论文标题
具有替代模型的多级蒙特卡洛用于资源充足性评估
Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment
论文作者
论文摘要
蒙特卡洛模拟通常用于电力系统的可靠性评估，但是当系统复杂时，它会缓慢收敛。可以将多级蒙特卡洛（MLMC）应用于加快计算的速度，而不会妥协模型的复杂性和准确性，这限制了现实世界的有效性。在MLMC中，组合了具有不同复杂性和速度的模型，并且可以访问快速近似模型对于实现高速加速至关重要。本文展示了机器学习的替代模型如何能够在没有过多的模型手动调整的情况下履行这一角色。讨论了构建和培训代理模型的不同策略。基于具有存储单元的大不列颠系统的资源充足案例研究用于证明拟议方法的有效性以及对替代模型准确性的敏感性。与使用手工构建的型号相比，机器学习的替代物的高精度和推理速度会导致非常大的加速。
Monte Carlo simulation is often used for the reliability assessment of power systems, but it converges slowly when the system is complex. Multilevel Monte Carlo (MLMC) can be applied to speed up computation without compromises on model complexity and accuracy that are limiting real-world effectiveness. In MLMC, models with different complexity and speed are combined, and having access to fast approximate models is essential for achieving high speedups. This paper demonstrates how machine-learned surrogate models are able to fulfil this role without excessive manual tuning of models. Different strategies for constructing and training surrogate models are discussed. A resource adequacy case study based on the Great Britain system with storage units is used to demonstrate the effectiveness of the proposed approach, and the sensitivity to surrogate model accuracy. The high accuracy and inference speed of machine-learned surrogates result in very large speedups, compared to using MLMC with hand-built models.
