Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Dec. 9, 2023

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges optimizing the performance of energy sources and backup are proposed, especially under conditions heavy loads or low renewable output. The problem lies need for an efficient mechanism that can enhance availability while protecting extending lifespan various system. Furthermore, it necessary adapt system's operations variations climatic sustained effectiveness. To address identified problem. It proposed use intelligent (IPMC) system employing fuzzy logic (FLC). IPMC designed optimize systems. aims predict adjust operating processes based on conditions, providing dynamic adaptive strategy. integration FLC specifically emphasized its effectiveness balancing multiple ensuring steady secure operation with offers several advantages over existing strategies. Firstly, showcases enhanced availability, particularly challenging such as Secondly, protects extends sources, contributing long-term sustainability. adaptation adds layer resilience system, making well-suited diverse geographical conditions. realistic data simulations MATLAB/Simulink, along real-time findings from RT-LAB simulator, indicates reliability practical applicability Efficient load supply preserved batteries further underscore benefits logic-based strategy achieving well-balanced operation.

Language: Английский

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Adaptive and coordinated load frequency control for isolated microgrids considering battery state of charge dynamics

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 112, P. 115467 - 115467

Published: Jan. 24, 2025

Language: Английский

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Performance and economic analysis of photovoltaic/thermal systems with phase change materials and a parallel serpentine design in dusty conditions

Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 125890 - 125890

Published: Feb. 1, 2025

Language: Английский

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Advancing solar energy forecasting with modified ANN and light GBM learning algorithms

AIMS energy, Journal Year: 2024, Volume and Issue: 12(2), P. 350 - 386

Published: Jan. 1, 2024

<abstract> <p>In the evolving field of solar energy, precise forecasting Solar Irradiance (SI) stands as a pivotal challenge for optimization photovoltaic (PV) systems. Addressing inadequacies in current techniques, we introduced advanced machine learning models, namely Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and Support Vector Machine Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency predictive accuracy, specifically engineered to overcome common pitfalls such overfitting data inconsistency. The RELAD-ANN model, its multi-layer architecture, sets standard detecting nuanced dynamics SI meteorological variables. By integrating sophisticated regression methods like Regression (SVR) Lightweight Gradient Boosting Machines (Light GBM), our results illuminated intricate relationship influencing factors, marking novel contribution domain energy forecasting. With R² 0.935, MAE 8.20, MAPE 3.48%, model outshone other signifying potential accurate reliable forecasting, when compared existing Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, 1-dimensional Convolutional Network, while LSIPF showed limitations ability. Light GBM emerged robust approach evaluating environmental influences on SI, outperforming SVR model. Our findings contributed significantly systems could be applied globally, offering promising direction renewable management real-time forecasting.</p> </abstract>

Language: Английский

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Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

Advanced Theory and Simulations, Journal Year: 2024, Volume and Issue: 7(7)

Published: April 30, 2024

Abstract Effective solar energy utilization demands improvements in forecasting due to the unpredictable nature of irradiance (SI). This study introduces and rigorously tests two innovative models across different locations: Sequential Deep Artificial Neural Network (SDANN) Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims identify complex patterns weather data, while RFGB, combining Boosting, proves more effective by offering a superior balance efficiency accuracy. The research highlights SDANN model's learning capabilities along with RFGB unique blend their comparative success over existing such as eXtreme (XGBOOST), Categorical (CatBOOST), Gated Recurrent Unit (GRU), K‐Nearest Neighbors (KNN) XGBOOST hybrid. With lowest Mean Squared Error (147.22), Absolute (8.77), high R 2 value (0.80) studied region, stands out. Additionally, detailed ablation studies on meteorological feature impacts model performance further enhance accuracy adaptability. By integrating cutting‐edge AI SI forecasting, this not only advances field but also sets stage for future renewable strategies global policy‐making.

Language: Английский

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Enhanced accuracy in solar irradiance forecasting through machine learning stack-based ensemble approach

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: Jan. 10, 2025

Accurate solar irradiance (SI) prediction is vital for optimizing photovoltaic systems. This study addresses shortcomings in existing forecasting methods by exploring advanced machine-learning techniques using meteorological satellite data. We develop three novel models SI forecasting: Stack-based Ensemble Fusion with Meta-Neural Network (SEFMNN), Extreme Gradient Boosting-Squared Error (XGB-SE), and Learning Machine (ELM). These predict All-sky Clear-sky shortwave across Chinese provinces (Guangdong, Shandong, Zhejiang) one Saudi Arabian province (Najran). The SEFMNN model combines Artificial Neural (ANN), Random Forest (RF), Support Vector (SVM) to improve accuracy. XGB-SE employs a specialized loss function manage extreme values historical are designed mitigate overfitting data inconsistency while balancing computational efficiency predictive Comparative analysis reveals that outperform the ELM model, achieving an R2 of 0.9979, MAE 0.0231, MSE 0.0020 Najran. demonstrates significantly enhances forecasting, aiding efficient system planning operation.

Language: Английский

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Control of Hybrid Renewable Energy and Storage-Integrated Multi-energy Microgrid with Electric and Gas Boiler, Electric Vehicle, and Household Loads

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 247 - 259

Published: Jan. 1, 2025

Language: Английский

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Forecasting techniques for power systems with renewables

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 381 - 412

Published: Jan. 1, 2025

Language: Английский

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A New Smart Charging Electric Vehicle and Optimal DG Placement in Active Distribution Networks with Optimal Operation of Batteries

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104521 - 104521

Published: March 1, 2025

Language: Английский

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Optimal operation strategy of energy storage system considering forecasting error of wind power generation under different weather condition

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 4345 - 4358

Published: April 10, 2025

Language: Английский

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