A vectorial implementation of dynamic optimal power flow(DOPF) including wind farms was *** vectorization of DOPF was established by arranging the control variables and state variables according to the variable types ...
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A vectorial implementation of dynamic optimal power flow(DOPF) including wind farms was *** vectorization of DOPF was established by arranging the control variables and state variables according to the variable types and time *** asynchronous generators in wind farms were modeled in Q-V formulation.A step-controlled primal-dual interior point framework(SCIPM) with upper and lower inequality constrains was adopted to solve this DOPF *** gradient and Hessian matrices of each time interval had relative non-zeros position with the admittance matrix,which was constant during *** a sparse data structure and memory allocation strategy was utilized to accelerate the construction of KKT *** effect of ramping rates and generation contract constrains on solving KKT system was analyzed in *** computation statistics,it is confirmed that approximate minimum degree(AMD) reordering algorithm is most efficient with only ramping rate constrains, and column approximate minimum degree(COLAMD) reordering algorithm is most efficient with both ramping rate and generation contract *** simulations on test systems ranging in size from 14 to 1040 buses over 12～96 time intervals validate the correctness and efficiency of the proposed *** technique with step-controlled primal-dual interior point method improves the calculation speed and convergence performance of DOPF.
A vectorial implementation of dynamic optimal power flow (DOPF) including wind farms was presented. The vectorization of DOPF was established by arranging the control variables and state variables according to the var...
详细信息
A vectorial implementation of dynamic optimal power flow (DOPF) including wind farms was presented. The vectorization of DOPF was established by arranging the control variables and state variables according to the variable types and time intervals. The asynchronous generators in wind farms were modeled in Q-V formulation. A step-controlled primal-dual interior point framework (SCIPM) with upper and lower inequality constrains was adopted to solve this DOPF model. The gradient and Hessian matrices of each time interval had relative non-zeros position with the admittance matrix, which was constant during iterations. Hence a sparse data structure and memory allocation strategy was utilized to accelerate the construction of KKT system. The effect of ramping rates and generation contract constrains on solving KKT system was analyzed in detail. Through computation statistics, it is confirmed that approximate minimum degree (AMD) reordering algorithm is most efficient with only ramping rate constrains, and column approximate minimum degree (COLAMD) reordering algorithm is most efficient with both ramping rate and generation contract constrains. Numerical simulations on test systems ranging in size from 14 to 1040 buses over 12～96 time intervals validate the correctness and efficiency of the proposed method. Vectorization technique with step-controlled primal-dual interior point method improves the calculation speed and convergence performance of DOPF.
State estimation base on a nonlinear programming model is presented（NLSE）,which is applied the vectorization *** choose the L2 norm estimation as object The nonlinear programming with equality constraint introduced ...
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State estimation base on a nonlinear programming model is presented（NLSE）,which is applied the vectorization *** choose the L2 norm estimation as object The nonlinear programming with equality constraint introduced slack variables, can guarantee the robust of the algorithm and dispose the convergence *** symmetric coefficient matrix of correction equation can be used by apply the AMD reordering algorithm and LDL algorithm on the solution,which can speed up the calculation *** whole model of nonlinear state estimation applies vectorization form,so the complexity extent is simplified and both versatility and maintainability of code are *** simulations use IEEE14,IEEE57,IEEE118, IEEE300,N1047 system to validate the correctness of the proposed model and method.
A vectorial implementation of dynamic optimal power flow(DOPF) including wind farms was *** vectorization of DOPF was established by arranging the control variables and state variables according to the variable types ...
详细信息
A vectorial implementation of dynamic optimal power flow(DOPF) including wind farms was *** vectorization of DOPF was established by arranging the control variables and state variables according to the variable types and time *** asynchronous generators in wind farms were modeled in Q-V formulation.A step-controlled primal-dual interior point framework(SCIPM) with upper and lower inequality constrains was adopted to solve this DOPF *** gradient and Hessian matrices of each time interval had relative non-zeros position with the admittance matrix,which was constant during *** a sparse data structure and memory allocation strategy was utilized to accelerate the construction of KKT *** effect of ramping rates and generation contract constrains on solving KKT system was analyzed in *** computation statistics,it is confirmed that approximate minimum degree(AMD) reordering algorithm is most efficient with only ramping rate constrains,and column approximate minimum degree(COLAMD) reordering algorithm is most efficient with both ramping rate and generation contract *** simulations on test systems ranging in size from 14 to 1040 buses over 12～96 time intervals validate the correctness and efficiency of the proposed *** technique with step-controlled primal-dual interior point method improves the calculation speed and convergence performance of DOPF.
A space-efficient partitioned representation of the inverse of a unit lower triangular matrix L may be used for efficiently solving sparse triangular systems on massively parallel computers. The number of steps requir...
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A space-efficient partitioned representation of the inverse of a unit lower triangular matrix L may be used for efficiently solving sparse triangular systems on massively parallel computers. The number of steps required in the parallel triangular solution is equal to the number of subsets of elementary triangular matrices in the partitioned representation of the inverse. Alvarado and Schreiber have recently described two partitioning algorithms that compute the minimum number of subsets in the partition over all permutations of L which preserve the lower triangular structure of the matrix. Their algorithms require space linear and time nonlinear in the number of nonzeros in L. This paper describes a partitioning algorithm that requires only O(n) time and space for computing an optimal partition, when L is restricted to be a Cholesky factor. (Here n is the order of L.) The savings result from the observation that instead of working with the structure of L, it is sufficient to work with its transitive reduction, the elimination tree of L. Experimentally the new partitioning algorithm requires negligible time in comparison to the previous partitioning algorithms and to the Multiple-Minimum-Degree ordering algorithm.
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