Optimal design of a solar-hybrid cogeneration cycle using Cuckoo Search algorithm

In this paper optimum design of solar-hybrid cogeneration cycle based on the Cuckoo Search (CS) algorithm is presented. The CS is one of the recently developed population based algorithms inspired by the behavior of some cuckoo species together via the Levy flight behavior of some birds and fruit flies. Moreover, solar power tower technology is practical for utilization in conventional fossil fired power cycles, in part because it can achieve temperatures as high as 1000 degrees C. An exergoeconomic optimization is reported here of a solar-hybrid cogeneration cycle. Modifications are applied to the well-known the prescribed simple cogeneration (CGAM) problem through hybridization by appropriate heliostat field design around the power tower to meet the plant's annual demand. The hybrid cycle is optimized utilizing a CS and compared with the results of the Genetic Algorithm (GA) in Matlab toolbox. Considering exergy efficiency and product cost as objective functions, and principal variables as decision variables, the optimum point is determined. The corresponding optimum decision variables are set as inputs of the system and the technical results are a 48% reduction in fuel consumption which leads to a corresponding decrease in CO2 emissions and a considerable decrease in chemical exergy destruction as the main source of irreversibility. In the analyses, the net power generated is fixed at 30 MW with a marginal deviation in order to compare the results with the conventional cycle. Despite the technical advantages of this scheme, the total product cost rises significantly (by about 87%), which is an expected economic outcome. Effective time-saving procedure using simple parallel computing, as well as utilizing reliable analysis and design tool are also some main features of the present study. The results show that the proposed method appears effective in optimizing thermodynamic cycles. (C) 2016 Elsevier Ltd. All rights reserved.