Optimization of geothermal engineering design by combining PB-CCD and multi-objective algorithm

Optimizing the parameters to achieve the best scheme is crucial for promoting the field application of EGS projects. In this paper, a multi-objective simulation-optimization (MOSO) framework for EGS is proposed. Effective electrical power, flow impedance, and levelized cost of electricity (LCOE) are considered as optimization objectives. A numerical model of heterogeneous hot dry rock (HDR) is developed based on spatial frequency to determine the response values of the three objective functions. The Plackett-Burman (PB) method is used to compare the influence of different parameters on the objective function. Five significant engineering parameters are selected as decision variables to establish the objective function. The surrogate model of the objective function is obtained through central composite design (CCD) fitting. Finally, Pareto solutions are obtained by comparing six multi-objective optimization algorithms, and the optimal solution of the nondominated sorting genetic algorithm II with adaptive rotation based simulated binary crossover (NSGA-II-ARSBX) algorithm is selected as the global optimal engineering design through comprehensive evaluation combined with the entropy weight method. The results show that the comprehensive score of the optimal solution is 0.88208. Compared with the original case, the effective power generation has increased by 3.7239 MW, and the LCOE has decreased by 0.007257 $/kWh. •A multi-objective simulation-optimization (MOSO) framework for enhanced geothermal system (EGS) is proposed.•The Plackett-Burman (PB) method was employed to screen five decision variables.•The 2-FI model represents effective power generation, while the Quadratic model represents seepage impedance and LCOE.•Compared to the original case, the effective electrical power has increased, while the LCOE has decreased.