Particle Swarm optimization Algorithm for Power Scheduling Problem Using Smart Battery

Makhadmeh, Sharif Naser; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Naim, Syibrah; Alyasseri, Zaid Abdi Alkareem; Abasi, Ammar Kamal

doi:10.1109/jeeit.2019.8717468

Cited by 36 publications

(19 citation statements)

References 10 publications

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“…It starts from a random solution, finds the optimal solution by iteration, and evaluates the quality of the solution according to fitness. Since it was proposed, PSO has been widely used in many fields, such as computer science, engineering, biology, and economics [26]- [42]. The PSO algorithm has many merits.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Parametric Reconstruction Method for the Long Time-Series Return-Stroke Current of Triggered Lightning Based on the Particle Swarm Optimization Algorithm

et al. 2020

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The lightning research group of the Chinese Academy of Meteorological Sciences has carried out observations and experiments with artificially triggered lightning for more than ten years, and it accumulated thousands of examples of channel base current data on the return stroke of artificially triggered lightning prior to 2020. Based on the current data, this paper explores ways of improving the constructed function of a long time-series (400) return stroke and the parametric reconstruction of the current waveform. The long time-series current can be divided into three components: the breakdown pulse current, corona current and quasi-uniform current. The corona current and quasi-uniform current are constructed by a Heidler function, while the breakdown pulse current is determined by the waveform characteristics of the current peak, which can be divided into the Heidler type and high-order exponential type. According to the three-component model of the return-stroke current, a two-step parametric reconstruction method for the long time-series lightning return-stroke current based on the particle swarm optimization (PSO) algorithm is proposed. In this paper, the results of the parameterized reconstruction of 14 return strokes are given, and the results of the multidimensional error analysis of 13 long time-series return strokes are given to illustrate the accuracy of the improved parameterized model of the return-stroke current and the effectiveness of the two-step reconstruction method based on the PSO algorithm.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Parametric Reconstruction Method for the Long Time-Series Return-Stroke Current of Triggered Lightning Based on the Particle Swarm Optimization Algorithm

et al. 2020

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“…The meta-heuristic optimization algorithms are widely used in addressing PSPSH due to their efficiency in exploring search spaces to find an optimal/near-optimal solution [40]. The most popular algorithms adapted for PSPSH are genetic algorithm (GA) [15], bacterial foraging optimization algorithm (BFOA) [41], harmony search algorithm (HSA) [42], particle swarm optimization (PSO) [43], and grey wolf optimizer (GWO) [12,44].…”

Section: Introductionmentioning

confidence: 99%

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

et al. 2021

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The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

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“…Particle-Swarm Optimization (PSO) algorithm is one of the most popular metaheuristic algorithms [6], This algorithm was first proposed by Eberhart and Kennedy in 1995 [7]. Compared with other algorithms, the PSO algorithm has high calculation speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm Based on a Three-Dimensional Convex Hull for Fitting a Screw Thread Central Axis

et al. 2021

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Particle Swarm optimization Algorithm for Power Scheduling Problem Using Smart Battery

Cited by 36 publications

References 10 publications

Parametric Reconstruction Method for the Long Time-Series Return-Stroke Current of Triggered Lightning Based on the Particle Swarm Optimization Algorithm

Parametric Reconstruction Method for the Long Time-Series Return-Stroke Current of Triggered Lightning Based on the Particle Swarm Optimization Algorithm

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

Improved Particle Swarm Optimization Algorithm Based on a Three-Dimensional Convex Hull for Fitting a Screw Thread Central Axis

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