Multi-objective optimization of a solar-hybrid cogeneration cycle: Application to CGAM problem

With increasing energy costs generally and oil prices in particular, and the global drive to reduce carbon emissions, renewable energy is considered by many as one way to address the economic and environmental issues associated with fossil fuel consumption. 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 °C. An exergoeconomic multi-objective optimization is reported here of a solar-hybrid cogeneration cycle. Modifications are applied to the well-known CGAM problem through hybridization by appropriate heliostat field design around the power tower to meet the plant's annual demand. The new cycle is optimized via a multi-objective genetic algorithm in Matlab optimization toolbox. Considering exergy efficiency and product cost as objective functions, and principal variables as decision variables, the optimum point is determined according to Pareto frontier graphs. 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.