IEEE Trans Neural Netw Learn Syst. 2015 Sep;26(9):2123-35. doi: 10.1109/TNNLS.2014.2376696. Epub 2014 Dec 18.
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
可再生能源（如风能和太阳能）在电力系统中的渗透显著增加了智能电网在系统运行、稳定性和可靠性方面的不确定性。在本文中，基于非参数神经网络的预测区间（PIs）被用于预测不确定性量化。与单个级别的 PI 不同，风力发电预测不确定性以一系列 PI 表示。然后，这些 PI 被分解为风力功率的分位数。提出了一种新的情景生成方法来处理风力发电预测不确定性。对于每个小时，对这些分位数点拟合经验累积分布函数（ECDF）。蒙特卡罗模拟方法用于从 ECDF 生成情景。然后将风力发电情景纳入随机安全约束机组组合（SCUC）模型。启发式遗传算法用于解决随机 SCUC 问题。实施了五个确定性和四个随机案例研究，其中包含风力发电的区间预测。一起呈现和讨论这些案例的结果。比较了不同机组组合策略的发电成本、计划和实时经济调度储备。实验结果表明，随机模型比确定性模型更稳健，从而降低了智能电网系统运行的风险。
