A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch Considering Wind Power Penetration
A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch Considering Wind Power Penetration
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DOI：
10.1109/tste.2020.3015353
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发表时间：
2021-01-01
影响因子：
8.8
通讯作者：
Valinejad, Jaber
中科院分区：
文献类型：
--
作者：
Hu, Zhixiong;Xu, Yijun;Valinejad, Jaber
随着可再生能源在电力系统中的广泛应用，电力系统经济调度问题由传统的确定性经济调度问题转化为随机性经济调度问题。为了估计随机经济调度问题中的不确定性以进行预测，传统的蒙特-卡罗（MC）方法在实际应用中非常耗时。为了克服这个问题，我们提出了一种新的高斯过程仿真器（GPE）为基础的方法来量化的不确定性，在SED考虑风电渗透。针对风力发电过程中存在的高维不确定性数据，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含概率结构。在这个低维的潜在空间中，拉丁超立方抽样（LHS）的计算机实验设计，GPE是第一次使用，作为一个非参数，代理模型的原始复杂的SED模型。这种降阶的代表，使我们能够评估的经济调度求解器在采样值与一个可以忽略不计的计算成本，同时保持理想的精度。IEEE 118节点测试系统的仿真结果表明，所提出的方法的令人印象深刻的性能。
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.