Overall day-ahead scheduling optimization for pumped-storage power stations considering the uncertainty of wind and photovoltaic power prediction

doi:10.3969/j.issn.2097-0706.2022.11.003

Abstract

Abstract:

In order to reduce the impact of uncertain forecasting on renewable energy outputs on the economy of day-ahead optimization scheduling， an overall day-ahead scheduling optimization model for pumped storage power stations considering the uncertainty of wind and photovoltaic power prediction is proposed. Firstly， the wind speed and solar irradiance are predicted based on Weibull distribution and Beta distribution，respectively. The predicted outputs of wind and photovoltaic power are obtained. The multi-scenario stochastic programming is adopted to solve the uncertainty of day-ahead wind and photovoltaic output predictions. Secondly， based on the scenario set of the predicted day-ahead wind and photovoltaic outputs and the load prediction curve， the overall day-ahead dispatching optimization model for the pumped storage power station considering wind， PV and thermal power is established. Taking steady net load and the minimum total peak-shaving cost as objectives， the water pumping and power generation outputs of the storage power station and the output of the thermal power units are obtained. Verified by the data from a province in China，the model can fully smooth the fluctuation of new energy outputs， reduce the peak load regulation pressure of thermal power units， and improve the economy of the system.

Key words: wind power, PV power, pumped storage, output prediction, load prediction, Weibull distribution, Beta distribution, multi-scenario stochastic programming, overall scheduling optimization

CLC Number:

TK01⁺9:TK71

OUYANG Ting, CAI Ye, WANG Weiyu, TANG Xiafei, TAN Yudong. Overall day-ahead scheduling optimization for pumped-storage power stations considering the uncertainty of wind and photovoltaic power prediction[J]. Integrated Intelligent Energy, 2022, 44(11): 20-27.

Figures/Tables 12

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机组类型	机组容量/MW	数量/台	总容量/MW
火电机组	660	18	11 880.0
	330	16	5 280.0
	210	2	420.0
抽水蓄能机组	300	4	1 200.0
风电机组			6 690.9
光伏机组			3 906.7
水电机组			15 806.4

场景	发生概率
场景	风电	光伏
1	0.410	0.190
2	0.120	0.160
3	0.150	0.300
4	0.180	0.120
5	0.140	0.230

指标	不含抽水蓄能	本文方法
净负荷峰谷差/MW	4 825.4	2 425.4
净负荷峰谷差减少率/%		49.75
调峰成本/万元	7 883.0	7 840.1
火电机组开机台数	36	32