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Dynamic Optimal Power Flow Using Interior Point Method and Benders Decomposition Considering Active and Reactive Constraints

机译:考虑有功和无功约束的使用内点法和Benders分解的动态最优潮流

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This article describes an approach for the dynamic optimal power flow problem using interior point method (IPM) and Benders decomposition considering both active and reactive constraints. An efficient predictor-corrector primal-dual interior-point algorithm is used to solve the linearized dynamic OPF problem. The inclusion of active and reactive security constraints will assure a subsequent feasible solution for the dynamic OPF problem. The dynamic OPF problem is decomposed into a master problem and subproblems for checking the feasibility of the active and reactive constraints. The master problem is formulated and solved without active and reactive constraints to avoid complexity. The obtained dynamic OPF schedule from the master problem is applied to the active and reactive subproblems to minimize the violations. In case the current mix of scheduled units cannot remove the violations, additional constraints will be introduced in the master problem for rescheduling dynamic OPF. The proposed approach has been evaluated on an IEEE 118-bus test system. The results obtained with the proposed approach are presented and compared favorably with results of other stochastic techniques.
机译:本文介绍一种使用内点法(IPM)和Benders分解同时考虑有功和无功约束的动态最优潮流问题的方法。一种有效的预测-校正原始对偶内点算法用于解决线性化动态OPF问题。包含主动和被动安全约束将确保为动态OPF问题提供后续可行的解决方案。动态OPF问题被分解为主要问题和子问题,用于检查主动约束和被动约束的可行性。提出和解决主要问题时没有主动和被动的约束,可以避免复杂性。从主问题获得的动态OPF计划将应用于有功和无功子问题,以最大程度地减少违规。如果当前的排定单位组合无法消除违规情况,则将在主问题中引入其他约束以重新排定动态OPF。该提议的方法已经在IEEE 118总线测试系统上进行了评估。提出了用该方法获得的结果,并与其他随机技术的结果进行了比较。

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