含高比例风电的电力市场电价预测

在解除管制的电力市场中,精确预测电价有助于市场各方有效参与市场运营与管理。清洁能源渗透率的提高,给电价预测精度带来了新的挑战。文中选择不同的输入特征变量并结合长短期记忆(LSTM)网络的特点,构建含高比例风电的电力市场电价预测模型对含有风电的电力市场电价进行预测。研究表明,风能和负荷的比值是含高比例风电的电力市场风电电价预测的关键输入参数。LSTM具备时间延迟记忆特点,拥有较好的电力市场时间序列电价预测能力。以北欧市场中DK1电力市场实际数据为基础,采用3种模型进行对比分析,结果表明含有风能和负荷的比值且考虑多时刻信息输入的LSTM模型可以较大地提高低谷时段的电价预测精度。

Electricity Price Prediction for Electricity Market with High Proportion of Wind Power

In the deregulated electricity market, the accurate forecasting of electricity price is helpful for all parties to participate in the market operation and management. The increase of clean energy penetration rate brings new challenges to the accuracy of electricity price prediction. By choosing different input characteristic variables and utilizing the characteristics of long-short term memory (LSTM) network, the electricity price prediction model for the electricity market with high proportion of wind power is built to predict the electricity price of the electricity market with wind power. The results show that the ratio of wind power to load is the key input parameter of the electricity price prediction in the electricity market with high proportion of wind power. LSTM has the characteristic of time delay memory, so it has better ability to predict the electricity market price in time series. Based on the actual data of DK1 electricity market in the Nordic market, three models are used for the comparative analysis. The results show that the LSTM model with the ratio of wind power to load and considering multi-time information input can greatly improve the prediction accuracy of electricity price during slack time.