Optimizing solar photovoltaic farm-based cogeneration systems with artificial intelligence (AI) and Cascade compressed air energy storage for stable power generation and peak shaving: A Japan-focused case study

被引：6
作者：
Assareh, Ehsanolah [1 ,2 ,3 ]
Keykhah, Abolfazl [1 ]
Bedakhanian, Ali [4 ]
Agarwal, Neha [2 ]
Lee, Moonyong [2 ]
机构：
[1] Islamic Azad Univ, Mat & Energy Res Ctr, Dept Renewable Energy Technol, Dezful Branch, Dezful, Iran
[2] Yeungnam Univ, Sch Chem Engn, Gyongsan 38541, South Korea
[3] Victoria Univ, Coll Sport Hlth & Engn CoSHE, Built Environm & Engn Program, Melbourne, Australia
[4] Shahrood Univ Technol, Fac Elect Engn, Shahrood 3619995161, Iran
来源：
APPLIED ENERGY | 2025年 / 377卷
基金：
新加坡国家研究基金会;
关键词：
Solar farm; Photovoltaic panel; Gas turbine; CAES; Artificial intelligence; Multi-objective optimization; HYDROGEN; EXERGY;
D O I：
10.1016/j.apenergy.2024.124468
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This study proposes a novel solar cogeneration system that integrates compressed air energy storage units (CAES) and gas turbines (GT) with a solar farm consisting of photovoltaic panels. The primary objective of this research is to address the instability of solar energy production and help during peak energy consumption by utilizing CAES. The proposed system is modeled using EES software, and its performance is optimized using advanced artificial intelligence (AI) methods, including artificial neural networks and intelligent algorithms. The analysis identifies five critical decision variables that significantly impact system performance: the number of photovoltaic panels, CAES pressure ratio, CAES inlet pressure, gas turbine efficiency, and compressor efficiency. The results demonstrate that the optimized solar cogeneration system can achieve exergy efficiency of 36.44% and a
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