近日，深圳大学化学与环境工程学院李倍助理教授团队在期刊《Applied Energy》（影响因子9.746，中科院JCR 1区，TOP期刊）上发表了题为《Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters》的研究论文。该团队李倍助理教授为第一作者。深圳大学为第一单位和第一通信单位，该团队所在的新能源科学工程学科属于广东省优势学科。
近年来，为了减少碳排放，大规模的新能源发电被安装。氢储能系统由于具有高能量密度，将能够有效的平滑新能源出力的波动性。此外，特高压直流输电通常被用来远距离传输电能。然而，当前基于集群氢能微电网和HVDC构建低碳排放电力系统的研究还不足。本研究中，作者提出了基于最大似然估计的概率定容算法用以减少不确定性的影响。首先，构建了氢能微电网和HVDC的模型；其次，构建了混合整数规划MIP优化运行策略；再者，构建新能源电站-HVDC输电-IEEE30电网-微电网集群运行模型（如图1所示）；最后遗传算法GA用以搜寻最佳容量组合。数据的不确定采用k-means样本抽样方法，根据不同的样本可以求出容量值的概率分布PDF。结果表明，所提出的基于最大似然估计的概率定容算法能够减少新能源出力波动的负面影响。整个算法的流程图如图2所示。
图1、新能源电站-HVDC输电-IEEE30电网-微电网集群运行模型
图2、基于最大似然估计的概率定容算法
原文链接：
https://www.sciencedirect.com/science/article/pii/S0306261921011004
李倍助理教授团队长期研究氢能零排放电力系统，能源网络耦合交通网络抵御灾害侵袭等。开发了多能源网络的日前-实时两阶段优化运行技术[3,5,10]。开发了氢储能系统三层优化运行技术[4,6,7]。不同场景容量规划技术[2,8,9,11,12]。然而，针对概率定容方面的研究还不足，故此本文提出了基于最大似然估计的概率定容算法用以减少不确定性的影响[1]。
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