Thermo-economic optimization of RSORC (regenerative solar organic Rankine cycle) considering hourly analysis

In this paper, a RSORC (regenerative solar organic rankine cycle) is optimized. For this purpose, hourly analysis is considered and evaporator pressure, condenser pressure, refrigerant mass flow rate, number of solar panel (solar collector), storage capacity and regenerator effectiveness are selected as design parameters. Then RPGA (Real Parameter Genetic Algorithm) is used to find the maximum value of a new objective function named the RAB (relative annual benefit). The optimization is separately performed for three working fluids including R123, R245fa and isobutane. The optimization results reveal that the best studied working fluid is isobutane with 258810 $/year as relative annual benefit and follow by R245fa and R123 with 68173 and 64028 $/year as the RAB. The hourly analysis shows that in the optimum situation, a plant with isobutane as a working fluid produces higher electricity in the day hours while no electricity is produced in the night hours. Furthermore, a plant with isobutane needs the higher evaporator pressure, mass flow rate and number of solar panels with the lower condenser pressure, storage tank capacity and regenerator effectiveness compared with R245fa and R123. Finally the sensitivity analysis on simulation time step is performed and results are reported.

[1]  Yiping Dai,et al.  Thermodynamic analysis and optimization of an (organic Rankine cycle) ORC using low grade heat source , 2013 .

[2]  Lourdes García-Rodríguez,et al.  Analysis and optimization of the low-temperature solar organic Rankine cycle (ORC) , 2010 .

[3]  Yiping Dai,et al.  Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm , 2013 .

[4]  Mauro Villarini,et al.  State of Art of Small Scale Solar Powered ORC Systems: A Review of the Different Typologies and Technology Perspectives , 2014 .

[5]  Hassan Hajabdollahi,et al.  Thermo-Economic Optimization of Solar CCHP Using Both Genetic and Particle Swarm Algorithms , 2015 .

[6]  Yiping Dai,et al.  Thermodynamic analysis and optimization of a solar-driven regenerative organic Rankine cycle (ORC) based on flat-plate solar collectors , 2013 .

[7]  Ibrahim Dincer,et al.  Multi-Objective Optimization of Plain Fin-and-Tube Heat Exchanger Using Evolutionary Algorithm , 2011 .

[8]  Lourdes García-Rodríguez,et al.  Design recommendations for solar organic Rankine cycle (ORC)–powered reverse osmosis (RO) desalination , 2012 .

[9]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[10]  Lourdes García-Rodríguez,et al.  Preliminary design of seawater and brackish water reverse osmosis desalination systems driven by low-temperature solar organic Rankine cycles (ORC). , 2010 .

[11]  Daniele Fiaschi,et al.  Thermo-fluid dynamics preliminary design of turbo-expanders for ORC cycles , 2012 .

[12]  Sotirios Karellas,et al.  Investigation of an autonomous hybrid solar thermal ORC–PV RO desalination system. The Chalki island case , 2011 .

[13]  Aleksandra Borsukiewicz-Gozdur.  Experimental investigation of R227ea applied as working fluid in the ORC power plant with hermetic turbogenerator , 2013 .

[14]  Andreas Schuster,et al.  Influence of supercritical ORC parameters on plate heat exchanger design , 2012 .

[15]  Ennio Macchi,et al.  Binary ORC (Organic Rankine Cycles) power plants for the exploitation of medium–low temperature geothermal sources – Part B: Techno-economic optimization , 2014 .

[16]  M. A. Mehrabian,et al.  Predicting the direct, diffuse, and global solar radiation on a horizontal surface and comparing with real data , 2011 .

[17]  S. Sanaye,et al.  Multi-objective optimization of rotary regenerator using genetic algorithm , 2009 .

[18]  Fahad A. Al-Sulaiman,et al.  Greenhouse gas emission and exergy assessments of an integrated organic Rankine cycle with a biomass combustor for combined cooling, heating and power production , 2011 .

[19]  Gequn Shu,et al.  Simulation and thermodynamic analysis of a bottoming Organic Rankine Cycle (ORC) of diesel engine (DE) , 2013 .

[20]  Francesco Di Maria,et al.  Electrical energy production from the integrated aerobic-anaerobic treatment of organic waste by ORC , 2014 .

[21]  Lisa Branchini,et al.  Systematic comparison of ORC configurations by means of comprehensive performance indexes , 2013 .

[22]  A. Borsukiewicz-Gozdur,et al.  ORC power plant for electricity production from forest and agriculture biomass , 2014 .

[23]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[24]  Guoquan Qiu,et al.  Selection of working fluids for micro-CHP systems with ORC , 2012 .

[25]  Kangyao Deng,et al.  Energy and exergy analyses of a bottoming Rankine cycle for engine exhaust heat recovery , 2013 .

[26]  Hassan Hajabdollahi,et al.  Thermo-economic environmental optimization of Organic Rankine Cycle for diesel waste heat recovery , 2013 .

[27]  Frank P. Incropera,et al.  Fundamentals of Heat and Mass Transfer , 1981 .

[28]  Fahad A. Al-Sulaiman,et al.  Exergy analysis of parabolic trough solar collectors integrated with combined steam and organic Rankine cycles , 2014 .

[29]  Hassan Hajabdollahi,et al.  Thermo-economic and environmental optimization of solar assisted heat pump by using multi-objective particle swam algorithm , 2014 .

[30]  Onder Kaska,et al.  Energy and exergy analysis of an organic Rankine for power generation from waste heat recovery in steel industry , 2014 .

[31]  Fahad A. Al-Sulaiman,et al.  Energy and exergy analyses of a biomass trigeneration system using an organic Rankine cycle , 2012 .

[32]  Hassan Hajabdollahi,et al.  Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN , 2012, Appl. Soft Comput..

[33]  Fahad A. Al-Sulaiman,et al.  Exergy modeling of a new solar driven trigeneration system , 2011 .

[34]  Ibrahim Dincer,et al.  Thermoeconomic optimization of a shell and tube condenser using both genetic algorithm and particle swarm , 2011 .