• 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 ›› 2022, Vol. 43 ›› Issue (10): 87-97.doi: 10.12204/j.issn.1000-7229.2022.10.009

• Smart Grid • Previous Articles     Next Articles

Scheduling Strategy of Multi-stage Stochastic Programming Considering Non-anticipativity of Scheduling Decision

ZHAO Xuenan1(), DUAN Kaiyue1(), LI Meng2(), WANG Xiang2(), LEI Xia2(), ZHONG Hongming2(), HAN Yuhui1(), CHEN Ying1(), LIU Mengcong1()   

  1. 1. State Grid East Inner Mongolia Electric Power Company Limited, Hohhot 010010, China
    2. School of Electric Engineering and Electronic Information, Xihua University, Chengdu 610039, China
  • Received:2022-02-22 Online:2022-10-01 Published:2022-09-29
  • Contact: LEI Xia E-mail:zhaoxuenanhd@126.com;duankaiyue98@163.com;576212534@qq.com;xiangwang@xhu.mail.edu.cn;snow_lei246@mail.xhu.edu.cn;383182742@qq.com;hanyuhui0470@163.com;604439278@qq.com;liumengcong@md.sgcc.com.cn
  • Supported by:
    National Natural Science Foundation of China(51877181);State Grid Corporation of China Research Program(52660021000Q)


With the increase of renewable energy penetration, the volatility and intermittence brought by distributed renewable energy will be transferred to the main network, which will affect the safe operation of the system. The study of uncertainty optimization method plays a certain guiding role in the actual operation of the system. However, the traditional stochastic optimization and robust optimization methods do not meet the unpredictable requirements of the actual operation of the system. In this paper, a multi-stage stochastic programming model considering the uncertainty of distributed renewable power generation is established with the goal of minimizing the expected cost of daily operation. The pre-scheduling decision can be made at each stage according to the realization of the previous uncertain information, which will not be affected by the future uncertain information, and is in line with the actual operation law of the system, and meets the unexpected requirements. In order to avoid the disaster of dimensionality in solving multi-stage stochastic programming problems, the stochastic dual dynamic programming algorithm is used to solve the problem. The simulation results show that, compared with the traditional deterministic model, the optimal scheduling decision tree obtained by multi-stage stochastic optimization has wider decision space than the single decision scheme obtained by deterministic optimization. The scheduling decision can be updated according to the implementation and decision of the uncertain information in the previous stage, and the operating cost of the system can be reduced.

Key words: uncertain optimization, multi-stage stochastic programming, non-anticipativity, stochastic dual dynamic programming (SDDP) algorithm

CLC Number: