摘要
随着大规模光伏接入需求的产生,高渗透率的光伏设备对配电网的安全运行提出了很高的要求,研究准确评估配电网光伏消纳能力是当前学界与业界亟待解决的一个重要问题。光伏出力随着天气的变化存在诸多的不确定性,目前的数学优化与场景模拟方案只是针对配电网的一个总体消纳能力评估。这些方案总体过于保守,并且未能针对特定情况(如气候变化、用电发展等)下的配电网光伏消纳能力进行评估。由此引入了二阶段光伏消纳能力评估方法。首先针对配电网未来负荷不确定性,利用循环神经网络对配电网未来负荷能力进行短期预测;然后根据预测值,针对光伏出力不稳定性的情况,进一步考虑功率转移下的配电网运行稳定性,由此更好地模拟配电网在大规模接入光伏情况下的运行特征。该方案对于实际光伏容量评估与规划具有现实指导意义。最后,以上海市奉贤区某配电系统为算例的模拟分析佐证了该方法的可操作性与准确性。
With the demand of large-scale photovoltaic access, high-permeability photovoltaic equipment has put forward high requirements for the safe operation of distribution network. It is an important issue that needs to be solved urgently in current academia and industry to study and accurately evaluate the photovoltaic absorption capacity of distribution network. There are many uncertainties in photovoltaic output with the change of weather. The current mathematical optimization and scenario simulation scheme is only an overall absorption capacity assessment for the distribution network. These schemes are generally too conservative and fail to evaluate the PV absorption capacity of distribution networks under specific circumstances(e.g., climate change, electricity consumption development). Therefore, a two-stage PV absorption capacity assessment method is introduced. Firstly, according to the uncertainty of future load of distribution network, the cyclic neural network is used to predict the future load capacity of distribution network in short term. According to the predicted value, the stability of distribution network operation under power transfer is further considered in view of the instability of PV output, so as to better simulate the operation characteristics of distribution network under large-scale PV access. This scheme has practical guiding significance for actual PV capacity evaluation and planning. The simulation analysis of a distribution system in Fengxian district of Shanghai also proves the operability and accuracy of the method.
作者
徐呈程
祝燕萍
方欢欢
蒋兴新
赵珞
樊戴福
胡东晓
陈方瑞
XU Chengcheng;ZHU Yanping;FANG Huanhuan;JIANG Xingxin;ZHAO Luo;FAN Daifu;HU Dongxiao;CHEN Fangrui(Fengxian Power Supply Company,State Grid SMEPC,Shanghai 201400,China;School of Intelligent Science&Engineering,Harbin Engineering University,Harbin 15000l,Heilongjiang Province,China)
出处
《电力与能源》
2022年第5期374-379,468,共7页
Power & Energy
关键词
配电网
光伏消纳能力
循环神经网络
发电功率转移因子
distribution network
photovoltaic absorption capacity
recurrent neural network
generation power transfer factor
