A two-stage stochastic scheduling optimization for multi-source power system considering randomness and concentrating solar power plant participation

被引：0

作者：

Yun Y. ^{[1
]}

Dong H. ^{[1
,2
]}

Chen Z. ^{[3
]}

Huang R. ^{[3
]}

Ding K. ^{[3
]}

机构：

[1] School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou

[2] School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou

[3] Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Concentrating solar power plant; Multi-source power system; Randomness; Scenario simulation; Two-stage stochastic scheduling optimization;

D O I：

10.19783/j.cnki.pspc.190380

中图分类号：

学科分类号：

摘要：

In allusion to the problem of integrated scheduling optimization in a variety of new energy power generation, the paper integrates the wind power, photovoltaic power, concentrating solar power and thermal power generation as the Multi-Source Power System (MSPS), and a two-stage stochastic scheduling optimization method based on robust stochastic optimization theory is proposed. First, this paper introduces the basic structure of MSPS and establishes the output power models of MSPS. Then, the scenario generation and reduction frame based on the interval method and the probability distance is proposed for dealing with the randomness of new energy output power. On this basis, a two-stage stochastic scheduling optimization model for MSPS is established. Simultaneously, this paper transforms the constraints containing random variables into the constraints reflecting the decision maker's attitude of bearing systematic risks based on robust stochastic optimization theory, and then establishes MSPS stochastic scheduling optimization model including two robust coefficients. The simulation proves that the model can reduce the risk of power shortage and provide scheduling optimization decision-making foundation for different-risk-attitude system manager. © 2020, Power System Protection and Control Press. All right reserved.

引用

页码：30 / 38

页数：8

共 21 条

[1] Huang G., Xu D., Ding Q., Et al., Review of grid dispatching considering thermal power and large-scale wind power integration, Power System Protection and Control, 46, 15, pp. 162-170, (2018)
[2] Li H., Eseye A.T., Zhang J., Et al., Optimal energy management for industrial microgrids with high-penetration renewables, Protection and Control of Modern Power Systems, 2, 2, pp. 122-135, (2017)
[3] Wang Z., Liu M., Decentralized dynamic economic dispatch of microgrids using distributed simplex method, Power System Protection and Control, 46, 15, pp. 1-8, (2018)
[4] Li S., Dai J., Dong H., Et al., Optimal operation of wind power-photovoltaic-pumped storage joint power generation system considering correlations, Proceedings of the CSU-EPSA, 31, 11, pp. 92-102, (2019)
[5] Cao Y., Wang K., Shi W., Et al., Study on dispatching strategies of a wind-solar-seawater pumped storage hybrid power system, Power System Protection and Control, 46, 2, pp. 16-23, (2018)
[6] Chen R., Sun H., Guo Q., Et al., Reducing generation uncertainty by integrating CSP with wind power: an adaptive robust optimization-based analysis, IEEE Transactions on Sustainable Energy, 6, 2, pp. 583-594, (2015)
[7] Jin H., Sun H., Guo Q., Et al., Multi-day self-scheduling method for combined system of CSP plants and wind power with large-scale thermal energy storage contained, Automation of Electric Power Systems, 40, 11, pp. 17-23, (2016)
[8] Ju L., Tan Z., Yuan J., Et al., A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind-photovoltaic-energy storage system considering the uncertainty and demand response, Applied Energy, 171, pp. 184-199, (2016)
[9] Tan Z., Wang G., Ju L., Et al., Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response, Energy, 124, pp. 198-213, (2017)
[10] Peng C., Xie P., Pan L., Et al., Flexible robust optimization dispatch for hybrid wind/photovoltaic/hydro/thermal power system, IEEE Transactions on Smart Grid, 7, 2, pp. 751-762, (2016)

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