生物质-太阳能-风能-电网混合能源系统的最优容量配置及技术经济环境评估：印度南部某机构的案例研究

Abstract The rapid development of infrastructure in educational institutions needs the inclusion of on-campus renewable power generation to meet the guidelines of governments and international energy agencies. Several studies have evaluated the utilization of solar and wind energy sources with grid power, with the aim of economically integrating clean energy into small-scale applications. The load patterns of educational institutions differ from those of other applications, necessitating a specialized study to effectively implement the available energy sources. Bio-energy is an attractive option to supply power during low wind or non-sunshine hours. In response, this study comprehensively analyzed the feasibility of implementing a hybrid energy system that incorporates biomass, solar, wind, and grid energy in a higher education institution. The hybrid optimization software tool ’HOMER PRO’ optimized the total system. The average demand and peak load of the institution are 813.86 kWh/day and 128.21 kW, respectively. Among eight configurations identified in this study, a hybrid system with 387 kW solar photovoltaic, 20 kW wind turbine, 4 kW biomass gasifier-operated generator, and grid utility support has 95 % renewable energy penetration. The optimized system has a levelized cost of energy of INR 3.36 (US$ 0.040)/kWh. The specific cost of electricity from solar, wind, and biomass systems is identified as 3.27 (US$ 0.039), 1.64 (US$ 0.02), and 8.01 (US$ 0.096) INR/kWh, respectively. The system can sell 3,66 MWh annually with a significant return on investment. The facility requires 6000 and 10000 m 2 of area for establishing solar and wind farms, respectively. The reduction in grid power utilization reduced the total system’s annual CO 2 emissions to 21 tons. Furthermore, a sensitivity analysis assessed the impact of resource availability and cost variations on the net present cost and cost of electricity. The implementation of the proposed system in other locations requires modifications in the input parameter in accordance with the resource data and load profiles.