Implementation of Load Control for Smart Metering in Smart Grids

Recioui, Abdelmadjid; Dekhandji, Fatma Zohra

doi:10.4018/978-1-7998-4027-5.ch006

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…Its main goal is to ensure the right balance between the consumed energy and the supplied one. Nevertheless, the involvement of smart devices leads to enormous data traffic among the different parts of the smart grid causing a delay in the response and processing time [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithm for cloud-fog system based load balancing in smart grids

Saoud

Recioui

2022

Bulletin EEI

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Energy management is among the key components of smart metering. Its role is to balance energy consumption and distribution. Smart devices integration results in a huge data exchange between different parts of the smart grid causing a delay in the response and processing time. To overcome this latency issue, the cloud computing has been proposed. However, cloud computing does not perform well when there are large distances from the cloud to the consumers. Fog computing solves this issue. In this paper, a cloud-fog computing system is presented to achieve an accurate load balancing. The hybridization of whale optimization algorithm with bat algorithm (WOA-BAT) is proposed for load balancing. The model performance is compared to state of art load balancing techniques as throttled, round robin, whale and particle swarm optimization algorithms in terms of processing and the response time. The results reveal that the proposed WOA-BAT has better results in terms of response time than the three algorithms with 4.3% improvement compared to RR and TH. It also outperforms all the algorithms in terms of processing time by at least 22.3%.

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Section: Introductionmentioning

confidence: 99%

Hybrid algorithm for cloud-fog system based load balancing in smart grids

Saoud

Recioui

2022

Bulletin EEI

Self Cite

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“…The principle of time control methods is to search for periods of low consumption and persuade PEV owners to charge during those periods by reducing the price of electricity during those periods [8]. Examples of this method are the ant colony algorithm [9], game theory [10], [11], valley filling [12], [13], genetic algorithm (GA) [14], geneticintelligent scatter search algorithm (GA-ISS) [15], home energy management [16]- [21], and priority scheduling [22]- [28]. However, these algorithms mostly aim to get more energy and low cost but do not take into account the impacts of voltage levels and unbalanced voltage levels in the three-phase system according to the standard, which would affect the efficiency of the power distribution system and does not represent the maximum and minimum achieved PEVs charging and PEV owner satisfaction level.…”

Section: Introductionmentioning

confidence: 99%

Electric Energy Management for Plug-in Electric Vehicles Charging in the Distribution System by a dual cascade scheduling algorithm

Panichtanakom

Chalermyanont

2022

Int. j. electr. comput. eng. syst. (Online)

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This paper presents an algorithm for plug-in electric vehicles (PEVs) charging in the three-phase distribution system for residential houses. It aims to prevent violent voltage level deviation and increasing losses on the three-phase distribution system due to uncontrolled charging and allocate power to each plug-in electric vehicle. The algorithm is comprised of two processes. The first process is power limitation and limited power of load imbalance by if-else rules, while the second process is power allocation to each PEV by the dual cascade scheduling algorithm which is the integration of tasking scheduling algorithms. A 100 kVA distribution transformer and 30 houses are defined in the simulation situation. Also, the available PEVs in single-phase, two-phase, and three-phase systems are assigned for verification of the proposed algorithm. Root-mean-square deviation (RMSD) referred to the satisfaction of PEV owners, total PEVs charged energy, and the average percentage of achieved charging time, as the result indicators. The results show the proposed algorithm can provide good results without rejected PEVs charging. Furthermore, this paper also displays the analysis of voltage level, percentage of voltage unbalance factor, and loss in the distribution system. In the future, coordination with home appliances to gain a high load margin or electric energy cost control will be improved in the proposed algorithm

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“…Additionally, it incorporates fog computing technologies for more precise load balancing. In [ 4 ], the authors conceptualized and constructed an SH prototype for storing SPV energy during power outages. The grid and SPV energy use were processed to compute costs, generate a bill, and identify the overall energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System

Almughram

Sami

Zafar

2022

Sensors

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Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households’ electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution.

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Implementation of Load Control for Smart Metering in Smart Grids

Cited by 4 publications

References 33 publications

Hybrid algorithm for cloud-fog system based load balancing in smart grids

Hybrid algorithm for cloud-fog system based load balancing in smart grids

Electric Energy Management for Plug-in Electric Vehicles Charging in the Distribution System by a dual cascade scheduling algorithm

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System

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