Design, energy, exergy, economy, and environment (4E) analysis, and multi-objective optimization of a novel integrated energy system based on solar and geothermal resources

Abstract

Increasing energy consumption rates and concerns over environmental problems like global warming have led researchers to provide new energy supply solutions, one of which is the use of combined energy systems. Combining conventional fossil energy cycles with renewable cycles is one of the most effective ways to improve their performance and also help harvest green energy. To address this solution, in this paper, a hybrid novel energy system including a gas cycle, a steam cycle, two organic Rankine cycles (ORC), and a renewable cycle based on geothermal and solar energy sources using concentrating photovoltaic thermal panels (CPVT) is proposed. A comprehensive study has been conducted on this system from the perspective of energy, efficiency, economy, and the environment. Different fluids have been investigated for use in ORC subsystems in this study. Additionally, a parametric study has been conducted to determine how the cycle's overall performance is affected. Multi-objective optimization using the Non-dominated Sorting Genetic Algorithm second version (NSGA-II) has been performed based on the objective variables of exergy efficiency and capital costs. In this paper, Engineering Equation Solver (EES) and MATLAB are used for modeling and optimization. The results show that the best fluids to use in ORC are R123 in the first cycle and Ammonia in the second cycle, which energy efficiency, exergy efficiency, exergy destruction rate, and generated power in the base state for the whole cycle are 50.59%, 25.44%, 1537.35 kW and 524.66 kW, respectively. Also, the amount of annual capital cost and the amount of carbon dioxide emission from energy and exergy are 107,034 dollars, 11,672, and 35,401 kg per month, respectively. The optimization results indicate a 0.25% improvement in exergy efficiency and a 500$ annual capital cost reduction at the optimal point.