文章摘要
朱嘉远,刘洋,许立雄,等.考虑风电消纳的热电联供型微网日前鲁棒经济调度[J].电力系统自动化. DOI: 10.7500/AEPS20180214007.
ZHU Jiayuan,LIU Yang,XU Lixiong, et al.Day-ahead Economic Dispatch of Microgrid with Combined Heat and Power System considering Wind Power Accommodation Based on the Robust Optimization[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20180214007.
考虑风电消纳的热电联供型微网日前鲁棒经济调度
Day-ahead Economic Dispatch of Microgrid with Combined Heat and Power System considering Wind Power Accommodation Based on the Robust Optimization
DOI:10.7500/AEPS20180214007
关键词: 双层鲁棒优化  预测偏差控制  分层迭代  列约束生成算法  强对偶理论  Big-M法
KeyWords: bi-level robust optimization  predictive deviation control  layered iteration  column and constraint generation algorithm  strong duality theorem  Big-M method
上网日期:2019-01-10
基金项目:
作者单位E-mail
朱嘉远 四川大学电气信息学院 zhujy@stu.scu.edu.cn 
刘洋 四川大学电气信息学院 yang.liu@scu.edu.cn 
许立雄 四川大学电气信息学院 xulixiong@163.com 
蒋卓臻 四川大学电气信息学院 563988776@qq.com 
马晨霄 四川大学电气信息学院 570900926@qq.com 
摘要:
      针对热电联供型微网中的风电不确定性,本文构建双层鲁棒模型从而得到最恶劣风电出力场景下的微网最优日前调度方案。模型考虑了可控机组运行成本、功率交互成本,并将弃置的风电功率以惩罚的形式引入目标以提高微网对风电的消纳能力。鉴于模型内外层之间是相互影响的,本文将原问题分解为日前计划调度主问题以及计及风电出力不确定性的执行调控子问题从而进行求解。在求解过程中,利用线性优化强对偶理论对max-min结构的子问题进行转化,并引入Big-M法将所得对偶模型线性化,然后采用列约束生成算法对主问题和子问题进行交互迭代从而获得最优解。最后通过算例验证了本文所提模型的有效性。
Abstract:
      To address the uncertainty of wind power in the microgrid with the combined heat and power system, a bi-level robust model is presented to obtain the optimal scheduling scheme in the worst-case scenario. The objective function inside the model is designed by considering the costs of controllable generators and electricity purchasing(selling). Additionally to improve the ability of microgrid to accommodate the wind power, the abandoned wind curtailment cost is introduced into the objective function. Considering that the inner and outer layers of model interact with each other, the primal problem is decomposed into the day-ahead scheduling problem and the sub-problem which considers the uncertainty of wind power. In the solving process, the strong duality theorem is employed to transform the sub-problem into an equivalent maximization problem. In addition, the bilinear terms in the dual problem is converted into linear terms by using the Big-M method. Finally, the problem is solved by the column and constraint generation (C CG) algorithm. Experiment results indicate the effectiveness of the presented method.
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