盛四清, 李亮亮, 刘 梦. 考虑多重不确定因素的含风电场低碳经济调度[J]. 现代电力, 2016, 33(2): 77-83.
引用本文: 盛四清, 李亮亮, 刘 梦. 考虑多重不确定因素的含风电场低碳经济调度[J]. 现代电力, 2016, 33(2): 77-83.
SHENG Siqing, LI Liangliang, LIU Meng. Low carbon Economic Dispatch of Power System Integrated with Wind Farms by Considering Uncertainties Factors[J]. Modern Electric Power, 2016, 33(2): 77-83.
Citation: SHENG Siqing, LI Liangliang, LIU Meng. Low carbon Economic Dispatch of Power System Integrated with Wind Farms by Considering Uncertainties Factors[J]. Modern Electric Power, 2016, 33(2): 77-83.

考虑多重不确定因素的含风电场低碳经济调度

Low carbon Economic Dispatch of Power System Integrated with Wind Farms by Considering Uncertainties Factors

  • 摘要: 碳交易引入含风电的调度系统中,由于碳交易价格的不确定性增加了含风电系统的不确定因素的数量。针对低碳经济调度中的多重不确定因素,根据其不同的特性和对调度系统的不同影响区别对待,将风电预测误差和负荷预测误差表示成模糊变量和随机变量,并用混合机会约束考虑其对调度系统旋转备用的影响,应用多场景技术根据碳交易价格的波动模型建立考虑碳交易价格波动下的低碳调度模型。模型的求解通过机组组合方式的确定和机组出力分配两部分进行,通过场景评价指标得到最优机组组合方式下的最优机组出力。最后通过10机组算例证明了模型的有效性和合理性。

     

    Abstract: Because of the uncertainty of the price of carbon trading, carbon trading is introduced in dispatching system integrated with wind power system, which increases the number of uncertain factors. In view of the multiple uncertainties of low carbon economic dispatching, in this paper, according to their different characteristics and their different influence on the dispatching system, wind power forecast error and load forecast error are expressed as fuzzy variables and random variables, and a hybrid chance constrained condition is used to consider its influence on spinning reserve of dispatching system. According to carbon trading price volatility model, multiple scenarios technology is used to establish low carbon dispatching model by considering the carbon trading price fluctuation. The model is solved through the determining of the unit on/off status and the dispatching of unit power output, and the optimal power output is determined under the optimal unit on/off status by using scenario evaluation index. A ten unit test system is used to verify the rationality and effectiveness of proposed model.

     

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