Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

Shi, Zhongtuo; Yao, Wei; Li, Zhouping; Zeng, Lingkang; Zhao, Yifan; Zhang, Runfeng; Tang, Yong; Wen, Jinyu

doi:10.1016/j.apenergy.2020.115733

Cited by 169 publications

(38 citation statements)

References 136 publications

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“…This not only leads to increased costs, but also limits the size of the data being processed. Therefore, adaptive algorithms and AI-based coordination mechanisms are needed to achieve flexibility and distributed data management [80][81][82].…”

Section: From Traditional Methods To Artificial Intelligencementioning

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.

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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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Section: From Traditional Methods To Artificial Intelligencementioning

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.

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“…Considering the need of tackling challenges posed by the intermittence and variability of renewable power generation on the one hand (Qadir et al, 2021 ) and the increasing complexity of the diverse renewable energy sources as the main players in the electric networks with energy storages on the other (F. Ahmad, Almuayqil, et al, 2021 ; Ahmad, Zhang, et al, 2021 ; Ahmad et al, 2019 ; T. Ahmad, Almuayqil, et al, 2021 ; Ahmad, Zhang, et al, 2021 ; Dreglea et al, 2021 ; Kalair et al, 2021 ; Lee & He, 2021 ), the crucial contribution of digitisation and AI to the R&D of the smart grid, the backbone of the SD of our decarbonised, 100% renewable energy future, is evident and paramount. At the same time, case studies also identified challenges the current grid with some smart grid components faces, such as insufficient data to meet high requirements on data, imbalanced learning, interpretability of AI, difficulties in transfer learning, the robustness of AI to communication quality, and the robustness against attack or adversarial events (Aly, 2020 ; Kempitiya et al, 2020 ; Schappert & von Hauff, 2020 ; Shi et al, 2020 ).…”

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

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Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.

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“…µ 11 , µ 12 , µ 13 , µ 21 , µ 22 , and µ 23 are weighting coefficients. Using the artificial intelligence optimization algorithm changes the weighting coefficient to select the optimal performance combination, including system stability, frequency adjustment accuracy, and low energy consumption of the control system [26].…”

Section: Design Of Robust Controller For Lfc In Mgmentioning

confidence: 99%

Optimized Robust Controller Design Based on CPSOGSA Optimization Algorithm andH₂/H_∞Weights Distribution Method for Load Frequency Control of Micro-Grid

Zou¹,

Qian

Zeng

et al. 2021

IEEE Access

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In this paper, a mixed H 2 /H ∞ robust control strategy that considers the weights of the H 2 and H ∞ norm in the optimization process is proposed, and it is used to solve the load frequency control (LFC) problem of the micro-grid (MG). The MG load frequency model established in this paper includes battery energy storage system (BESS), fuel cell (FC), wind turbine (WT), photo-voltaic (PV), and diesel engine generator (DEG). The optimal mixed H 2 /H ∞ robust controller takes the minimum square integral of the system's frequency fluctuation as the goal of control optimization by integrating the robust performance expressed by the H 2 /H ∞ two norms. The hybrid particle swarm optimization and gravitational search algorithm with chaotic map algorithm (CPSOGSA) is used to optimize the weight value reflecting the H 2 and H ∞ performance of the system and the evaluation function's weighting matrix of the output performance so that the controller can reach the optimum under the constraints. Simulation experiments show that the robust controller designed by the proposed method has better dynamic performance when compared with H ∞ robust controller, H 2 robust controller, and traditional H 2 /H ∞ robust controller, and the results are very satisfactory.INDEX TERMS Load-frequency control, Islanded micro-grid, Intelligent H 2 /H ∞ robust control, Intelligent optimization algorithm

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Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

Cited by 169 publications

References 136 publications

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

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

Deploying digitalisation and artificial intelligence in sustainable development research

Optimized Robust Controller Design Based on CPSOGSA Optimization Algorithm andH₂/H_∞Weights Distribution Method for Load Frequency Control of Micro-Grid

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Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

Cited by 169 publications

References 136 publications

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

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

Deploying digitalisation and artificial intelligence in sustainable development research

Optimized Robust Controller Design Based on CPSOGSA Optimization Algorithm andH2/H∞Weights Distribution Method for Load Frequency Control of Micro-Grid

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Product

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Optimized Robust Controller Design Based on CPSOGSA Optimization Algorithm andH₂/H_∞Weights Distribution Method for Load Frequency Control of Micro-Grid