Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Alamir, Nehmedo; Kamel, Salah; Megahed, Tamer F.; Hori, Maiya; Abdelkader, Sobhy M.

doi:10.1016/j.epsr.2022.108905

Cited by 38 publications

(18 citation statements)

References 41 publications

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“…During the initial stage of the exploration phase, pelicans close in on the prey by identifying its position, which is randomly generated. The stochastic nature of the prey's location enhances the exploration capacity of the POA [40]. The mathematical expression of the initial phase is depicted in Eq.…”

Section: Pelican Optimization Algorithm (Poa)mentioning

confidence: 99%

Student Performance Estimation Through Innovative Classification Techniques in Education

Fan,

Zhu,

Zhan

2024

IJACSA

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In the current era of intense educational competition, institutions must effectively classify individuals based on their abilities, proactively forecast student performance, and work towards enhancing their forthcoming examination outcomes. Providing early guidance to students is crucial in helping them focus their efforts on specific areas to boost their academic achievements. This analytical approach supports educational institutions in mitigating failure rates by utilizing students' previous performance in relevant courses to predict their outcomes in a specific program. Data mining encompasses a variety of techniques used to reveal hidden patterns within vast datasets. In the context of educational data mining, these methods are applied within the educational sphere, with a specific emphasis on analyzing data from both students and educators. These patterns can offer significant value for predictive and analytical objectives. In this study, Gaussian Process Classification (GPC) was employed for the prediction of student performance. To improve the model's accuracy, two cutting-edge optimizers, namely the Golden Eagle Optimizer (GEO) and the Pelican Optimization Algorithm (POA), were incorporated. When assessing the model's performance, four widely used metrics were utilized: Accuracy, Precision, Recall, and F1-score. The results of this study underscore the effectiveness of both the POA and GEO optimizers in enhancing GPC performance. Specifically, GPC+GEO demonstrated remarkable effectiveness in the Poor grade, while GPC+POA excelled in the Acceptable and Excellent category. This highlights the positive impact of these optimization techniques on the model's predictive capabilities.

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Section: Pelican Optimization Algorithm (Poa)mentioning

confidence: 99%

Student Performance Estimation Through Innovative Classification Techniques in Education

Fan,

Zhu,

Zhan

2024

IJACSA

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“…This impact is interpreted in terms of the stability of such islanded microgrids which is at stake due to the utilization of intermittent distributed generation sources. This in turn requires the application of battery storage systems for voltage and frequency stability, under varying load conditions [3, 4]. In general, there is a direct relation between the load profile and the social activities of the consumers.…”

Section: Introductionmentioning

confidence: 99%

“…In [1] the dynamic electricity pricing mechanism is proposed using the particle swarm optimization algorithm. A hybrid demand response scheme is introduced in [3] for the optimal operation of the microgrid in terms of cost where maximize the microgrid operator benefit and reduce the overall operation cost.…”

Section: Introductionmentioning

confidence: 99%

“…Microgrid systems with distributed generation have been successful in electrifying far-flung areas [1,2]. The operation of low-voltage microgrids is critical as it has a massive impact on the performance due to limited power backup and abrupt variation in the overall load [2,3]. This impact is interpreted in terms of the stability of such islanded microgrids which is at stake due to the utilization of intermittent distributed generation sources.…”

Section: Introductionmentioning

confidence: 99%

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A novel automated demand response control using fuzzy logic for islanded battery‐operated rural microgrids

Azeem

Memon

Baig

et al. 2023

IET Renewable Power Gen

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Islanded rural microgrid require continuous resource monitoring. Demand response schemes have been phenomenal in managing loads. However, urban demand response schemes are well equipped with market prices and peak time penalties to control deferrable loads. In rural microrids, regular loads such as fans, lights and water pumps are normally used that do not fall under category of deferrable loads. In addition, full liberty of utilizing regular load at any time, lack of awareness and no information of storage reserves make task of load management more complex. In this research fully automated two layered demand response scheme is designed for regular operating loads. The first layer control is load mode control. The mode of operation is decided on the state of charge (SoC) of battery. In second layer, fuzzy controller is designed on the consumer's routines, SoC and ambient temperature as membership function. Results are assessed in terms of consumers comfort and availablity of SoC. The load operation in automated demand response remained indentical to actual rutine operation as per consumer's desire with 5 to 7% deviation. In all modes of operation SoC levels remained 15% higher and heavy load operated 13.5% more compare to relevant study.

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“…Indeed, [17] offers a promising solution to optimize the operation of such systems, potentially leading to enhance energy efficiency in multifarious technological applications. A hybrid energy management in micro grids is proposed in [18] with pelican optimization algorithm (POA). The utilization of POA improves the efficiency and reliability of energy management systems within micro grids, contributing to more sustainable and resilient power distribution.…”

Section: Introductionmentioning

confidence: 99%

Eco‐environmental operation of energy hub considering uncertainties of supply/demand‐side resources with introducing a novel optimization algorithm

Akbarpour,

Abdollahi,

Abdali

et al. 2023

IET Renewable Power Gen

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In this paper, the optimal operation of Energy hub (EH) based upon both demand and renewable energy resources (RESs) uncertainties is investigated. Here, an innovative scenario‐based energy hub energy management system (EH‐EMS) is proposed for simultaneous cost and emission reduction considering the abovementioned uncertainties. Moreover, a multi objective hybrid heuristic algorithm, the so‐called hBES‐MOGWO, is introduced for solving the proposed EH‐EMS, which is a combination of bald eagle search (BES) and multi objective gray wolf optimization (MOGWO) algorithms. The proposed EH consists of photovoltaic (PV), wind turbine (WT), boiler, heat storage (HS),combined heat and power (CHP) and multi‐carrier energy storage technology such as power to gas (PtG) technology, electric vehicles and heat storages. Flexible loads are also taken into account for reducing cost and emission by participation in demand response programs (DRPs). The optimization results demonstrate the effectiveness of the proposed hBES‐MOGWO algorithm. The simulation results illustrated that the total cost of EH‐EMS can be effectively reduced by installing energy storage systems and implementing DRPs. The results emphasize that considering uncertainties can lead to increase the operating costs and emission, while flexible loads participation in DRPs and PtG can reduce the aforementioned ones.

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Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Cited by 38 publications

References 41 publications

Student Performance Estimation Through Innovative Classification Techniques in Education

Student Performance Estimation Through Innovative Classification Techniques in Education

A novel automated demand response control using fuzzy logic for islanded battery‐operated rural microgrids

Eco‐environmental operation of energy hub considering uncertainties of supply/demand‐side resources with introducing a novel optimization algorithm

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