• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

Electric Power Construction ›› 2020, Vol. 41 ›› Issue (4): 100-108.doi: 10.3969/j.issn.1000-7229.2020.04.012

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A Two-Stage Stochastic Programming Method for Multi-Energy Microgrid System Considering the Uncertainty of New Energy and Load

WANG Liyan1, XU Qiang1, HUANG Kaiyi2, WU Jian1, AI Qian2, ZHANG Yaxin1, JIA Xuan1   

  1. 1. State Grid Liaocheng Power Supply Company, Liaocheng 252000, Shandong Province, China; 2. Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University),  Shanghai 200240, China
  • Online:2020-04-01
  • Supported by:
    This work is supported by Science and Technology Project of State Grid Shandong Electric Power Company(No.52061118005Y).

Abstract: Aiming at the uncertainty of new energy and load, the quantile gradient boosting regression tree (QGBRT) combined with chance constrained programming method based on quantile-confidence interval prediction is proposed, and a two-stage stochastic scheduling optimization model based on energy hub is established. In the form of confidence interval, the method can reflect the adjustable margin of the unit commitment from the statistical level, and provide reliable reference for intraday and real-time dispatching. In the QGBRT prediction method, pinball function describing uncertainty is used as the loss function, and prediction results are used as the constraint information of chance constrained programming. At the same time, in order to make full use of the constantly updated information and relieve the pressure of intraday scheduling plan, in the first stage of the day, according to the published information, the start and stop states of all devices and trading energy state in the microgrid are determined. In the second stage of the day, according to the updated information, the equipment outputs on the basis of the first stage are adjusted and optimized to achieve the accurate and optimal scheduling, which is applicable to actual scheduling. Finally, a typical residential area with multi-energy system is taken as an example to verify the effectiveness and economy of the two-stage stochastic programming method.

Key words: quantile gradient boosting regression tree(QGBRT), chance constrained programming, uncertainty, energy hub, integrated energy system, microgrid

CLC Number: