The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in the stochastic economic dispatch (SED) problem for the purpose of forecasting, the conventional Monte-Carlo (MC) method is prohibitively time-consuming for practical applications. To overcome this problem, we propose a novel Gaussian-process-emulator (GPE)-based approach to quantify the uncertainty in SED considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling (LHS) as the computer experimental design, a GPE is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated SED model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

中文翻译：

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考虑风电渗透的随机经济调度不确定性的贝叶斯方法

以随机过程表示的可再生能源在电力系统中的渗透率不断提高，将传统的确定性经济调度问题转化为随机问题。为了估计随机经济调度（SED）问题中的不确定性以进行预测，常规的蒙特卡洛（MC）方法在实际应用中非常耗时。为了克服这个问题，我们提出了一种基于高斯过程仿真器（GPE）的新颖方法来量化考虑风力渗透的SED中的不确定性。面对代表来自风力发电的相关不确定性的高维现实世界数据，提出了一种基于流形学习的Isomap算法，以有效地表示数据的低维隐藏概率结构。在这个低维的潜在空间中 以拉丁文超立方体采样（LHS）作为计算机实验设计，GPE首次用作原始复杂SED模型的非参数替代模型。这个降序代表使我们能够以可忽略的计算成本在采样值下评估经济调度求解器，同时保持理想的准确性。在IEEE 118总线测试系统上进行的仿真结果显示了该方法的出色性能。