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arXiv 1602.07009eess.SYcs.SY
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数据驱动的实时电力调度以最大化可变可再生能源发电

Data-Driven Real-Time Power Dispatch for Maximizing Variable Renewable Generation

Tsinghua University(清华大学);Argonne National Laboratory(阿贡国家实验室)

Zhigang Li, Feng Qiu, Jianhui Wang

AI总结:

本文提出一种数据驱动的实时调度方法,通过利用不超额限制来最大化可变可再生能源发电利用,采用混合随机和稳健优化方法优化可再生能源单元的不超额限制和传统单元的运行基准点,通过样本平均逼近技术将决策模型建模为混合整数线性规划。

摘要:

传统电力调度方法在容纳大规模可变可再生能源发电(VRG)方面存在困难,导致实际行业中不必要的VRG弃风弃光。最近的可调度区间方法有潜力减少VRG弃风弃光,但由于未利用可再生能源单元的历史调度记录,可调度区间分配不 effective。为弥补这一差距,本文提出了一种新的数据驱动实时调度方法,通过使用不超额限制(DNE)来最大化VRG利用。该方法定义了系统在不妥协可靠性的情况下可容纳的最大发电输出范围。VRG单元的DNE限制和传统单元的运行基准点通过混合随机和稳健优化进行联合优化,并通过样本平均逼近技术利用历史VRG数据将决策模型建模为混合整数线性规划。还提出了一种选择历史数据样本的策略,以在不同预测输出水平下更准确地捕捉VRG不确定性。计算实验显示了所提方法的有效性。

英文摘要:

Traditional power dispatch methods have difficulties in accommodating large-scale variable renewable generation (VRG) and have resulted in unnecessary VRG spillage in the practical industry. The recent dispatchable-interval-based methods have the potential to reduce VRG curtailment, but the dispatchable intervals are not allocated effectively due to the lack of exploiting historical dispatch records of VRG units. To bridge this gap, this paper proposes a novel data-driven real-time dispatch approach to maximize VRG utili-zation by using do-not-exceed (DNE) limits. This approach defines the maximum generation output ranges that the system can ac-commodate without compromising reliability. The DNE limits of VRG units and operating base points of conventional units are co-optimized by hybrid stochastic and robust optimization, and the decision models are formulated as mixed-integer linear programs by the sample average approximation technique exploiting historical VRG data. A strategy for selecting historical data samples is also proposed to capture the VRG uncertainty more accurately under variant prediction output levels. Computational experiments show the effectiveness of the proposed methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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