Synergistic Intelligent Control Mechanisms for Enhanced Grid Stability and Efficient Energy Management in Smart Power Systems DOI Creative Commons

K. Anusha,

R J Anandhi,

Alok Jain

et al.

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 616, P. 03011 - 03011

Published: Jan. 1, 2025

The need to physically upgrade and expand India’s inadequate overburdened electric power structure has emerged as a national imperative given contemporary societal, ecological, legal conditions well novelty risks. It targets the development of safer, more flexible reliable systems, in view increasing customers’ demand for enhanced quality. This article focuses characteristics new generation Smart Grids (SG) with focus on advanced communication control creating self-healing systems. paper examines capabilities like fault detection, isolation restoration along sophisticated QoS both bulk transmission distribution. reasoning provided here lends significant support adoption Dynamic Probabilistic Optimal Power Flow (DSOPF) an important enabler smart grid. expands how adding DSOPF DMS capability can facilitate these design objectives provide foundation progressive integrated grids.

Language: Английский

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms DOI Creative Commons

Negar Rahimi,

Sejun Park, Wonseok Choi

et al.

Journal of Electrical Engineering and Technology, Journal Year: 2023, Volume and Issue: 18(2), P. 719 - 733

Published: Jan. 12, 2023

With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar is one most common and well-known existing networks. But because its non-stationary non-linear characteristics, it needs to predict solar irradiance provide more reliable Photovoltaic (PV) plants manage supply demand. Although there are various methods irradiance. This paper gives overview recent studies with focus on forecasting ensemble which divided into two main categories: competitive cooperative forecasting. In addition, parameter diversity data considered also preprocessing post-processing All these investigated this study. end, conclusion been drawn recommendations future have discussed.

Language: Английский

Citations

46
Unleashing the potential of sixth generation (6G) wireless networks in smart energy grid management: A comprehensive review DOI Creative Commons
Mohammed H. Alsharif, Abu Jahid, Raju Kannadasan

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 1376 - 1398

Published: Jan. 13, 2024

As the world continues to seek sustainable and efficient energy solutions, integration of advanced technologies into smart grid management (SEGM) becomes a paramount focus. The advent Sixth Generation (6G) wireless networks promises revolutionize way grids are monitored, controlled, optimized. This review paper explores potential 6G in context SEGM. It discusses vision techniques that can be harnessed unlock full capabilities networks. delves challenges opportunities presented by technology, addressing issues such as scalability, security, real-time monitoring, dynamic spectrum access. Moreover, it how enable seamless with other technologies, blockchain cybertwin, enhance resilience reliability grids. comprehensive aims shed light on transformative role networks, paving for intelligent future management.

Language: Английский

Citations

29
Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models DOI Creative Commons
Rafiq Asghar, Francesco Riganti Fulginei, Michele Quercio

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 90461 - 90485

Published: Jan. 1, 2024

Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV forecasts are increasingly crucial for managing controlling integrated systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase accuracy of various geographical regions. Hence, this paper provides a state-of-the-art review five most popular ANN forecasting. These include multilayer perceptron (MLP), recurrent (RNN), long short-term memory (LSTM), gated unit (GRU), convolutional (CNN). First, internal structure operation these studied. It then followed by brief discussion main factors affecting their forecasting accuracy, including horizons, meteorological evaluation metrics. Next, an in-depth separate analysis standalone hybrid provided. has determined that bidirectional GRU LSTM offer greater whether used as model or configuration. Furthermore, upgraded metaheuristic algorithms demonstrated exceptional performance when applied models. Finally, study discusses limitations shortcomings may influence practical implementation

Language: Английский

Citations

16
A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights DOI Creative Commons
Blessing Olatunde Abisoye, Yanxia Sun, Zenghui Wang

et al.

Renewable energy focus, Journal Year: 2023, Volume and Issue: 48, P. 100529 - 100529

Published: Dec. 20, 2023

The efforts to revolutionize electric power generation and produce clean sustainable electricity have led the exploration of renewable energy systems (RES). This form is replenished cost-effective in terms production maintenance. However, RES, such as solar wind energies, intermittent; this one drawbacks its usage. In order overcome limitation, studies been undertaken forecast availability output. current trending method forecasting generated by RES artificial intelligence (AI) method. with all potential, traditional AI, Artificial Neural Network (ANN), Support Vector Machine (SVM) many more, does not it all. Because this, metaheuristic algorithms are being explored optimization techniques increase performance accuracy these AI methods some challenges models. study presents an insightful survey (traditional metaheuristic) systems. A existing surveyed literature was presented. taxonomy formulated, theoretical backgrounds were Also, various forms improved versions applied optimize classical systems' output surveyed. conceptual framework hybrid application formulated. Finally, discussion, insight, models future directions

Language: Английский

Citations

28
Renewable energy sources integration via machine learning modelling: A systematic literature review DOI Creative Commons

Talal Alazemi,

Mohamed Darwish, Mohammed Radi

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26088 - e26088

Published: Feb. 1, 2024

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms costs and technology, expecting a massive diffusion near future placing several challenges to power grid. Since RESs depend on stochastic —solar radiation, temperature wind speed, among others— they introduce high uncertainty grid, leading imbalance deteriorating network stability. In this scenario, managing forecasting RES is vital successfully integrate them into grids. Traditionally, physical- statistical-based models have been used predict outputs. Nevertheless, former are computationally expensive since rely solving complex mathematical atmospheric dynamics, whereas latter usually consider linear models, preventing from addressing challenging scenarios. recent years, advances machine learning techniques, which can learn historical data, allowing analysis large-scale datasets either under non-uniform characteristics or noisy provided researchers with powerful data-driven tools that outperform traditional methods. paper, systematic literature review conducted identify most widely learning-based approaches forecast results show deep artificial neural networks, especially long-short term memory accurately model autoregressive nature output, ensemble strategies, allow handling large amounts highly fluctuating best suited ones. addition, promising integrating forecasted output decision-making problems, such as unit commitment, address economic, operational managerial grid discussed, solid directions for research provided.

Language: Английский

Citations

15
Optimal design and system-level analysis of hydrogen-based renewable energy infrastructures DOI
Jinyue Cui, Muhammad Aziz

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 58, P. 459 - 469

Published: Jan. 24, 2024

Language: Английский

Citations

14
A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation DOI Creative Commons
Hongji Hu,

Samson S. Yu,

Hieu Trinh

et al.

Designs, Journal Year: 2024, Volume and Issue: 8(1), P. 10 - 10

Published: Jan. 18, 2024

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand manage the challenges faced by electric grid. Uncertainties systems can arise from various sources have significant implications for grid reliability, stability, economic efficiency. Australia, susceptible extreme weather such as wildfires heavy rainfall, faces vulnerabilities its network assets. The decentralized distribution population centers poses supplying remote areas, which a crucial consideration emerging technologies emphasized this paper. In addition, evolution modern grids, facilitated deploying advanced metering infrastructure (AMI), has also brought new system due risk cyber-attacks via communication links. However, existing literature lacks analysis encompassing related events, cyber-attacks, asset management, well advantages limitations mitigation approaches. To fill void, covers broad spectrum considering their impacts on explores conventional robust control probabilistic data-driven approaches modeling correlating uncertainty events state optimal decision making. This article investigates development scenario-based operations, microgrids (MGs) energy storage (ESSs), demand-side frequency ancillary service (D-FCAS) reserve provision regulation ensure design uncertainty-tolerance system. delves into trade-offs linked with implementation strategies, computational speed, It how these strategies may influence planning operation future grids.

Language: Английский

Citations

13
Frost management in agriculture with advanced sensing, modeling, and artificial intelligent technologies: A review DOI
Weiyun Hua, Paul Heinemann, Long He

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 110027 - 110027

Published: Feb. 1, 2025

Language: Английский

Citations

1
AI and IoT-Enabled Smart Urban Waste Management System for Efficient Collection, Segregation, and Disposal DOI Creative Commons

Anusuya Devi,

Navdeep Singh,

Nagarjuna Thandra

et al.

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 619, P. 03002 - 03002

Published: Jan. 1, 2025

This ‘Smart Urban Waste Management System’ outlines an innovative architecture for the current challenges in urban waste management through application of IoT, AI and Blockchain technologies to increase efficiency, transparency, sustainability. IoT enabled smart bins with sensors are used system monitoring levels tracking generating real time data platforms. Using computer vision, powered algorithms leveraged predict generation patterns planning, optimize collection routes optimisation automation segregation. Additionally, blockchain technology enables secure transparent collection, segregation disposal systems, accountability. communication protocols such as LoRaWAN NB-IoT implemented guarantee low cost high scalability, using minimal power, fitting very well any large city. In this dissertation, we investigate how these can be joined seamlessly form a circular, data-driven ecosystem that helps achieve principles circular economy by encouraging resource repurposing energy recovery.

Language: Английский

Citations

1
Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions DOI Creative Commons
Latifa A. Yousef, Hibba Yousef, Lisandra Rocha‐Meneses

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(24), P. 8057 - 8057

Published: Dec. 14, 2023

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied the management variable renewable energy (VRE) systems, and an outlook to future directions research field. The VRE types included are namely solar, wind marine varieties. AI techniques, particularly machine learning (ML), gained traction as result data explosion, offer method for integration multimodal more accurate forecasting applications. aspects include optimized power generation into grids, including demand forecasting, storage, system optimization, performance monitoring, cost management. Future applications proposed discussed, issue availability, quality, addition explainable (XAI), quantum (QAI), coupling with emerging digital twins technology, natural language processing.

Language: Английский

Citations

21