Optimal scheduling of renewable energy microgrids: A robust multi-objective approach with machine learning-based probabilistic forecasting

Applied Energy, Journal Year: 2024, Volume and Issue: 369, P. 123548 - 123548

Published: June 5, 2024

Language: Английский

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Review of Energy Management Systems in Microgrids

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(3), P. 1249 - 1249

Published: Feb. 2, 2024

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple generation with different features are used in microgrids, managing these becomes an important problem. The generated power of modules and turbines microgrids is constantly changing irradiation speed. Due to this impermanent uncertain nature renewable resources, generally, storage systems employed microgrid systems. To control the units sustain supply demand balance within provide sustainable reliable loads, management used. Many methods realize optimize microgrids. This review article provides a comparative critical analysis system can be tailored for purposes, which also discussed detail. Additionally, various uncertainty measurement summarized manage variability intermittency sources load demand. Finally, some thoughts about potential future directions practical applications given.

Language: Английский

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A backpropagation neural network-based hybrid energy recognition and management system

Energy, Journal Year: 2024, Volume and Issue: 297, P. 131264 - 131264

Published: April 8, 2024

Language: Английский

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Enhancing interpretability in power management: A time-encoded household energy forecasting using hybrid deep learning model

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 315, P. 118795 - 118795

Published: July 16, 2024

Nowadays, residential households, including both consumers and emerging prosumers, have exhibited a growing demand for active/reactive power. This surge arises from activities such as charging electrical devices, leveraging flexible resources, integrating renewable energy sources. To meet this escalating effectively, operators must ensure the provision of an ample supply Achieving necessitates identification influential factors generation precise forecasts power demand. Hence, work proposes efficient hybrid deep learning model consisting long short-term memory self-Attention (LSTM-Attention). incorporates explicit time encoding to forecast one-hour-ahead consumption active reactive using real-time data units. The integration models represents strategic development robustness. Leveraging inherent strengths architectures allows synergistic compensation that addresses limitations within each, contributing overall effective forecasting model. Moreover, Shapley Additive Explanations (SHAP) framework was employed interpretability, investigation underscores pivotal role incorporating temporal features into forecasting. SHAP findings can be effectively applied in management strategies optimally enhance response. Finally, evaluate effectiveness proposed model, comprehensive array performance metrics employed. results demonstrate superior accuracy compared alternative models. achieved lowest root mean square error (RMSE), absolute (MAE), percentage (MAPE) with value 0.0256, 0.0181, 14.255 %, respectively. formulated method also significantly contribute industrial sector by improving forecasting, thereby enhancing interpretability identifying most critical factors.

Language: Английский

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Achieving grid resilience through energy storage and model reference adaptive control for effective active power voltage regulation

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 22, P. 100533 - 100533

Published: Jan. 28, 2024

This article presents a comprehensive examination of the utilization energy storage units for voltage regulation in grids. Specifically, focus is on practical implementation active power control using Model Adaptive Control (MRAC) algorithm. The provides detailed description algorithm, considering grid parameters and showcasing application through MRAC. results implementing an unit global are discussed, highlighting advantages superiority this method.

Language: Английский

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Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids

Energy Conversion and Economics, Journal Year: 2024, Volume and Issue: 5(4), P. 259 - 279

Published: Aug. 1, 2024

Abstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been predominant means storage. However, technological advancements led recognition hydrogen as a promising solution address long‐term requirements microgrid systems. This study conducted comprehensive literature review aimed at analysing and synthesizing principal optimization control methodologies employed in hydrogen‐based within context building infrastructures. A comparative assessment was evaluate merits disadvantages different approaches. The techniques for management are categorized based predictability, deployment feasibility, computational complexity. In addition, proposed ranking system facilitates an understanding its suitability diverse applications. encompasses deterministic, stochastic, cutting‐edge methodologies, such machine learning‐based approaches, compares discusses respective merits. key outcome this research is classification various strategy MG, along with mechanism identify which will be suitable under what conditions. Finally, detailed examination advantages strategies controlling optimizing hybrid systems emphasis utilization provided.

Language: Английский

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Robust deep learning model with attention framework for spatiotemporal forecasting of solar and wind energy production

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100919 - 100919

Published: Feb. 1, 2025

Language: Английский

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Enhancing Weather Forecasting Integrating LSTM and GA

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(13), P. 5769 - 5769

Published: July 1, 2024

Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression deep learning methods for forecasting to deal with this variability. This research employs models estimate missing historical data three different time horizons, incorporating long short-term memory (LSTM) forecast short- medium-term conditions at Quinta de Santa Bárbara in the Douro region. Additionally, a genetic algorithm (GA) is used optimize LSTM hyperparameters. The results obtained show proposed optimized effectively reduced evaluation metrics across horizons. underscore importance of accurate making important decisions various sectors.

Language: Английский

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Statistical evaluation of a diversified surface solar irradiation data repository and forecasting using a recurrent neural network-hybrid model: A case study in Bhutan

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122706 - 122706

Published: March 1, 2025

Language: Английский

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Multistep Probabilistic Forecasting Approach for Tunnel Boring Machine Cutterhead Torque and Thrust Based on VMD-BDNN

International Journal of Geomechanics, Journal Year: 2025, Volume and Issue: 25(7)

Published: April 16, 2025

Language: Английский

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