Pigeon Inspired Optimization and Bacterial Foraging Optimization for Home Energy Management

Batool, Saadia; Khalid, Adia; Amjad, Zunaira; Arshad, Hafsa; Aimal, Syeda; Farooqi, Mashab; Javaid, Nadeem

doi:10.1007/978-3-319-69811-3_2

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…After choosing a convenient method for MO-PSPSH and normalizing their fitness values, modeling MO-PSPSH is conducted in this step. Based on the objective functions in Equations (11), (14), (16) and (21), PSPSH is modeled in Equation 29as a MOP.…”

Section: Step 2: Normalize the Objective Functionmentioning

confidence: 99%

“…Several studies have proposed different architectures of HEMS and discussed how it provides benefits in addressing PSPSH and improves the efficiency of the power system of SGs. Most of these studies formulated PSPSH objective function only to reduce EB [17][18][19][20][21]. In contrast, a few of these studies formulated PSPSH as a multi-objective optimization problem (MOP) to reduce EB and improve UC level without considering PAR's effect in the scheduling processes [15].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Step 2: Normalize the Objective Functionmentioning

confidence: 99%

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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“…1 Create an initial population of random bacteria Perform the chemotaxis process by interleaving swims using Equations (22) and (23).…”

Section: Normalized Two-swim Modified Bacterial Foraging Optimizationmentioning

confidence: 99%

“…In the fourth row of Table 1, Saadia and others [23] gained electricity cost reduction up to 40% in a home energy management system (HEMS) with a single home using BFOA and pigeon inspired optimization (PIO). Cost, PAR and waiting time of the appliances were calculated on the bases of a 120 h time slot.…”

Section: Introductionmentioning

confidence: 99%

Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid

et al. 2019

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An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.

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A novel link-based Multi-objective Grey Wolf Optimizer for Appliances Energy Scheduling Problem

et al. 2022

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Pigeon Inspired Optimization and Bacterial Foraging Optimization for Home Energy Management

Cited by 4 publications

References 11 publications

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

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

Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid

A novel link-based Multi-objective Grey Wolf Optimizer for Appliances Energy Scheduling Problem

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