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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals
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Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals

机译：使用基于神经网络的预测区间将风电功率预测的不确定性纳入随机单位承诺中

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摘要

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.
机译：风能和太阳能等可再生能源渗透到电力系统中，大大增加了智能电网中系统运行，稳定性和可靠性的不确定性。在本文中，基于非参数神经网络的预测间隔（PI）用于预测不确定性量化。在PI列表中表示了风电预测的不确定性，而不是单一级别的PI。然后将这些PI分解为风能的分位数。提出了一种新的情景生成方法来处理风电预测的不确定性。对于每个小时，将经验累积分布函数（ECDF）拟合到这些分位数点。蒙特卡罗模拟方法用于从ECDF生成方案。然后，将风能场景合并到随机安全约束单位承诺（SCUC）模型中。启发式遗传算法被用来解决随机SCUC问题。实施了五个确定性和四个随机案例研究，并结合了风电的间隔预测。这些案例的结果将一起介绍和讨论。比较了发电成本以及不同单位承诺策略的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具有更高的鲁棒性，从而降低了智能电网系统运行的风险。

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