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.
A Fast Solving Method Based on Warm-Start for Large-Scale Security Constrained Unit Commitment Model
The mixed integer linear programming(MILP)model for large-scale security constrained unit commitment(SCUC)problem is difficult to solve because of its high-dimensional and non-convex characteristics,especially when considering post-contingency security constraints,the scale of model increases rapidly,and MILP algorithm often encounters the bottleneck of decreas-ing convergence gap.In order to meet the requirements in spot market clearing process,a fast solving method based on warm-start is proposed to improve the solve speed of SCUC.To reduce the scale of model and speed up the convergence process,the method starts with a feasible solution,integer variables are fixed based on locational marginal price and unit benefit analysis,also the non-binding security constraints are reduced.Simulation results show that the proposed method can greatly reduce the scale of SCUC model.Especially for the large-scale SCUC problem with post-contingency security constraints,the method can effectively overcome the bottleneck of decreasing convergence gap and significantly improve the solution efficiency.
security constrained unit commitmentpost-contingency security constraintsinteger variable fixingconstraint reduc-tionwarm-start
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