Smart grids and renewable energy systems: Perspectives and grid integration challenges

Khalid, Muhammad

doi:10.1016/j.esr.2024.101299

Cited by 30 publications

(7 citation statements)

References 190 publications

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“…The final organised sequence for generator startup is G3, followed by G4, G2, G1, and lastly G5. Once the optimal startup sequence is established, the HLC verifies the availability of sufficient reserve energy to cover the energy required from the startup of these generators, by employing Equations ( 1) and (2). Following a successful assessment of the available reserve energy, the HLC initiates the startup process.…”

Section: Generic Restoration Action Time (Mins)mentioning

confidence: 99%

“…Consequently, there is a pressing need for ongoing research aimed at enhancing the overall resilience of power grids. This research is essential in addressing the dual challenges of maintaining system reliability in the face of increasing renewable energy integration while simultaneously advancing toward a more sustainable energy future [2].…”

Section: Introductionmentioning

confidence: 99%

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Novel Hierarchical Energy Management System for Enhanced Black Start Capabilities at Distribution and Transmission Networks

Colak,

Abouyehia,

Ahmed

2024

Energies

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A novel energy management system featuring a unique framework involving multiple hierarchical controllers at the distribution and transmission network levels is proposed. The unique objective function of this energy management system is designed to enhance system inertia during black start and optimise load shedding. The objective function further aims to increase reliance on renewable energy sources, prioritising solar power along with battery and fuel cell technologies. This work delves deeply into the dynamics of multi-area power networks, where some areas possess black start capabilities (BSAs) while others do not (NBSAs). The proposed energy management system specifically explores the complex interplay between these black start capabilities and the hierarchical load restoration order. During grid blackouts, the systems located in BSA areas are tasked with first restoring essential loads in their own regions before extending aid to the adjacent NBSA areas, taking into account factors such as their available reserved power and geographical proximity. This work is extended to analyse complex multi-area power network architectures. This extended analysis provides invaluable insights for enhancing power restoration processes and facilitating the large-scale integration of sustainable energy solutions in complex systems. The proposed energy management system is validated using the IEEE 39-Bus network, which consists of ten distinct areas, each differing in their black start capabilities. The results demonstrate the superiority of the proposed system.

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Section: Generic Restoration Action Time (Mins)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Hierarchical Energy Management System for Enhanced Black Start Capabilities at Distribution and Transmission Networks

Colak,

Abouyehia,

Ahmed

2024

Energies

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“…In this context, smart grid technology is a significant consideration. This technology enhances the grid's capacity to promptly adjust and react to fluctuations in wind energy supply, thereby improving the overall dependability and efficiency of the electrical system [11,12]. Another key strategy to tackle the outstanding challenges is the application of highly sophisticated forecasting techniques.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

Habib,

Hossain

2024

Energies

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This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications.

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“…However, it is important to recognize that these technologies are not without challenges. The intermittency of solar energy production and the need for efficient storage to address seasonal and diurnal variations pose technical challenges (Hassan et al, 2023;Fachrizal et al, 2024;Khalid, 2024).…”

Section: Introductionmentioning

confidence: 99%

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

Fekik,

Hamida,

Azar

et al. 2024

Front. Energy Res.

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This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future.

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Smart grids and renewable energy systems: Perspectives and grid integration challenges

Cited by 30 publications

References 190 publications

Novel Hierarchical Energy Management System for Enhanced Black Start Capabilities at Distribution and Transmission Networks

Novel Hierarchical Energy Management System for Enhanced Black Start Capabilities at Distribution and Transmission Networks

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

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