A Secure Distributed Cloud-Fog Based Framework for Economic Operation of Microgrids

Tajalli, Seyede Zahra; Tajalli, Seyed Ali Mohammad; Kavousi‐Fard, Abdollah; Niknam, Taher; Dabbaghjamanesh, Morteza; Mehraeen, Shahab

doi:10.1109/tpec.2019.8662201

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…The main issue for the economic and efficient operation of MGs is the management and optimal scheduling of DGUs main grid, and loads [6]. In [7], the authors suggested a stochastic management framework for the optimal scheduling of HMGs.…”

Section: Bgi Tmentioning

confidence: 99%

Effective Management of Energy Internet in Renewable Hybrid Microgrids: A Secured Data Driven Resilient Architecture

Mohammadi

Kavousi‐Fard

Dabbaghjamanesh

et al. 2022

IEEE Trans. Ind. Inf.

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This paper proposes a two-layer in-depth secured management architecture for the optimal operation of energy internet in hybrid microgrids. In the cyber layer of the proposed architecture, a two-level intrusion detection system (IDS) is proposed to detect various cyber-attacks (i.e. Sybil attacks, spoofing attacks, false data injection attacks) on wireless-based advanced metering infrastructures. The sequential probability ratio testing (SPRT) approach is utilized in both levels of the proposed IDS to detect cyber-attacks based on a sequence of anomalies rather than only one piece of evidence. The process of making a decision in the proposed IDS is a random walk that starts from a point between two thresholds and moves toward one of them concerning received data samples. The feasibility and performance of the proposed architecture are examined on the IEEE 33-bus test system and the results are provided for both islanded and grid-connected operation modes.

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Section: Bgi Tmentioning

confidence: 99%

Effective Management of Energy Internet in Renewable Hybrid Microgrids: A Secured Data Driven Resilient Architecture

Mohammadi

Kavousi‐Fard

Dabbaghjamanesh

et al. 2022

IEEE Trans. Ind. Inf.

Self Cite

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“…Load changes can be captured and managed in a distributed manner using multi-level fog computing. This helps avoid centralized management and enhances the realization of an optimal dispatch of microgrids [53]. In such a model issues like high communication traffic, massive computational burdens in large-scale systems, and the increased complexity of real-time monitoring of microgrids can be avoided.…”

Section: Smart Energy Applicationsmentioning

confidence: 99%

“…Tajalli et al [53] developed a distributed multi-agent-based model structured on a three-layer fog computing structure for the optimal economic dispatch of microgrids. This model actively monitors load changes and the availability of renewable energy to determine the need for dispatching microgrids to supplement the energy efficiently.…”

Section: Optimization With Iifcsmentioning

confidence: 99%

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

et al. 2021

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Several cities have recently moved towards becoming smart cities for better services and quality of life for residents and visitors, with: optimized resource utilization; increased environmental protection; enhanced infrastructure operations and maintenance; and strong safety and security measures. Smart cities depend on deploying current and new technologies and different optimization methods to enhance services and performance in their different sectors. Some of the technologies assisting smart city applications are the Internet of Things (IoT), fog computing, and cloud computing. Integrating these three to serve one system (we will refer to it as integrated IoT-fog-cloud system (iIFC)) creates an advanced platform to develop and operate various types of smart city applications. This platform will allow applications to use the best features from the IoT devices, fog nodes, and cloud services to deliver best capabilities and performance. Utilizing this powerful platform will provide many opportunities for enhancing and optimizing applications in energy, transportation, healthcare, and other areas. In this paper we survey various applications of iIFCs for smart cities. We identify different common issues associated with utilizing iIFCs for smart city applications. These issues arise due to the characteristics of iIFCs on the one side and the requirements of different smart city applications on the other. In addition, we outline the main requirements to effectively utilize iIFCs for smart city applications. These requirements are related to optimization, networking, and security.

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“…This multi-agent approach uses local-only communication to allow high levels of a node churn to implement a distributed processing capability. A tracking of load changes was tackled by [26], in which they proposed a distributed multi-agent based framework, using first-order consensus algorithm, structured on three-layer Fog computing design for effective maximum economic micro-grid dispatch. This system tracks shifts in load at any time of day, taking into account unexpected entries and exits of the units.…”

Section: Related Workmentioning

confidence: 99%

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Mutlag

Ghani

Mohammed

et al. 2020

Sensors

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In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

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A Secure Distributed Cloud-Fog Based Framework for Economic Operation of Microgrids

Cited by 13 publications

References 15 publications

Effective Management of Energy Internet in Renewable Hybrid Microgrids: A Secured Data Driven Resilient Architecture

Effective Management of Energy Internet in Renewable Hybrid Microgrids: A Secured Data Driven Resilient Architecture

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

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