Optimal management of energy storage systems for peak shaving in a smart grid

Firas M. Makahleh; Ayman Amer; Ahmad A. Manasrah; Hani Attar; Ahmed A.A. Solyman; Mehrdad Ahmadi Kamarposhti; Phatiphat Thounthong

doi:10.32604/cmc.2023.035690

Optimal management of energy storage systems for peak shaving in a smart grid

Firas M. Makahleh, Ayman Amer, Ahmad A. Manasrah, Hani Attar, Ahmed A.A. Solyman, Mehrdad Ahmadi Kamarposhti^*, Phatiphat Thounthong

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

76 Downloads (Pure)

Abstract

In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

Original language	English
Pages (from-to)	3317-3337
Number of pages	21
Journal	Computers, Materials and Continua
Volume	75
Issue number	2
DOIs	https://doi.org/10.32604/cmc.2023.035690
Publication status	Published - 31 Mar 2023
Externally published	Yes

Keywords

Cost
energy storage
particle swarm optimization (PSO)
peak load
smart grid

ASJC Scopus subject areas

Biomaterials
Modelling and Simulation
Mechanics of Materials
Computer Science Applications
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.32604/cmc.2023.035690

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Final published version, 899 KBLicence: CC BY

Cite this

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title = "Optimal management of energy storage systems for peak shaving in a smart grid",

abstract = "In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.",

keywords = "Cost, energy storage, particle swarm optimization (PSO), peak load, smart grid",

author = "Makahleh, {Firas M.} and Ayman Amer and Manasrah, {Ahmad A.} and Hani Attar and Solyman, {Ahmed A.A.} and Kamarposhti, {Mehrdad Ahmadi} and Phatiphat Thounthong",

note = "Funding Information: Funding Statement: This work was supported in part by an International Research Partnership “Electrical Engineering—Thai French Research Center (EE-TFRC)” under the project framework of the Lorraine Universit{\'e} d{\textquoteright}Excellence (LUE) in cooperation between Universit{\'e} de Lorraine and King Mongkut{\textquoteright}s University of Technology North Bangkok and in part by the National Research Council of Thailand (NRCT) under Senior Research Scholar Program under Grant No. N42A640328. Publisher Copyright: {\textcopyright} 2023 Tech Science Press. All rights reserved.",

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doi = "10.32604/cmc.2023.035690",

language = "English",

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T1 - Optimal management of energy storage systems for peak shaving in a smart grid

AU - Makahleh, Firas M.

AU - Amer, Ayman

AU - Manasrah, Ahmad A.

AU - Attar, Hani

AU - Solyman, Ahmed A.A.

AU - Kamarposhti, Mehrdad Ahmadi

AU - Thounthong, Phatiphat

N1 - Funding Information: Funding Statement: This work was supported in part by an International Research Partnership “Electrical Engineering—Thai French Research Center (EE-TFRC)” under the project framework of the Lorraine Université d’Excellence (LUE) in cooperation between Université de Lorraine and King Mongkut’s University of Technology North Bangkok and in part by the National Research Council of Thailand (NRCT) under Senior Research Scholar Program under Grant No. N42A640328. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.

PY - 2023/3/31

Y1 - 2023/3/31

N2 - In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

AB - In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

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KW - energy storage

KW - particle swarm optimization (PSO)

KW - peak load

KW - smart grid

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Optimal management of energy storage systems for peak shaving in a smart grid

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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