摘要
光伏发电易受温度、辐照度等环境因素的影响,而近年来雾霾(PM_(2.5)浓度较高)污染严重,大幅降低了光伏系统发电量。因此研究雾霾天气下光伏发电量预测方法对光伏市场的发展具有重要意义。通过采集上海某户用光伏屋顶的全年光伏数据,利用控制变量法及雾霾相似日原理,拟合分析PM_(2.5)的浓度与发电量损失指数之间的关系,通过迭代原理优化光伏发电量预测算法,并给出雾霾环境下光伏发电量预测公式,修正光伏收益预测模型。结果表明:优化后的光伏预测发电量算法可提高发电量预测结果的精确性和稳定性。通过对3种光伏经济模型进行收益分析,验证了迭代优化算法可有效提高光伏收益预测的精确性。
Photovoltaic power generation is vulnerable to environmental factors such as temperature and irradiance.In recent years,haze(with high concentration of PM_(2.5))has caused serious pollution,greatly reducing the power generation of the photovoltaic system.Therefore,it is of great significance for the photovoltaic market to predict the photovoltaic power generation in the haze weather.In this paper,based on the annual photovoltaic data of a household photovoltaic roof in Shanghai,the relationship between the PM_(2.5) concentration and the power generation loss index is fitted and analyzed with controlled variables and the similar days for haze analysis.According to the principle of iteration,the algorithm for photovoltaic power generation prediction is optimized,and the formula for photovoltaic power generation prediction under haze is given to modify the photovoltaic revenue prediction model.The results show that the optimized algorithm can improve the accuracy and stability of the prediction results.Through the revenue analysis of three photovoltaic economic models,the iterative optimization algorithm can improve the accuracy of photovoltaic revenue forecast.
作者
陈炜
任静
武新芳
于文英
刘永生
CHEN Wei;REN Jing;WU Xinfang;YU Wenying;LIU Yongsheng(Institute of Solar Energy,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《中国电力》
CSCD
北大核心
2021年第10期223-230,共8页
Electric Power
基金
国家自然科学基金资助项目(51971128)
上海市优秀学术/技术带头人计划(20XD1401800)
上海市科委项目(19020501000)。
关键词
空气污染
雾霾
PM_(2.5)
光伏发电量预测
迭代优化
光伏收益预测
光伏系统设计
air pollution
haze
PM_(2.5)
photovoltaic power generation prediction
iterative optimization
photovoltaic revenue forecast
photovoltaic system design
作者简介
陈炜(1994—),男,硕士研究生,从事太阳能电池及光伏系统研究,E-mail:156226453@qq.com;通信作者:刘永生(1974—),男,教授,博士生导师,从事太阳能电池及光伏系统研究,E-mail:ysliu@shiep.edu.cn。
