Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Pu, Tao; Wang, Xinying; Cao, Yifan; Liu, Zhicheng; Qiu, Chao; Qiao, Junfei; Zhang, Shuhua

doi:10.1186/s13677-021-00259-1

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…This layer assumes more elaborate data processing tasks, which require considerable computational resources on the part of edge devices. This layer implements sophisticated algorithms in order to analyze the observance of power usage from resources in the cloud and at the edge, including the detection of usage patterns, trends, and anomalies that could indicate inefficiencies or security threats [13]. The cloud layer also delivers mainstream security services, e.g., advanced encryption policies (which are applied to data in flight and data at rest) and global, machine learning (ML)powered anomaly detection systems based on networkwide data, which in turn can be scrutinized in detail with the use of the cloud's computational muscle to handle incident response.…”

Section: Cloud Layermentioning

confidence: 99%

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Huang,

Sun,

Jiang

et al. 2024

EAI Endorsed Trans Energy Web

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Accurate power consumption assessment is of critical importance in the fast-evolving world of cloud and edge computing. These technologies enable rapid data processing and storage but they also require huge amounts of energy. This energy requirement directly impacts operational costs, as well as environmental responsibility. We are conducting research to develop a specialized cloud-edge power measurement and security model. This model delivers reliable power usage data from these systems while maintaining security for the data they process and store. A combination of simulation-based analysis and real-world experimentation helped us to deliver these results. Monte Carlo based simulations produced power usage predictions under various conditions and Load Testing validated their real-world performance. A Threat Modeling-based security study identified potential vulnerabilities and suggested protection protocols. A collaborative approach enhances power measurements accuracy and encourages secure operation of the combined cloud-edge systems. By fusing these metrics, a more efficient and secure operation of computing resources becomes possible. This research underscores the critical importance of developing advanced techniques for power metering and security in cloud-edge computing systems. Future research may focus on both expanding the model’s use to an array of larger, more complex networks, as well as the inclusion of AI driven predictive analytics to amplify accuracy of power management.

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Section: Cloud Layermentioning

confidence: 99%

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Huang,

Sun,

Jiang

et al. 2024

EAI Endorsed Trans Energy Web

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“…Nonetheless, it has many flaws, including heterogeneous environments, security and privacy concerns, and limited bandwidth resources [100]. In this context, edge computing shifts the frontier of computation applications away from centralized nodes and toward the communication network's outskirts [101], [102]. Edge computing puts computing resources closer to end users and sensors to do data analytics for smart grid decisions.…”

Section: F Edge Computing or On-device Aimentioning

confidence: 99%

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

Mustafa,

Khan,

Aslam

et al. 2023

IEEE Access

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Electricity load and price data pose formidable challenges for forecasting due to their intricate characteristics, marked by high volatility and non-linearity. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for effectively predicting data exhibiting high volatility, frequent fluctuations, mean-reversion tendencies, and non-stationary behavior. Therefore, this review article is dedicated to providing a comprehensive exploration of the application of machine learning and deep learning techniques in the context of electricity load and price prediction. In contrast to existing literature, our study distinguishes itself in several key ways. We systematically examine ML and DL approaches employed for the prediction of electricity load and price, offering a meticulous analysis of their methodologies and performance. Furthermore, we furnish readers with a detailed compendium of the datasets utilized by these forecasting methods, elucidating the sources and specific characteristics underpinning these datasets. Then, we rigorously conduct a performance comparison across various performance metrics, facilitating a comprehensive assessment of the efficacy of different predictive models. Notably, this comparison is carried out using the same datasets that underlie the diverse methodologies reviewed within this study, ensuring a fair and consistent evaluation. Moreover, we provide an in-depth examination of the diverse performance measures and statistical tools employed in the studies considered, providing valuable insights into the analytical frameworks used to gauge forecasting accuracy and model robustness. Lastly, we devote significant attention to the identification and analysis of prevailing challenges within the realm of electricity load and price prediction. Additionally, we delve into prospective directions for future research, thereby contributing to the advancement of this critical field.

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“…The existing cloud computing paradigm is stubborn to address issues and challenges such as rapid response and local autonomy. To address this challenge, the authors of [5] consider a power control framework combining edge computing and reinforcement learning, which makes full use of edge nodes to sense network state and control power equipment to achieve the goal of fast response and local autonomy. Additionally, the authors focus on the non-convergence problem of power flow calculation, and combine deep reinforcement learning and multi-agent methods to realize intelligent decisions, with designing the model such as state, action, and reward.…”

Section: Author Abstractmentioning

confidence: 99%

A Brief Analysis of the 2017-2021 IEEE Energy Internet and Energy Systems Integration Conference Topics

Chigarev¹

2022

Preprint

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This article is devoted to a bibliometric analysis of IEEE Energy Internet and Energy Systems Integration Conference proceedings posted in IEEE Xplore in 2017-2021. The main objective of the study was to identify the actual research issues on the Energy Internet topic. Author Keywords, INSPEC Controlled Terms, and terms compiled from n-grams derived from conference papers title and abstract texts were used to describe the actual issues. The terms were clustered using VOSviewer, and the terms included in each cluster were treated as a description of the actual issues. Brief descriptions of several publications relevant to the issue in question were given for task detail. The actual issue, which in my opinion deserves further, more detailed analysis, is proposed.

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Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Cited by 17 publications

References 27 publications

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

A Brief Analysis of the 2017-2021 IEEE Energy Internet and Energy Systems Integration Conference Topics

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