Design, Deployment and Performance Evaluation of an IoT Based Smart Energy Management System for Demand Side Management in Smart Grid

Saleem, Muntaha; Usman, Muhammad Rehan; Usman, Muhammad; Politis, Christos

doi:10.1109/access.2022.3147484

Cited by 69 publications

(31 citation statements)

References 47 publications

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“…4 The smart grid (SG) is one of the essential solutions to the current power grid's many problems. 5 The intelligent system helps manage schedule uncertainties and power transfers between sections. By incorporating RERs, optimizing the transmission and distribution networks' transfer capability, and satisfying demand requirements, the SG presents a method that addresses and manages unforeseen uncertainties in planning more aggressively.…”

Section: Highlightsmentioning

confidence: 99%

Toward an intelligent community microgrid energy management system based on optimal control schemes

Azaroual

Mbungu

Ouassaid

et al. 2022

Intl J of Energy Research

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This article investigates a modelization of the energy management strategy of a grid-tied photovoltaic (PV), wind generator, and battery system. The model uses a smart metering system to collect data from various electrical components of a microgrid. The power optimization strategy is modeled under demand response, including time-of-use pricing to create a home's energy flow more efficiently. Two optimal control strategies are developed to coordinate

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Section: Highlightsmentioning

confidence: 99%

Toward an intelligent community microgrid energy management system based on optimal control schemes

Azaroual

Mbungu

Ouassaid

et al. 2022

Intl J of Energy Research

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“…For instance, the objects within the networks are distributed according to a particular pattern such that; the nodes are concentrated in a particular region in the network or may be distributed uniformly. More precisely, according to [10,11,12], the deployment of the IoT objects may follow a Gaussian distribution or other kinds of distributions. Moreover, the mobile objects may also follow a particular pattern in movements (e.g., levy flight) since they are not static [13].…”

Section: Overviewmentioning

confidence: 99%

On the Consumption of Resources of the Hybrid Objects in the Internet of Things (Iot)

MUNDHER¹,

HADEED²,

Alramli³

2022

MINAR Journal

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The advent of the Internet of Things (IoT) introduces a variety of challenges. One of the most frequent challenges is the consumption of resources (e.g., power and memory). The consumption of resources is considered an important aspect when it comes to the general performance of the system. Therefore, it is important to consider this kind of issue before designing such systems or applications. This research aims to assess the number of resources consumed when having hybrid objects (static and dynamic) in the Internet of Things (IoT). The objects considered in this work can be devices such as sensors, smartphones, or other sensing objects that can be used in exchanging data (resources). The settings of the experiments performed in this work vary including colorful parameters (i.e., object deployment, movement patterns, and routing protocols) and a combination of them. In this work, 4 groups of experiments are designed considering different parameters. The simulations are evaluated in terms of two metrics; the amount of data exchanged and covered areas. These two metrics are used as indicators to measure the consumption of resources. The findings showed that the Gaussian strategy in deploying the static and mobile nodes in the IoT can reduce the consumption of resources (e.g., memory and power) and cover more areas within the simulation environment regardless of the movement pattern and the routing protocols used.

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“…Authors in [ 20 ] examined the SG information management system using the CC scheme, employing distributed information management for real-time information capturing, ubiquitous accessibility, and real-time data retrieval using parallel processing. In [ 21 ], a cloud-driven demand response framework was devised to enable quicker response times in large-scale implementations. There were two kinds of demand response strategies investigated: cloud-driven demand response and dispersed demand response.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization

Alsokhiry

Annuk

Mohamed

et al. 2023

Sensors

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Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE’s processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.

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Design, Deployment and Performance Evaluation of an IoT Based Smart Energy Management System for Demand Side Management in Smart Grid

Cited by 69 publications

References 47 publications

Toward an intelligent community microgrid energy management system based on optimal control schemes

Toward an intelligent community microgrid energy management system based on optimal control schemes

On the Consumption of Resources of the Hybrid Objects in the Internet of Things (Iot)

An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization

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