面向虚拟电厂运营的温度敏感负荷分析与演变趋势研判

Analysis and Evolution Trend of Temperature-Sensitive Loads for Virtual Power Plant Operation

随着极端天气频发,温度敏感负荷用电逐年攀升,温度敏感负荷作为虚拟电厂优质的调控资源,亟须分析气象变化对于此类负荷的影响,由于叠加极端高温、大规模寒潮等异常天气的影响,温度敏感负荷波动剧烈,常规分析预测方法难以适应极端气象场景。针对寒潮天气下温度敏感负荷样本数据及预测精度不足的问题,提出寒潮天气小样本条件下的温度敏感负荷日最大负荷预测方法。该方法先采用时序对抗生成网络(TimeGAN)扩充寒潮期间小样本数据,再采用卷积-长短时记忆神经网络(CNN-LSTM)对寒潮期间的日最大负荷进行预测。以国内某省近两年迎峰度冬期间数据进行模型验证,结果表明所提模型优于其他模型的预测结果,在验证集上日最大负荷的预测精度为99.5%。

With the frequent occurrence of extreme weather,the electricity consumption of temperature-sensitive loads is increasing year by year.As a high-quality regulation resource of virtual power plant(VPP),temperature-sensitive loads urgently need to be analyzed for the impact of meteorological changes on them.Due to the influence of abnormal weather such as extreme high temperature and large-scale cold waves,temperature-sensitive loads fluctuate violently.Conventional analysis and prediction methods are not adaptable to the extreme meteorological scenarios.Aiming at the problem of insufficient sample data and prediction accuracy of temperature-sensitive loads under cold wave weather,this paper proposes a daily maximum load prediction method for temperature-sensitive loads under the condition of small sample in cold wave weather.In this method,the TimeGAN is used to expand the small sample data during the cold wave period,and then the CNN-LSTM network is used to predict the daily maximum load during the cold wave period.Finally,the model is verified by the load data of a province in China during the winter period in the past two years.The results show that the prediction results of the proposed model are better than those of other models,with the prediction accuracy of the daily maximum load on the verification set being 99.5%.