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Solving corrective risk-based security-constrained optimal power flow with Lagrangian relaxation and Benders decomposition

机译:用拉格朗日松弛法和Benders分解法解决基于风险的校正性安全约束最优潮流

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This paper presents an efficient decomposition based algorithm to solve the corrective risk-based security-constrained optimal power flow (CRB-SCOPF) problem. The mathematical formulation was proposed imposing, in addition to the traditional post-contingency corrective constraints, constraints related to both circuit risk and system risk. Solving the CRB-SCOPF model is difficult since the risk index is a function of conditions under normal and all contingencies, and thus it greatly increases the dimension of the optimization problem. The proposed approach applies Lagrangian relaxation to the system risk constraints and then applies Benders decomposition to the remaining Lagrangian subproblem. The proposed approach is tested on the IEEE 30-bus system and on the ISO New England bulk system. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文提出了一种有效的基于分解的算法来解决基于修正风险的安全约束最优潮流(CRB-SCOPF)问题。提出了数学公式,除了传统的应急后纠正约束外,还提出了与电路风险和系统风险相关的约束。求解CRB-SCOPF模型非常困难,因为风险指数是正常情况和所有突发情况下条件的函数,因此极大地增加了优化问题的范围。所提出的方法将拉格朗日松弛应用于系统风险约束,然后将Benders分解应用于剩余的拉格朗日子问题。所提出的方法已在IEEE 30总线系统和ISO新英格兰批量系统上进行了测试。 (C)2015 Elsevier Ltd.保留所有权利。

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