Generally, the procedure for Solving Security constrained unit commitment (SCUC) problems within Lagrangian Relaxation framework is partitioned into two stages: one is to obtain feasible SCUC states;the other is to so...Generally, the procedure for Solving Security constrained unit commitment (SCUC) problems within Lagrangian Relaxation framework is partitioned into two stages: one is to obtain feasible SCUC states;the other is to solve the economic dispatch of generation power among all the generating units. The core of the two stages is how to determine the feasibility of SCUC states. The existence of ramp rate constraints and security constraints increases the difficulty of obtaining an analytical necessary and sufficient condition for determining the quasi-feasibility of SCUC states at each scheduling time. However, a numerical necessary and sufficient numerical condition is proposed and proven rigorously based on Benders Decomposition Theorem. Testing numerical example shows the effectiveness and efficiency of the condition.展开更多
大规模安全约束机组组合(security constrained unit commitment,SCUC)问题的混合整数线性规划(mixed integer linear programming,MILP)模型因其高维、非凸的特点导致求解困难,尤其在考虑故障态安全约束后模型规模骤增,MILP算法常遇到...大规模安全约束机组组合(security constrained unit commitment,SCUC)问题的混合整数线性规划(mixed integer linear programming,MILP)模型因其高维、非凸的特点导致求解困难,尤其在考虑故障态安全约束后模型规模骤增,MILP算法常遇到收敛间隙下降瓶颈问题。为满足现货市场出清对SCUC问题求解时间的要求,提出了基于热启动的快速求解方法,从待求模型的一个可行解出发,根据节点边际电价和机组收益分析进行整数变量固定,同时削减无约束力的安全约束,以缩减模型规模,加快收敛进程。仿真结果表明:所提方法能够大幅缩减SCUC模型规模,尤其对于考虑故障态安全约束的大规模SCUC问题,能有效克服收敛间隙下降瓶颈问题,求解效率提高特别显著。展开更多
随着电网规模的持续扩大,市场环境下考虑网络安全约束的机组组合(security-constrained unit commitment,SCUC)模型中的变量和约束显著增加,模型的求解性能变差。当模型规模过大时,会出现现有的商用求解器无法求解的状况,造成大规模模...随着电网规模的持续扩大,市场环境下考虑网络安全约束的机组组合(security-constrained unit commitment,SCUC)模型中的变量和约束显著增加,模型的求解性能变差。当模型规模过大时,会出现现有的商用求解器无法求解的状况,造成大规模模型求解困难的问题。为实现大规模机组组合模型的快速求解,从减少模型约束数量的角度出发,提出了一种基于边界法的线性约束简化方法。通过边界法剔除模型中冗余的线性约束,可以有效降低模型规模,实现模型的快速求解。基于IEEE-39、WECC 179和IEEE-118算例,在市场环境下进行日前SCUC测试。通过对比简化前后的求解时间,表明该方法能够显著提高模型的求解速率。展开更多
文摘Generally, the procedure for Solving Security constrained unit commitment (SCUC) problems within Lagrangian Relaxation framework is partitioned into two stages: one is to obtain feasible SCUC states;the other is to solve the economic dispatch of generation power among all the generating units. The core of the two stages is how to determine the feasibility of SCUC states. The existence of ramp rate constraints and security constraints increases the difficulty of obtaining an analytical necessary and sufficient condition for determining the quasi-feasibility of SCUC states at each scheduling time. However, a numerical necessary and sufficient numerical condition is proposed and proven rigorously based on Benders Decomposition Theorem. Testing numerical example shows the effectiveness and efficiency of the condition.
文摘大规模安全约束机组组合(security constrained unit commitment,SCUC)问题的混合整数线性规划(mixed integer linear programming,MILP)模型因其高维、非凸的特点导致求解困难,尤其在考虑故障态安全约束后模型规模骤增,MILP算法常遇到收敛间隙下降瓶颈问题。为满足现货市场出清对SCUC问题求解时间的要求,提出了基于热启动的快速求解方法,从待求模型的一个可行解出发,根据节点边际电价和机组收益分析进行整数变量固定,同时削减无约束力的安全约束,以缩减模型规模,加快收敛进程。仿真结果表明:所提方法能够大幅缩减SCUC模型规模,尤其对于考虑故障态安全约束的大规模SCUC问题,能有效克服收敛间隙下降瓶颈问题,求解效率提高特别显著。
文摘随着电网规模的持续扩大,市场环境下考虑网络安全约束的机组组合(security-constrained unit commitment,SCUC)模型中的变量和约束显著增加,模型的求解性能变差。当模型规模过大时,会出现现有的商用求解器无法求解的状况,造成大规模模型求解困难的问题。为实现大规模机组组合模型的快速求解,从减少模型约束数量的角度出发,提出了一种基于边界法的线性约束简化方法。通过边界法剔除模型中冗余的线性约束,可以有效降低模型规模,实现模型的快速求解。基于IEEE-39、WECC 179和IEEE-118算例,在市场环境下进行日前SCUC测试。通过对比简化前后的求解时间,表明该方法能够显著提高模型的求解速率。
