AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

Guo, Wanying; Qureshi, Nawab Muhammad Faseeh; Jarwar, Muhammad Aslam; Kim, Jaehyoun; Shin, Dong Ryeol

doi:10.1016/j.seta.2022.102990

Cited by 10 publications

(4 citation statements)

References 137 publications

(79 reference statements)

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“…Generating large volumes of fault data through simulation calculations is possible, but ensuring consistency and accuracy in these simulated data can pose a challenge. It is often necessary for the results of these simulations to align closely with the exact fault outcomes for effective analysis of real-world power grid incidents [78]. The process of fault detection and prediction involves analyzing historical data to identify potential faults in the power system, which is crucial for maintaining the power system's reliability and stability.…”

Section: Fault Detection and Analysismentioning

confidence: 99%

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Zahraoui,

Korõtko,

Rosin

et al. 2024

Sustainability

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This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. Then, the paper delves into the concept of resilience and the role of microgrids in maintaining power stability. The paper reviews various AI techniques and methods, and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control.

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Section: Fault Detection and Analysismentioning

confidence: 99%

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Zahraoui,

Korõtko,

Rosin

et al. 2024

Sustainability

View full text Add to dashboard Cite

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“…It also demonstrates the potential of artificial intelligence to deliver economic benefits in demand forecasting. Guo et al (2023) analyzed that grid frequencies play a central role in grid control because they reflect the power generation and demand balance. The excess power supply can increase frequencies, while shortages lead to lower frequencies.…”

Section: Potential Of Artificial Intelligence-assisted Renewable Ener...mentioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

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“…It analyses real-time data from smart meters and regulators, monitoring the grid performance, identifies anomalies or faults, and optimizes the power flows. AI algorithms enable dynamic grid balancing, where RES generation is matched with demand in real time, reducing reliance on fossil fuel-based backup generation [11]. AI has the potential to revolutionize the energy sector by optimizing energy generation, transmission, distribution and consumption-thus, the entire chain is revitalized by AI.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

Cited by 10 publications

References 137 publications

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Artificial intelligence-based solutions for climate change: a review

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

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