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Multi-objective optimization of multi-energy complementary systems integrated biomass-solar-wind energy utilization in rural areas
Energy Conversion and Management ( IF 9.9 ) Pub Date : 2024-11-11 , DOI: 10.1016/j.enconman.2024.119241
Min Chen, Jiayuan Wei, Xianting Yang, Qiang Fu, Qingyu Wang, Sijia Qiao

Rural areas possess abundant renewable energy sources, such as solar and biomass energy; however, the current methods of energy utilization suffer from low efficiency and serious pollution issues. As rural residents’ living standards continue to improve, there is an urgent need to optimize and adjust the structure of rural energy systems. Multi-energy complementary systems (MECS) have the potential to enhance energy utilization efficiency, achieve high efficiency and energy savings, significantly reduce carbon emissions, and effectively address the challenges faced by rural energy development. This study explores a typical framework for rural MECS that integrates photovoltaic, wind turbine, and biomass biogas combined cooling, heating, and power technology while considering the partial load ratio of equipment components and coupling characteristics between different energy sources. Based on various scenarios of valley electricity utilization, multi-objective optimization models are established to determine the capacity of MECS with economy, environment, and primary energy saving rate as objective functions. The non-dominated sorting genetic algorithm (NSGA-II) along with Technique for Order Preference by Similarity to Ideal Solution decision-making method is adopted to obtain optimal solutions from the Pareto solution set. The case study conducted in a rural area of central China has demonstrated the effective enhancement of coupling capacity in MECS through battery storage. By actively storing energy during off-peak electricity periods, battery storage strengthens the complementary capabilities of photovoltaic systems, wind turbines, and itself. This approach allows for a reduction in planned capacity for photovoltaic and wind power systems within MECS while increasing the planned capacity for internal combustion engines, resulting in respective decreases in system investment costs by 16.19% and 13.18%. Furthermore, incorporating more biogas-fired cogeneration during off-peak electricity periods improves the system’s performance economically, environmentally, and with regards to primary energy saving rate.

中文翻译:


农村生物质-光-风能综合利用多能源互补系统的多目标优化



农村地区拥有丰富的可再生能源,如太阳能和生物质能;然而,目前的能源利用方法存在效率低下和污染严重的问题。随着农村居民生活水平的不断提高,亟需优化和调整农村能源系统的结构。多能互补系统 (MECS) 具有提高能源利用效率、实现高效节能、显著减少碳排放的潜力,有效应对农村能源发展面临的挑战。本研究探讨了一种融合光伏、风力涡轮机和生物质沼气冷热电技术的典型框架,同时考虑了设备部件的部分负荷比和不同能源之间的耦合特性。基于谷电利用的各种情景,建立多目标优化模型,以确定以经济、环境和一次节能率为目标函数的MECS容量。采用非支配排序遗传算法 (NSGA-II) 以及通过与理想解相似性排序优先的技术决策方法,从 Pareto 解集中获得最优解。在华中农村地区进行的案例研究表明,通过电池存储可以有效提高 MECS 的耦合能力。通过在非高峰用电时段主动储存能量,电池存储增强了光伏系统、风力涡轮机和自身的互补能力。 这种方法允许减少 MECS 内光伏和风电系统的规划容量,同时增加内燃机的规划容量,从而使系统投资成本分别降低 16.19% 和 13.18%。此外,在非高峰用电时段加入更多的沼气热电联产可以提高系统的经济、环境和一次节能率。
更新日期:2024-11-11
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