Multi-objective optimization of a gas turbine-based CCHP combined with solar and compressed air energy storage system

2018 ◽  
Vol 164 ◽  
pp. 93-101 ◽  
Author(s):  
Xusheng Wang ◽  
Cheng Yang ◽  
Manman Huang ◽  
Xiaoqian Ma
Keyword(s):  
Energy Storage ◽  
Gas Turbine ◽  
Storage System ◽  
Energy Storage System ◽  
Compressed Air ◽  
Compressed Air Energy Storage ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Multi-Objective Optimization of a Battery-Supercapacitor Hybrid Energy Storage System Based on the Concept of Cyber-Physical System

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1801
Author(s):  
Chenyun Pan ◽  
Shengyu Tao ◽  
Hongtao Fan ◽  
Mengyao Shu ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Cyber Physical System ◽  
Multi Objective Optimization ◽  
Hybrid Energy ◽  
Multi Objective ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Control Scheme

Optimal operation of energy storage systems plays an important role in enhancing their lifetime and efficiency. This paper combines the concepts of the cyber–physical system (CPS) and multi-objective optimization into the control structure of the hybrid energy storage system (HESS). Owing to the time-varying characteristics of HESS, combining real-time data with physical models via CPS can significantly promote the performance of HESS. The multi-objective optimization model designed in this paper can improve the utilization of supercapacitors, reduce energy consumption, and prevent the state of charge (SOC) of HESS from exceeding the limitation. The new control scheme takes the characteristics of the components of HESS into account and is beneficial in reducing battery short-term power cycling and high discharge currents. The rain-flow counting algorithm is applied for battery life prediction to quantify the benefits of the HESS under the control scheme proposed. A much better power-sharing relationship between the supercapacitor and the lithium–ion battery (LiB) can be observed from the SIMULINK results and the case study with our new control scheme. Moreover, compared to the traditional low-pass filter control method, the battery lifetime is quantifiably increased from 3.51 years to 10.20 years while the energy efficiency is improved by 1.56%.

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