黑启动路径优化是电网恢复过程中的关键环节，为解决含新能源电网黑启动路径优化中搜索空间大、计算复杂度高、收敛速度慢等问题，提出了一种基于改进A*算法的含新能源电网黑启动路径优化策略。首先分析了新能源场站输出功率的不确定性，采用BP神经网络对其进行超短期功率预测。其次，以输电线路恢复时间为目标函数，考虑线路投运状态、启动功率等约束构建了黑启动恢复优化模型，在此基础上采用引入权重系数、路径平滑等策略对传统的A*算法进行改进，以此获取含新能源的电网黑启动最优恢复路径。最后，通过MAT/AB仿真算例对比黑启动过程中不同路径优化策略的性能。结果表明，该算法相比其他策略具有更高的恢复效率，并且提高了黑启动过程中的可用有功功率和总发电量，有效保障了供电方案的可靠性和快速性。

Black start path optimization is a key step in the process of power grid restoration. To solve the problems of large search space, high computational complexity, and slow convergence speed in black start path optimization of new energy power grids, an improved A * algorithm based black start path optimization strategy for new energy power grids is proposed. Firstly, the uncertainty of the output power of new energy stations is analyzed, and a BP neural network is used for ultra short term power prediction. Secondly, taking the restoration time of transmission lines as the objective function, a black start restoration optimization model is constructed considering constraints such as line operation status and starting power. Based on this, the traditional A * algorithm is improved by introducing weight coefficients and path smoothing strategies to obtain the optimal restoration path for black start of power grids containing new energy. Finally, a MAT/AB simulation example is used to compare the performance of different path optimization strategies during the black start process. The results show that this algorithm has higher recovery efficiency compared to other strategies, and improves the available active power and total power generation during the black start process, effectively ensuring the reliability and speed of the power supply scheme.