan. 1, 2025

Solar power forecasting has a significant relevance to the optimization of energy management and maintaining reliability systems against growing use renewable sources globally. Accurate solar generation would therefore allow for an increasingly effective integration into grid, supporting transition toward sustainable solutions. Most models suffer from following crucial defects: weak representation temporal dependency, failure generalize on different weather conditions, poor handling nonlinear relationships in data. In this respect, paper proposes new Smart Attention Bi-LSTM model that integrates strengths Bidirectional Long Short-Term Memory network with attention mechanisms. The SAB-LSTM further improves performance prediction by enabling dynamically focus most valuable historical data points hence overcome traditional methods forecasting. This method significantly learning complex patterns maintains high accuracy under variable seasonal conditions. was put severe test rich dataset Kaggle, including various across seasons. contribution research covers not only development methodologies like sector but also sheds light how deep techniques are important robustness forecasts.

Language: Английский

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Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10740 - 10740

Published: Dec. 7, 2024

Virtual power plants (VPPs) have emerged as an innovative solution for modern systems, particularly integrating renewable energy sources. This study proposes a novel prediction approach combining improved K-means clustering with Time Convolutional Networks (TCNs), Bi-directional Gated Recurrent Unit (BiGRU), and attention mechanism to enhance the forecasting accuracy of wind photovoltaic generation in VPPs. The proposed TCN-BiGRU-Attention model demonstrates superior predictive performance compared traditional models, achieving high robustness. These results provide reliable basis optimizing VPP operations sources effectively.

Language: Английский

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