Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Cao, Junwei; Zhang, Wanlu; Xiao, Zeqing; Hu, Hua

doi:10.3390/en12081556

Cited by 30 publications

(8 citation statements)

References 38 publications

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“…The rest of the literature references demonstrate application. The aspects contain petroleum supply chain [10,11,[98][99][100][101][102][103][104][105][106][107], steel-making [108][109][110][111], electric power system [112][113][114][115][116][117][118][119], and wind power [120][121][122][123]. The aforementioned references were mostly searched from Google Scholar with related key words, such as "optimization", "combinatorial optimization problem", "machine learning", "supervised learning", "reinforcement learning", "game theory", "refinery scheduling", "steel-making", "electric power system", "wind power", etc.…”

Section: Copsmentioning

confidence: 99%

“…Compared to the SARL algorithm, the training process can converge more stably. Aiming at transient voltage stability in energy internet (EI), Cao et al [114] proposed a deep RL algorithm based on CNN to improve decision-making optimization to balance the power supply-demand. This algorithm has a more accurate prediction compared to conventional ML algorithms, and it also satisfies the expectation of system stability.…”

Section: Electric Power Systemmentioning

confidence: 99%

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A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

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Section: Copsmentioning

confidence: 99%

Section: Electric Power Systemmentioning

confidence: 99%

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

View full text Add to dashboard Cite

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“…e traditional methods include bootstrap method [63], support vector machine [64], and professional software simulation [65]. Reference [65] chose to build the simulation model of a real urban power grid in the power grid simulation software BPA (Bonneville power administration) to generate the simulation data and verified the optimization algorithm of the EI reactive power decision based on deep reinforcement learning on the simulation data.…”

Section: Application Of Gan In Eimentioning

confidence: 99%

Generative Adversarial Network and Its Application in Energy Internet

Xiao

2022

Mathematical Problems in Engineering

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Energy Internet (EI) can provide consumers with flexible energy-sharing services. Recently, artificial intelligence (AI) technology has been widely used in the field of EI. Generative adversarial network (GAN) is one of the hottest research directions in the field of AI in recent years, and its excellent data generation ability has attracted wide attention. First, this paper introduces the framework, advantages, disadvantages, and improvement of classic GAN. Then, the application of GAN in the field of EI is reviewed. Finally, the paper is summarized, and the possible application of GAN in EI is prospected.

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“…Optimization studies with system stability as the research goal, such as transient voltage stability, are of great significance for maintaining the effective and safe operation of power systems [124]. Among many system control methods, distributed control can give consideration to remote data and minimize the requirements of communication.…”

Section: Distributed Control Based On Artificial Intelligence For System Stabilitymentioning

confidence: 99%

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

Wei

Qin

et al. 2021

IET cyber-phys. syst.

Self Cite

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Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI-based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems. | INTRODUCTION | Energy internetNowadays, the rapid development of human society has led to the massive consumption of fossil energy, forcing mankind to face many challenges such as energy crisis, environmental pollution and global warming. Therefore, people began to pay attention to the production and utilization of renewable energy [1,2]. According to statistics in [3], the annual growth of the world's total wind and solar power generation since 2000 is 22% and 40%, respectively. It is estimated that by 2050, renewable energy will account for 80% of the total power generation in the United States [4]. However, the traditional power grid cannot adapt to the large-scale access of renewable energy due to their disadvantages of intermittence and randomicity [5], which limit the use of clean energy. Taking wind power as an example, China's wind power curtailment in 2016 was as high as 4.97 � 10 10 kWÂůh, accounting for 17% of China's total wind power generation [6].This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Cited by 30 publications

References 38 publications

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

Generative Adversarial Network and Its Application in Energy Internet

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

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