Algorithm based on particle swarm applied to electrical load scheduling in an industrial setting

Lopes, Regina Lúcia Mendonça; Costa, Fabiano F.; Oliveira, Aurenice M.; Lima, Carla Caroline Valença de

doi:10.1016/j.energy.2018.01.090

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…The fogs are connected to one cloud data center. The simulations were performed on Java platform in Netbeans and cloud analyst tool [52] for one day (24h). The parameters settings on cloud analyst tool are illustrated in Table 1.…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

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

Saoud

Recioui

2022

Bulletin EEI

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“…The authors in [34] propose energy-aware fog-based load balancing on equipment in a smart factory. An improved PSO algorithm is proposed for an efficient energy-consumption model.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

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The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud-fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

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“…Therefore, demand response (DR) plays an essential role in balancing the available generation capacity against the demanded energy [9,10]. DR refers to the change in customer consumption profile related to the change in energy price or incentive offers [11]. Meanwhile, the developments in information and communication technologies (ICT) provide smart residential homes that provide optimal control for easier monitoring by connecting all household sensors and appliances through a home area network (HAN) [12,13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods

et al. 2019

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From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions’ set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method.

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Algorithm based on particle swarm applied to electrical load scheduling in an industrial setting

Cited by 9 publications

References 11 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

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods

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