代码构建了两阶段鲁棒优化模型，并用文档中的相对简单的算例，进行benders分解算法的验证，此篇文献是benders分解算法的入门级

这是一个BENDERS 分解的MATLAB 文件

matlab_使用Benders分解法求解机组组合问题

针对联网运行的微电网，对其优化潮流（OPF）问题进行扩展，同时考虑机组组合（UC），建立微电网优化运行模型。针对模型中含有大量混合0／1决策变量和连续运行变量的求解，采用Benders分解方法将变量分离，在无网络约束UC主问题和网络约束OPF子问题之间迭代求解。在改造后的IEEE 13节点系统上进行了算例分析，表明所提方法可以快速可靠地优化微电网系统的运行。

两阶段鲁棒优化CCG列于约束生成和Benders代码，可扩展改编，复现自原论文。文件中附源代码以及论文。使用matlab-yalmip编

求解机组组合问题的广义Benders分解方法，简金宝，全然，提出一种求解机组组合(unit commitment，UC)问题的广义Benders分解方法(generalized Benders decomposition method, GBDM)。首先将UC问题转化为一个混合整�

Benders分解(Games) Benders分解(Games) Benders分解(Games) Benders分解(Games) Benders分解(Games)

The book is organized in five parts. Part I, which includes Chapter 1, provides motivating examples and illustrates how optimization problems with decomposable structure are ubiquitous. Part II describes decomposition theory, algorithms, and procedures. Particularly, Chapter 2 and 3 address solution procedures for linear programming problems with complicating constraints and complicating variables, respectively. Chapter 4 reviews and summarizes VIII Preface duality theory. Chapter 5 describes decomposition techniques appropriate for continuous nonlinear programming problems. Chapter 6 presents decomposition procedures relevant for mixed-integer linear and nonlinear problems. Chapter 7 considers specific decomposition techniques not analyzed in the previous chapters. Part III, which includes Chapter 8, provides a comprehensive treatment of sensitivity analysis. Part IV provides in Chapter 9 some case studies of clear interest for the engineering profession. Part V contains some of the codes in GAMS used throughout the book. Finally, Part VI contains the solutions of the even exercises proposed throughout the book.

Benders分解讲义

为了全面和准确地考虑风电出力的不确定性和消纳能力，并兼顾系统运行的经济性和可靠性，通过在风电不确定区间可优化的鲁棒区间经济调度模型中引入常规机组和储能系统运行状态的离散决策变量，建立风储联合运行的双层鲁棒区间机组组合模型。针对连续变量和离散变量间存在耦合关系，导致计算过程中对偶转换失效而使模型难以求解的问题，提出基于Benders分解算法的两阶段迭代求解策略。仿真分析表明，所提模型在确定风储联合运行方式时，能更全面地考虑风电不确定性及消纳能力对系统运行经济性和可靠性的影响。