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

doi:10.12204/j.issn.1000-7229.2022.10.009

Abstract

Abstract:

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:

TM744

ZHAO Xuenan, DUAN Kaiyue, LI Meng, WANG Xiang, LEI Xia, ZHONG Hongming, HAN Yuhui, CHEN Ying, LIU Mengcong. Scheduling Strategy of Multi-stage Stochastic Programming Considering Non-anticipativity of Scheduling Decision[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(10): 87-97.

Figures/Tables 15

Fig.1 Construction of scenario tree

Fig.2 Forward pass procedure

Fig.3 Backward pass procedure

Fig.4 Expected cost function represented by Benders’ cut set

Fig.5 Predicted curves of load and renewable energy supply

Table 1 Grid parameters

Fig.6 Time-of-use electricity price

Table 2 Effect of scenario-tree size on SDDP algorithm

Table 3 Influence of sample size on SDDP algorithm

Fig.7 Convergence of SDDP algorithm

Table 4 Comparison between multi-stage stochastic programming model and deterministic model

Fig.8 Comparison of renewable energy output

Fig.9 Scheduling plan based on deterministic model

Fig.10 Comparison of interactive power of power grid

Fig.11 Comparison of charge and discharge power of storage battery

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