Thermo-economic analysis for determination of optimized connection between solar field and combined cycle power plant

被引:14
|
作者
Nabati, Amit Masoud [1 ]
Sadeghi, Moharnad Sadegh [2 ]
Naserabad, Sadegh Nikbakht [3 ]
Mokhtari, Hamid [4 ]
Izadpanah, Sobhan [5 ]
机构
[1] Shahrood Univ Technol, Fac Mech Engn, Shahroud, Semnan Province, Iran
[2] Iran Univ Sci & Technol, Sci, Tehran, Iran
[3] Semnan Univ, Fac Mech Engn, Semnan, Iran
[4] Pars Water & Energy Recovery & Optimizat Syst Dev, Tehran, Iran
[5] Islamic Azad Univ, Fac Mech Engn, Cent Tehran Branch, Tehran, Iran
关键词
ISCC; HRSG design; Solar field design; Power; Economic analysis; DIRECT STEAM-GENERATION; GAS-TURBINE; EXERGY ANALYSIS; OPTIMUM DESIGN; PRESSURE HRSG; SYSTEM ISCCS; 4E ANALYSIS; PERFORMANCE; TECHNOLOGIES; WATER;
D O I
10.1016/j.energy.2018.08.047
中图分类号
O414.1 [热力学];
学科分类号
摘要
One of the best procedures to increase output power and decrease greenhouse gases in power plants is integrated solar combined cycle (ISCC). Two main parts of ISCC which have a great impact on cost and net output power of the cycle are 1) solar field and 2) heat recovery steam generator (HRSG), These two parts are optimized in this paper using the genetic algorithm (GA). As a case study, Components of Damavand power plant have been mathematically modeled using thermodynamic equations. After validation the best procedure to connect the solar field and combined cycle is investigated and two scenarios are considered clue to the fact that the case study powerplant has HRSG with two pressure levels. The first scenario is connection of the solar field to the high pressure drum of HRSG and the second scenario is connection of solar field to low pressure drum. After analyzing, the results demonstrate that the second scenario increases the power and reduces the costs. After selection of the best scenario, the design parameters of HRSG and solar field are optimized. The result of GA optimization in HRSG is increase in power by 16.8 MW and 1.1 M$/year decrease in costs. Besides, the result of GA optimization is reducing the cost of solar field construction by 5.1 M$. (C) 2018 Published by Elsevier Ltd.
引用
收藏
页码:1062 / 1076
页数:15
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