新能源的不确定性导致功率预测误差,造成新能源发电商因投标电量存在偏差而产生弃风损失和偏差惩罚.提出基于特征约束的多层感知机(MLP)功率预测算法结合储能双边交易提供电量支撑,降低投标偏差.首先,通过改进网络结构和自适应学习提高MLP隐含层关联度,加强对输入数据非线性规则的表征能力,提高功率预测精度.其次,提出新能源发电商与储能商在进入日前市场之前进行双边交易的模式,进一步降低因功率预测误差引起的偏差惩罚和弃风损失.最后,算例证明基于特征约束的MLP有效提高新能源功率预测精度,并且通过与储能商双边交易,有效提高新能源发电商的总体收益.

The uncertainty of new energy results in power prediction errors, causing new energy producers to bear high wind curtailment losses and deviation penalties due to bidding deviations. To address these issues, this paper proposes a feature-constrained multi-layer perception (MLP) power prediction algorithm, combined with storage-based bilateral transactions, to provide power support and reduce bidding deviations. First, the MLP model is enhanced by improving the relevancy of the hidden layers through adaptive learning, which strengthens its ability to capture nonlinear rules in input data and improves power prediction accuracy. Then, the algorithm allows for bilateral transactions between new energy producers and storage enterprise before entering the day-ahead market, helping mitigate the penalties associated with prediction errors including deviation and curtailment costs. Finally, the case study demonstrates that the feature-constrained MLP effectively improves the power prediction accuracy. Additionally, engaging in bilateral transactions with storage enterprise significantly reduces the costs incurred by new energy producers due to bid deviations.