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Simulation and Multi-Objective Optimization of a Solar Micro CCHP Using Intelligent Techniques

Younesi, Ali | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54499 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Boroushaki, Mehrdad
  7. Abstract:
  8. Today, due to the scarcity of fossil energy resources, security of energy supply, and increasing environmental concerns, we need to develop new technologies to promote energy-saving and reduce greenhouse gas emissions. One of the suitable options for this purpose is to use the simultaneous production of electric power, cooling, and heating. Meanwhile, trigeneration systems that provide part of their energy needs from the sun, due to the free solar energy source and low environmental impact, can be an ideal technology for clean and safe scattered production. The present study has suggested a trigeneration system of cooling, heating, and power generation based on the organic Rankin cycle and ejector refrigeration subsystem. This system uses the sun energy to provide part of its energy to produce cooling, heating, and power. It works in two cooling and power generation modes in summer and heating and power generation in winter. In the following, by describing the studied cycle, the hourly simulation of the solar subsystem in TRNSYS software and the organic Rankin cycle subsystem in ASPEN HYSYS software for a full day in summer and a full day in winter are discussed. Then, according to the challenges in these systems, the multi-objective particle swarm optimization (MOPSO) algorithm, considering the inlet pressure to the first turbine, the outlet pressure of the first turbine, and the outlet pressure of the second turbine as optimization constraints, to increase Thermal efficiency and reduction of system product cost rate have been exploited. Matlab software has been used to optimize the MOPSO algorithm. The optimization results for all hours showed that the average increase in heat efficiency for the whole day in summer and winter was 0.39 and 1.03%, respectively. Also, the average reduction in product cost for the entire day in summer and winter was 0.47 and 2.16 percent, respectively. Finally, the results of system performance in the initial and optimal state are compared with each other
  9. Keywords:
  10. Organic Rankine Cycle (ORC) ; TRNSYS Software ; Multiobjective Partial Swarm Optimization (MOPSO) ; Combine Cooling Heating and Power (CCHP) ; Multiobjective Optimization ; Solar Energy

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