新建微电网缺少历史运行数据，常规数据驱动的方法难以精确预测可再生能源出力，进而影响调度计划制定的准确性。本文提出一种适用于新建微电网小样本数据场景下的微电网优化调度方法。首先设计了融合域对抗神经网络和长短期记忆网络的改进网络结构，将域对抗思想和梯度反转机制引入到迁移学习中，提高模型泛化能力，减小数据的域分布差异，使用出力特征相似电站的丰富运行数据对目标电站出力进行预测，克服了小样本条件下出力预测精度不高的问题。进一步，将优化调度模型转化为马尔可夫决策过程，使用双延迟深度确定性策略梯度算法求解。最后以改进CIGRE14节点微电网为例验证了所提方法的有效性。

Newly built microgrids lack historical operation data, and conventional data-driven methods are difficult to accurately predict renewable power output, which in turn affects the accuracy of scheduling plans. In this paper, a microgrid optimal scheduling method for newly built microgrids with small sample data scenarios is proposed. Firstly, an improved network structure named DANN-LSTM integrating domain adversarial neural network (DANN) and long-short-term memory network (LSTM) is designed, and the domain adversarial idea and gradient inversion mechanism are introduced into the migration learning to improve the generalization ability of the model, reduce the difference in the domain distribution of the data, and use the rich operating data of power stations with similar output characteristics to predict the output of the target station, which overcomes the problem of poor accuracy of the output prediction under the conditions of small samples. The problem of low accuracy of output prediction under small sample conditions is overcome. Further, the optimal scheduling model is transformed into a Markov decision process, which is solved using the double-delay deep deterministic policy gradient algorithm (TD3). Finally, the effectiveness of the proposed method is verified with an example of an improved CIGRE 14- node microgrid.