Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings

Amini, Fariba; Zadeh, Saeid Ghassem; Rostami, Naghi; Tabar, Vahid Sohrabi

doi:10.1049/rpg2.12705

Cited by 14 publications

(4 citation statements)

References 185 publications

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“…Electric grid operators globally face a persistent challenge posed by natural, nonnatural, predictable, and unpredictable factors. These elements can compromise the flexibility and reliability of the grid [8]. Incidents like extreme weather conditions, geodesic events, wildfires, acts of war, and cyberattacks constitute genuine threats to the electric grid's reliability.…”

Section: Related Workmentioning

confidence: 99%

Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model

Aoun,

Adda,

Ilinca

et al. 2024

Energies

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Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid’s resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid’s resilience, this study employs an extrapolation of Leontief’s input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids.

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Section: Related Workmentioning

confidence: 99%

Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model

Aoun,

Adda,

Ilinca

et al. 2024

Energies

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“…The resilient energy planning aims to create a robust energy system that withstands shock events, such as climate events and variable energy supply (Amini, 2023) . The reliable objective of the proposed framework is rooted in several aspects.…”

Section: Reliable Energy Systemmentioning

confidence: 99%

Powering the Future: An Integrated Framework for Clean Renewable Energy Transition

Wehbi

2024

Preprint

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Transition to renewable energy has been recognized as a crucial step to addressing climate change and achieving greenhouse gas reduction targets, but it can also cause energy sprawl if not planned properly. Clean renewable energy communities (CREC) are emerging globally as an approach for decentralized energy systems and an alternative to traditional centralized energy systems. CREC aims to lower the energy carbon footprint, enhance local energy resilience, and improve the quality of life of residents. Through a comprehensive literature review, this study reviews metrics that can assess the impact of energy transition plans and support decision-making to select technologies that create efficient, reliable, and accessible energy systems. It classifies these metrics into a five-dimensional sustainability approach: environmental, technical, social, economic, political and institutional. The paper proposes a conceptual framework to guide decision-makers in recognizing the role of sustainable land development, sustainable energy planning, and resiliency as an integrated approach to energy transition planning. This framework stresses mapping the place-based potential for clean renewable energy at various scales, highlights the importance of resilience in energy planning, and addresses challenges associated with energy source selection, built environment efficiency, and energy trade. While the framework can serve as a starting point for evaluating energy transition plans, further work is needed to address the limitations of existing metrics and identify additional evaluations for energy mixed land use that are critical to managing energy sprawl on ecosystem services and other land uses.

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“…Integrating electrical grids and buildings is guided by the study of energy efficiency. The regions and context are different for each study, thus highlighting the need to follow policies and regulatory frameworks [97]. With global warm- ing becoming more severe year after year, it is paramount to prioritize resilient systems.…”

Section: F Future Directions and Research Opportunitiesmentioning

confidence: 99%

Assessing the Feasibility of Integrating Renewable Energy: Decision Tree Analysis for Parameter Evaluation and LSTM Forecasting for Solar and Wind Power Generation in a Campus Microgrid

Fadoul,

Hassan,

Çağlar

2023

IEEE Access

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The world has embarked on a road to sustainable energy production. As a result, countries have turned to microgrid developments. This article aims to study the feasibility of renewable sources such as solar PV and wind power for integrating a microgrid campus, taking the example of a case in East Africa, precisely the case of the University of Djibouti. We applied the weather parameters to evaluate the solar and wind potential with the Decision Tree method for analyzing and classifying the degrees of solar radiation and the consistency of wind speed. These data are spread over eight years to establish and capture seasonal changes and prove the accessibility of renewable sources in a specific site. The results were compared to Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes classifiers, which showed that the performance of classifying the Decision tree outperformed all other methods with an accuracy of 0.99321. The second work of this article explored the forecasting of the possible powers predicted with the LSTM deep learning method by the generation of the Solar PV array and wind turbines which were simulated on PVLib and Windpowerlib. The results are favorable, and the LSTM has performed well on the different hyperparameters. With the combination of machine learning and deep learning, it was possible to theoretically conclude the integration of renewable energies since we investigated all the potential possibilities in evaluating meteorological parameters and power predictions. Finally, decision scores from the Decision Tree architecture and the LSTM features were integrated to form a hybrid Tree-LSTM fusion method. It introduces a novel architectural concept that can effectively address sequential data and harness the non-linear capabilities of decision trees. The proposed model was validated by tuning the hyperparameters. Enhancing the maximum depth of the model increases the performance at a certain point, and conversely, reducing the minimum sample split improves the model performance. These contributions will help to create sustainable energy systems and increase the transition to a clean CO2 environment.

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Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings

Cited by 14 publications

References 185 publications

Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model

Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model

Powering the Future: An Integrated Framework for Clean Renewable Energy Transition

Assessing the Feasibility of Integrating Renewable Energy: Decision Tree Analysis for Parameter Evaluation and LSTM Forecasting for Solar and Wind Power Generation in a Campus Microgrid

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