基于数据驱动鲁棒优化的用户侧综合能源舱低碳规划

在“双碳”目标下,实现多能互补利用的综合能源系统规划研究势在必行,而装卸、配置灵活的用户侧综合能源舱成为了新兴的重要研究对象。构建了引入多元混合储能、碳捕集装置的两阶段综合能源舱规划模型,达到了舱内多能灵活互补、碳排放回收利用的效果。为处理能源舱规划环节中的风电、光伏新能源出力和用户负荷不确定性问题,提出了基于极端场景椭球集的数据驱动鲁棒优化方法,对不确定变量间的相关性进行精确描述,改善了传统鲁棒优化结果过于保守的问题,并通过相较传统分布鲁棒概率计算方法而言更简便的椭球端点提取方法以得到极端场景。利用椭球极端场景优势,改进了列与约束生成法(column and constraint generation method,CCG)求解方法的步骤,避免了复杂子问题对偶处理。最后通过算例仿真,与传统区间不确定集鲁棒优化方法进行对比,证明所提规划方法在降低经济成本与节能低碳方面的优越性。

Research on Low Carbon Planning Based on Data Driven Robust Optimization for User-Side Integrated Energy Module

Under the dual carbon target, it is imperative to develop integrated energy system planning and research, and the flexible loading, unloading and configuration of the user-side integrated energy module is one of the emerging research objects. In this paper, a two-stage integrated energy planning model is established with the introduction of multiple hybrid energy storages and carbon capture devices, which achieves the complementary use of energy and carbon emission recovery and utilization in the system. To deal with new energy output such as photovoltaic and wind power, and user load uncertainty, extreme scenario is proposed. According to ellipsoid set data driven method of robust optimization in uncertainty, the paper accurately describes the correlation between variables and improves the conservation of traditional robust optimization result. Compared with the traditional method with mass probability calculation, a simpler ellipsoid endpoint extraction method is used to obtain extreme scenes. In this paper, the steps of column and constraint generation (CCG) solution are improved by using the advantage of ellipsoidal extreme scenarios to avoid the complicated duality processing of sub-problems. Finally, by example simulation and comparison with the traditional interval uncertain set robust optimization method, it is proved that method proposed in this paper has advantages in reducing economic cost, energy saving and reducing carbon emissions.