Capacity optimization of hybrid renewable energy system considering part-load ratio and resource endowment

Abstract

To help decarbonize the energy sector, a hybrid renewable energy system consisting of wind, solar, and biogas-fueled combined cooling, heating, and power components was investigated. In such systems, thermal and electrical energy are inextricably linked, the local resource endowment is critical, and the main components can be run at part-load. Taken together, these factors make it difficult to optimize a hybrid renewable energy system. To address this issue, we propose a novel method which combines orthogonal design and intelligent algorithms to optimize the capacity of each unit within the system. The optimization results show that the initial investment cost and the dynamic payback period can be reduced by 24.8% and 23.5% compared with optimal orthogonal capacity, and the emission reduction rate increases by 2.3%. In addition, by considering part-load operation and the local resource endowment, it is possible to achieve a 52.2% increase in comprehensive value of the whole system and a 40.4% reduction in investment cost as compared to a simplified model. Importantly, a regional analysis justifies that the proposed evaluation method provides a versatile assessment tool for system optimization.