The capacity optimization and techno-economic analysis of stand-alone hybrid renewable energy systems based on a two-stage nested optimization approach

For a stand-alone renewable energy system, the configuration with an appropriate energy storage system can effectively cope with the power output volatility of renewable sources such as solar and wind energy, and ultimately improve the power supply reliability. In this paper, in order to optimize the capacity of stand-alone hybrid renewable energy systems (HRESs) respectively coupled with battery (BAT), hydrogen energy storage system (HESS) and thermal energy storage system (TESS), a two-stage nested optimization approach is proposed by combining multi-objective optimizer and single-objective optimizer. In the first stage, the multi-objective optimizer is employed for the capacity optimization of HRESs with different topologies by considering the economic cost and power supply reliability (i.e., the levelized cost of energy (LCOE) and loss of load supply probability (LPSP)) simultaneously, and the optimal results are obtained by using Multiple Criteria Decision-making method. In the second stage, while taking LCOE as the only objective function and LPSP in the best optimal results obtained in the first stage as the constraint condition, the single-objective optimizer is employed to further optimize the capacity of various HRESs. The case study shows that when the HRESs are used to supply the annual load demand of 86.27 MWh, the optimal capacity configurations obtained by the proposed two-stage nested optimization approach are 76 kW PV, 162.87 kWh BAT with LPSP=0.0984 and LCOE=0.3228 $/kWh for the PV/WT/BAT hybrid system; 159.2 kW PV, 58 kW EL, 23 kW FC, 788 kWh HST with LPSP=0.053 and LCOE=0.7675 $/kWh for the PV/WT/HESS hybrid system; 177.6 kW PV, 73 kW EH, 52 kW PB, 806 kWh TEST with LPSP=0.0161 and LCOE=0.5594 $/kWh for the PV/WT/TESS hybrid system, respectively. Meanwhile, compared to the results obtained by using multi-objective optimization alone, the LCOE decreases by 0.0001 $/kWh, 0.0031 $/kWh, and 0.0417 $/kWh, respectively, without depressing the reliability. Furthermore, identical conclusions can be obtained from the sensitivity analysis for different solar irradiance, load profiles and equipment prices, which demonstrates the effectiveness and superiority of the proposed approach.