A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems

Energies, Год журнала: 2024, Номер 17(16), С. 4128 - 4128

Опубликована: Авг. 19, 2024

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient infrastructure. Their is vital achieving sustainability among all clean sources, including wind, solar, hydropower. This review paper provides thoughtful analysis the current status grid, focusing on integrating various RES, such as wind grid. highlights significant role RES in reducing greenhouse gas emissions traditional fossil fuel reliability, thereby contributing to environmental empowering security. Moreover, key advancements grid technologies, Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), Supervisory Data Acquisition (SCADA) systems, are explored clarify related topics usage technologies enhances efficiency, resilience introduced. also investigates application Machine Learning (ML) techniques management optimization within with techniques. findings emphasize transformative impact advanced alongside need continued innovation supportive policy frameworks achieve future.

Язык: Английский

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Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models

Journal of Energy Storage, Год журнала: 2024, Номер 96, С. 112613 - 112613

Опубликована: Июнь 24, 2024

Язык: Английский

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A hybrid model based on CapSA-VMD-ResNet-GRU-attention mechanism for ultra-short-term and short-term wind speed prediction

Renewable Energy, Год журнала: 2024, Номер unknown, С. 122191 - 122191

Опубликована: Дек. 1, 2024

Язык: Английский

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Application of fuzzy control based on adaptive neural network in high voltage output boost circuit

IEICE Electronics Express, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

To improve the performance of boost circuit, a fuzzy inference systems (FIS) design method based on adaptive neural networks (ANN) system identification is proposed for circuit. Using ANN training data to generate initial first-order Takagi—Sugeno (T-S) FIS. Adjust FIS parameters by comparing them with testing and checking data, iterate until error within an acceptable range form final The steady-state dynamic capabilities circuit under control have been verified through simulation experiments be superior traditional proportion integral differential (PID) control. experimental results show that when input voltage jumps from 28V 22V, speed improved 21.5% compared PID.

Язык: Английский

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A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations

Energies, Год журнала: 2025, Номер 18(9), С. 2239 - 2239

Опубликована: Апрель 28, 2025

The growing global energy demand and the pursuit of sustainability highlight transformative potential artificial intelligence (AI) machine learning (ML) in systems. This thematic review explores their applications generation, transmission, consumption, emphasizing role optimizing renewable integration, enhancing operational efficiency, enabling data-driven decision-making. By employing a approach, this study categorizes analyzes key challenges opportunities, including economic considerations, technological advancements, social implications. While AI/ML technologies offer significant benefits, adoption developing nations faces challenges, such as high upfront costs, skill shortages, infrastructure limitations. Addressing these barriers through capacity building, international collaboration, adaptive policies is critical to realizing equitable sustainable integration

Язык: Английский

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An overview of Artificial Intelligence applications to electrical power systems and DC microgrids

E3S Web of Conferences, Год журнала: 2024, Номер 547, С. 01002 - 01002

Опубликована: Янв. 1, 2024

Microgrids are composed of distributed energy resources such as storage devices, photovoltaic (PV) systems, backup generators, and wind conversion systems. Because renewable sources intermittent, modern power networks must overcome the stochastic problem increasing penetration energy, which necessitates precise demand forecasting to deliver best possible supply. Technologies based on artificial intelligence (AI) have become a viable means implementing optimizing microgrid management. Owing sporadic nature sources, offers range solutions growth in sensor data compute capacity create sustainable dependable power. Artificial techniques continue evolve DC with aim perfect voltage profile, minimum distribution losses, optimal schedule power, planning controlling grid parameters lowering unit price. AI methods can improve Micro performance by monitoring reducing computational processing time. This paper comprehensive summary some most recent research used grids electrical system networks.

Язык: Английский

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Operative estimation of global horizontal irradiance using transfer functions through network types of artificial neural network in some selected sites in North-East Ethiopia: assessment and comparison

Heliyon, Год журнала: 2025, Номер unknown, С. e43101 - e43101

Опубликована: Март 1, 2025

Язык: Английский

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COVID-19 impact on wind and solar energy sector and cost of energy prediction based on machine learning

Heliyon, Год журнала: 2024, Номер 10(17), С. e36662 - e36662

Опубликована: Авг. 24, 2024

This study examines the impact of COVID-19 pandemic on renewable energy sectors across seven countries through techno-economic analysis and machine learning (ML). In China, fraction decreased in grid-connected systems due to 14.6 % higher diesel fuel prices. They reduced grid electricity prices, with Cost Energy (COE) reductions driven by a 2.8 inflation decrease 3 discount rate cut. The increase adoption USA during was initial operational costs components, significant rise government policy changes, despite reduction sell-back prices rising capital annual expanded capacity. Canada noted shift standalone 50 lower PV 2 WT 48 cost rise, reducing COE except grid/WT scenarios. Germany managed costs, decreasing inflation. India HRESs sevenfold capacity increase, lowering COE. Japan saw stable minimal variation. Iran faced economic challenges 104 impacting decrease. Machine forecasts suggest that may cause an China effects.

Язык: Английский

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Optimizing Lightweight Recurrent Networks for Solar Forecasting in TinyML: Modified Metaheuristics and Legal Implications

Energies, Год журнала: 2024, Номер 18(1), С. 105 - 105

Опубликована: Дек. 30, 2024

The limited nature of fossil resources and their unsustainable characteristics have led to increased interest in renewable sources. However, significant work remains be carried out fully integrate these systems into existing power distribution networks, both technically legally. While reliability holds great potential for improving energy production sustainability, the dependence solar plants on weather conditions can complicate realization consistent without incurring high storage costs. Therefore, accurate prediction is vital efficient grid management trading. Machine learning models emerged as a prospective solution, they are able handle immense datasets model complex patterns within data. This explores use metaheuristic optimization techniques optimizing recurrent forecasting predict from substations. Additionally, modified optimizer introduced meet demanding requirements optimization. Simulations, along with rigid comparative analysis other contemporary metaheuristics, also conducted real-world dataset, best achieving mean squared error (MSE) just 0.000935 volts 0.007011 two datasets, suggesting viability usage. best-performing further examined applicability embedded tiny machine (TinyML) applications. discussion provided this manuscript includes legal framework forecasting, its integration, policy implications establishing decentralized cost-effective system.

Язык: Английский

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Beyond conventional predictions: unfolding the ensemble Kalman filter's publications in renewable energy

Опубликована: Апрель 19, 2024

Язык: Английский

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