求解概率动态调度问题的Benders分解算法

A Novel Benders Decomposition Based Algorithm for Solving Probabilistic Dynamic Dispatch Problems

概率动态调度能够协调系统运行的经济性与可靠性,相较于传统确定性方法具有先进性。然而,模型规模庞大、求解困难是该类方法所面临的主要问题。提出了一种基于Benders分解的新算法对概率动态调度的大型线性规划问题进行求解。该算法针对各种运行状态之间的耦合关系,依据分解协调的思想,采用Benders分解技术将原问题分解,形成由正常运行状态下动态经济调度主问题与事故运行状态下运行状态调整子问题构成的迭代求解格式,降低了每次优化计算的求解规模;每次迭代过程中,通过对动态调度解的适应性检验,预先筛除无需调整的事故子问题,明显减少了每次迭代中进行优化计算的子问题的数目。算法提高了问题的求解速度,实现了对较大规模系统的有效求解。通过对某省电网的测试计算,表明了算法的正确性与有效性。

Probabilistic dynamic dispatch（PDD） can coordinate power system operating economy and reliability,and is thus superior to the traditional deterministic dynamic dispatch.However,the large scales and difficulties in calculation associated are the main obstacles for PDD＇s engineering applications.A novel solution method based on Benders decomposition is proposed to solve the large scale linear problem of PDD.According to the coupling between operating states,the proposed method decomposes the original PDD problem into a dynamic economic dispatch master problem（MP） at the normal operating states and a number of post-contingency adjustment sub-problems（SPs） at contingency states based on the Benders decomposition techniques.The solution of the original problem can be realized by the iteration of MP and SPs,and each optimization solution scale is effectively reduced.Moreover,a SP pre-selection technique by checking the adaptability of MP results is also deduced in the context to reduce the number of SPs needed in each iteration.The proposed method can increase the calculation speed and solve relatively large scale power system PDD problems.Case studies on a power grid show the validity and efficiency of the proposed method.