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小样本下风光储耦合系统的新能源消纳能力概率评估方法

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针对风光消纳面临的小样本不确定性问题,基于数据驱动混沌多项式展开法,提出小样本下风光储耦合系统的新能源消纳概率评估方法.首先,考虑风光储耦合系统的运行约束,提出以弃电率最小为目标的弃电率评估模型;随后,计及风光出力历史数据缺乏、难以采用传统不确定性优化方法处理问题,提出基于数据驱动混沌多项式展开法的弃电率概率评估算法.利用风光出力的多阶矩构建任意混沌多项式,基于多维高斯积分求解展开项系数;最后,根据展开系数解析计算弃电率的高阶矩信息,并根据最大熵法求解概率分布.仿真结果表明,所提方法相比于蒙特卡洛模拟法计算效率显著提升.
Probabilistic Evaluation Method for Renewable Energy Integration Capability for Wind-Photovoltaic-Storage Coupling System with Small Sample
Targeting the problem of small sample uncertainty faced by wind and photovoltaic power integration,the paper proposes a probabilistic evaluation method for renewable energy integration to solve the small sample uncertainty based on the data-driven polynomial chaos expansion method.Firstly,an evaluation model of power rejection rate with the goal of minimizing the power rejection rate is proposed considering the operation constraints for the wind-photovoltaic-storage system.Then focusing on the lack of historical wind and photovoltaic power data,it is difficult to use the traditional uncertainty optimization method to deal with the problem,so a data-driven chaotic polynomial expansion method is proposed for the probability evaluation of the power rejection rate.An arbitrary chaotic polynomial is constructed by using the multi-order moment of wind-photovoltaic output,and the expansion coefficients are solved based on multidimensional Gaussian integral.Finally,the higher-order moment information of the power rejection rate is calculated according to the expansion coefficient analysis,and the probability distribution is solved according to the maximum entropy method.The simulation results show that the proposed method is more efficient than the Monte Carlo simulation method.

wind-photovoltaic-storage coupling systemoptimal schedulingoptimal operationwind-photovoltaic power integrationpolynomial chaos expansions

于雷、姚俊伟、杨金龙

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国网湖北省电力有限公司直流公司,湖北宜昌 443000

国网湖北省电力有限公司宜昌供电公司,湖北宜昌 443000

三峡大学电气与新能源学院,湖北宜昌 443000

风光储耦合系统 优化调度 优化运行 风光消纳 多项式混沌展开

国家自然科学基金资助项目

52107108

2024

智慧电力
陕西省电力公司

智慧电力

CSTPCD北大核心
影响因子:0.831
ISSN:1673-7598
年,卷(期):2024.52(10)
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