The low-efficiency and power imbalance risk have challenged the aging fixed time resolution scheduling, especially when facing largely penetrated renewable energies. Time-adaptive unit commitment (T-UC) is recently advanced to solve the issues. However, existing T-UC methods are subjective open-looped, thus may be still far from optimality. To further improve the T-UC, a performance-driven time-adaptive stochastic UC (T-SUC) based on neural network (NN) is proposed. It firstly leverages k-means++ on multivariate forecasts to settle dispatch resolution for SUC. Then, the SUC performances, involving computing efforts and power imbalance risks (PIRs) at the finest horizon, are encoded by neural network. The analyzing for the NN further allows us to feedback the performances to control dispatch resolution. Numerical studies justify that, compared to recent T-UC rivals, our method reduces over 40% of the PIR on the finest intraday time resolution, with the fastest elapsed time.

中文翻译：

---

基于神经网络的性能驱动时间自适应随机单位承诺


低效率和功率不平衡风险对老化的固定时间分辨率调度提出了挑战，尤其是在面对基本渗透的可再生能源时。最近推进了时间自适应单位承诺 （T-UC） 来解决这些问题。然而，现有的 T-UC 方法是主观的开环的，因此可能仍然远非最优。为了进一步改进 T-UC，该文提出一种基于神经网络 （NN） 的性能驱动时间自适应随机 UC （T-SUC）。它首先在多元预测中利用 k-means++ 来确定 SUC 的调度分辨率。然后，神经网络对 SUC 性能进行编码，包括计算工作和最精细的功率不平衡风险 （PIR）。对 NN 的分析进一步允许我们反馈性能以控制调度分辨率。数值研究表明，与最近的 T-UC 竞争对手相比，我们的方法以最快的日内时间分辨率和最快的运行时间减少了 40% 以上的 PIR。