摘要
随着可再生能源渗透率增加,净负荷波动幅度也随之变大。因此,该文针对净负荷不确定性变化,结合预测误差相关性与条件风险价值(conditional value at risk,CVaR),提出一种综合能源系统灵活爬坡优化调度方法。首先,通过神经网络模型预测风光荷初始功率,针对预测误差,运用C藤Copula函数构建多元随机变量预测误差的联合概率分布,并据此提出一种灵活爬坡产品设计方法;然后,在考虑净负荷不确定性带来的弃风光和切负荷条件风险价值的基础上,构建考虑灵活爬坡产品风险价值的综合能源系统优化调度模型。仿真结果表明,考虑净负荷预测误差相关性的优化使系统整体经济性提升1.22%,切负荷与弃风光总量减少了17.01%,验证该文方法的有效性。结合不同置信水平下的CVaR值,可为综合能源系统调度提供一定的风险参考。
With the increase of renewable energy penetration,the net load fluctuation becomes larger.Therefore,a flexible ramping optimal scheduling method of integrated energy systems is proposed in this paper for the uncertain change of net load,combining the correlation of prediction error and conditional value at risk(CVaR).First,the initial power of scenic load is predicted by a neural network model,and the joint probability distribution of prediction errors of multivariate random variables is constructed by using the method of the C-vine Copula function for the prediction errors,and a flexible ramping product design method is proposed accordingly.Then,considering the conditional value at risk of scenic abandonment and load cutting brought by net load uncertainty,a flexible ramping product risk value is constructed.Later,an optimal scheduling model of integrated energy system considering the value at risk of flexible ramp climbing product is constructed considering the net load uncertainty.Simulation results show that optimization considering the correlation of load forecasting errors has improved the overall economic efficiency of the system by 1.22%.Both load shedding and curtailed wind power have decreased by 17.01%,validating the effectiveness of the proposed method.The CVaR values at different confidence levels can provide a certain risk reference for the scheduling of integrated energy systems.
作者
黄悦华
刘兴韬
张磊
陈庆
张子豪
涂金童
章轩瑞
HUANG Yuehua;LIU Xingtao;ZHANG Lei;CHEN Qing;ZHANG Zihao;TU Jintong;ZHANG Xuanrui(Electrical Engineering&New Energy College,China Three Gorges University,Yichang 443002,Hubei Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2023年第24期9411-9422,共12页
Proceedings of the CSEE
基金
国家自然科学基金项目(52007103,62233006)。
