An intelligent hybrid model for power flow optimization in the cloud-IOT electrical distribution network

Zhang, Yantian; Wang, Haoxiang; Xie, Yanzhao

doi:10.1007/s10586-017-1270-0

Cited by 19 publications

(6 citation statements)

References 17 publications

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“…Modelling smart grids is an active area of research [13]. There are various proposals and techniques, which are based on various data, such as the type of the energy production, the power distribution network, the energy storage devices, the power consumption, the learning algorithm for energy costs reduction, the energy usage etc.…”

Section: Smart-grid Modelling and Simulationmentioning

confidence: 99%

“…Furthermore, each method presents a different objective. Examples include Distributed Online Algorithm for Optimal Energy [14,15], Metering and optimal energy distribution [13], Optimal Storage management and Dimensioning [16], Learning algorithms for energy costs reduction and energy usage [17] and other. Here, we present some contributions to this field, based on an extensive survey of the literature.…”

Section: Smart-grid Modelling and Simulationmentioning

confidence: 99%

“…The algorithms have different approaches. In [13] smart homes optimize the consumption of their own devices and share information about their consumption. Another approach is to control an entire power grid [23].…”

Section: Modelling Of Smart-grid Infrastructurementioning

confidence: 99%

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A Survey on IoT-based Smart-Grid Overview

Saffarie¹,

Rahseparfard²

2023

Natural Language Processing, Information Retrieval and AI

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The Internet of Things (IoT) has drawn significant research attention. IoT is considered as a part of the Internet of the future and will comprise billions of intelligent communicating ‘things’. The future of the Internet will consist of heterogeneously connected devices that will further extend the borders of the world with physical entities and virtual components. The Internet of Things (IoT) will empower the connected things with new capabilities. The change of power sector is also guided by the growing penetration of renewable and Distributed Energy Resources (DER), as well as the increasing involvement of electricity consumers in the production and management of electricity, which in turn are expected to radically change the local electricity industry and markets, especially at distribution level, creating opportunities but also posing challenges to the reliability and efficiency of system operation. The trend is inline to the smart-grid concept, which represents an unprecedented opportunity to move the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. During the transition period, it is critical to carry out testing, technology improvements, consumer education, development of standards and regulations, and information sharing between projects to ensure that the benefits we envision from the smart grid become a reality.

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Section: Smart-grid Modelling and Simulationmentioning

confidence: 99%

Section: Smart-grid Modelling and Simulationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on IoT-based Smart-Grid Overview

Saffarie¹,

Rahseparfard²

2023

Natural Language Processing, Information Retrieval and AI

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“…The scenario is roughly that WebKit needs to collect the local multimedia stream and transmit it to the other party through the connection. The local area can choose whether to play the video via “video” and also need to receive the multimedia stream transmitted from the other party 16,17 . Next is the WebKit implementation class that satisfies the functional requirements of RTCPeerConnection.…”

Section: Coal Industry Green and Low‐carbon Development Mechanism And...mentioning

confidence: 99%

Mechanism and policy of multimedia communications model and green low‐carbon development mechanism in coal industry with cloud Internet of Things

Wang

Jia

2020

Int J Communication

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Summary With the rapid development of China's economy, the extensive mode of growth supported by coal has caused great damage to resources and the environment. Problems such as gas drainage, coal gangue dumping, water resources destruction, and surface subsidence during the coal mining process have directly led to severe damage to the environment in the mining area to ensure the survival and development of the coal industry, and to achieve sustainable development, we must actively promote the “optimized layout, intensive development, green mining, safe production, and clean use.” This paper proposes the multimedia communications model and green low‐carbon development mechanism in coal industry with cloud Internet of Things (IoT) to deal with this challenge. In a distributed computing scenario, the communication bandwidth between different nodes is very limited and heterogeneous, and some different computing nodes support a wide range of computing capabilities. Decentralized computing connects devices with computing power to the network, and the nodes communicate directly or indirectly will deal with this challenge effectively. The multimedia transmission is also applied to guarantee the visualized model. The experiment compared with the other state‐of‐the‐art methodologies has proven that the proposed model has the higher transmission efficiency and robustness.

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“…Private, Public, Community, and Hybrid Cloud), previous research has pointed out that IoT devices can benefit from the virtually unlimited resources of the cloud, which compensates for their limitations in storage and computing capabilities ( [11]- [13]). As a result, most of the architectures used to monitor ( [14], [15]), optimize [16], and analyze [17] IoT data streams have been developed based on cloud computing.…”

Section: ) Cloud Computingmentioning

confidence: 99%

Analytics Everywhere: Generating Insights From the Internet of Things

2019

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The Internet of Things is expected to generate an unprecedented number of unbounded data streams that will produce a paradigm shift when it comes to data analytics. We are moving away from performing analytics in a public or private cloud to performing analytics locally at the fog and edge resources. In this paper, we propose a network of tasks utilizing edge, fog, and cloud computing that are designed to support an Analytics Everywhere framework. The aim is to integrate a variety of computational resources and analytical capabilities according to a data life-cycle. We demonstrate the proposed framework using an application in smart transit. INDEX TERMS Descriptive analytics, diagnostic analytics, predictive analytics, edge computing, fog computing, cloud computing, Internet of Things.

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An intelligent hybrid model for power flow optimization in the cloud-IOT electrical distribution network

Cited by 19 publications

References 17 publications

A Survey on IoT-based Smart-Grid Overview

A Survey on IoT-based Smart-Grid Overview

Mechanism and policy of multimedia communications model and green low‐carbon development mechanism in coal industry with cloud Internet of Things

Analytics Everywhere: Generating Insights From the Internet of Things

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