Exergy analysis and particle swarm optimization of clean energy router based on a solar‐thermal‐assisted advanced adiabatic compressed air energy storage system

Chenyixuan Ni; Laijun Chen; Xiaotao Chen; Junyi Zhai; Shengwei Mei; Xiao‐Ping Zhang

doi:10.1049/rpg2.12758

Exergy analysis and particle swarm optimization of clean energy router based on a solar‐thermal‐assisted advanced adiabatic compressed air energy storage system

Chenyixuan Ni, Laijun Chen, Xiaotao Chen^*, Junyi Zhai, Shengwei Mei, Xiao‐Ping Zhang^*

^*Corresponding author for this work

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

62 Downloads (Pure)

Abstract

The clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.

Original language	English
Pages (from-to)	2302-2314
Journal	IET Renewable Power Generation
Volume	17
Issue number	9
Early online date	29 May 2023
DOIs	https://doi.org/10.1049/rpg2.12758
Publication status	Published - 6 Jul 2023

Keywords

optimisation
hybrid renewable energy systems
thermal energy storage

UN SDGs

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

Access to Document

10.1049/rpg2.12758Licence: Creative Commons: Attribution (CC BY)

NiC2023ExergyFinal published version, 1 MBLicence: Creative Commons: Attribution (CC BY)

Multi-scale ANalysis for Facilities for Energy STorage (Manifest)
Ding, Y., Radcliffe, J., Zhang, X. & Li, Y.
Engineering & Physical Science Research Council
30/09/16 → 29/09/21
Project: Research Councils

Cite this

@article{f3a0166fd5ae421ba5de1b9b3029e0e9,

title = "Exergy analysis and particle swarm optimization of clean energy router based on a solar‐thermal‐assisted advanced adiabatic compressed air energy storage system",

abstract = "The clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.",

keywords = "optimisation, hybrid renewable energy systems, thermal energy storage",

author = "Chenyixuan Ni and Laijun Chen and Xiaotao Chen and Junyi Zhai and Shengwei Mei and Xiao‐Ping Zhang",

year = "2023",

month = jul,

day = "6",

doi = "10.1049/rpg2.12758",

language = "English",

volume = "17",

pages = "2302--2314",

journal = "IET Renewable Power Generation",

issn = "1752-1416",

publisher = "Institution of Engineering and Technology",

number = "9",

}

TY - JOUR

T1 - Exergy analysis and particle swarm optimization of clean energy router based on a solar‐thermal‐assisted advanced adiabatic compressed air energy storage system

AU - Ni, Chenyixuan

AU - Chen, Laijun

AU - Chen, Xiaotao

AU - Zhai, Junyi

AU - Mei, Shengwei

AU - Zhang, Xiao‐Ping

PY - 2023/7/6

Y1 - 2023/7/6

N2 - The clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.

AB - The clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.

KW - optimisation

KW - hybrid renewable energy systems

KW - thermal energy storage

U2 - 10.1049/rpg2.12758

DO - 10.1049/rpg2.12758

M3 - Article

SN - 1752-1416

VL - 17

SP - 2302

EP - 2314

JO - IET Renewable Power Generation

JF - IET Renewable Power Generation

IS - 9

ER -

Exergy analysis and particle swarm optimization of clean energy router based on a solar‐thermal‐assisted advanced adiabatic compressed air energy storage system

Abstract

Keywords

UN SDGs

Access to Document

Fingerprint

Projects

Multi-scale ANalysis for Facilities for Energy STorage (Manifest)

Cite this