Optimal VAr planning by approximation method for recursive mixed-integer linear programming

Aoki, Kenichi; Fan, Mingtian; Nishikori, Akimine

doi:10.1109/59.192990

Cited by 180 publications

(62 citation statements)

References 7 publications

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“…An optimization approach was proposed in [1] based on recursive mixed-integer programming method. The feature of the proposed algorithm is to treat the capacitor or reactor compensation unit number as a discrete variable.…”

Section: Introductionmentioning

confidence: 99%

A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

et al. 2016

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Abstract:In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS) is defined to measure the degree of similarity between the loads in different days and the calculation method of the load similarity of load random matrix (LRM) is presented. By calculating the load similarity between the forecasting random matrix and the random matrix of historical load, the historical reactive power optimization dispatching scheme that most matches the forecasting load can be found for reactive power control usage. The differences of daily load curves between working days and weekends in different seasons are considered in the proposed method. The proposed method is tested on a standard 14 nodes distribution network with three different types of load. The computational result demonstrates that the proposed method for reactive power optimization is fast, feasible and effective in distribution network.

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Section: Introductionmentioning

confidence: 99%

A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

et al. 2016

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“…Since then a large spectrum of heuristic approaches have been proposed to deal with discrete variables e.g. round-off techniques [4], [5], [7], methods handling discrete variables in NLP and LP OPF formulations by means of penalty functions [8]- [10], ordinal optimization [11], recursive mixed-integer linear programming [12], interior point cutting plane [13], [14], global optimization methods [15]- [18], etc.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity-Based Approaches for Handling Discrete Variables in Optimal Power Flow Computations

Capitanescu

Wehenkel

2010

IEEE Trans. Power Syst.

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Abstract-This paper proposes and compares three iterative approaches for handling discrete variables in optimal power flow (OPF) computations. The first two approaches rely on the sensitivities of the objective and inequality constraints with respect to discrete variables. They set the discrete variables values either by solving a mixed integer linear programming (MILP) problem or by using a simple procedure based on a merit function. The third approach relies on the use of Lagrange multipliers corresponding to the discrete variables bound constraints at the OPF solution. The classical round-off technique and a progressive round-off approach have been also used as a basis of comparison. We provide extensive numerical results with these approaches on four test systems with up to 1203 buses, and for two OPF problems: loss minimization and generation cost minimization, respectively. These results show that the sensitivity-based approach combined with the merit function clearly outperforms the other approaches in terms of: objective function quality, reliability, and computational times. Furthermore, the objective value obtained with this approach has been very close to that provided by the continuous relaxation OPF. This approach constitutes therefore a viable alternative to other methods dealing with discrete variables in an OPF.Index Terms-discrete variables, mixed integer linear programming, mixed integer nonlinear programming, nonlinear programming, optimal power flow

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“…A better formulation with the format [3]- [5], [35]- [38] is to consider the fixed cost, ($/hour), which is the lifetime fixed cost prorated to per hour, in addition to the incremental/variable cost, . This is a more realistic model of Var cost, but this would complicate the problem from a nonlinear programming (NLP) to a mixed-integer NLP (MINLP), because there is a binary variable to indicate whether the Var source will be actually installed or not.…”

Section: A Minimize Var Costmentioning

confidence: 99%

“…Reference [3] presents the recursive mixed-integer programming technique using an approximation method. Reference [4] employs branch and bound (B&B) method, which is a typical method for integer programming.…”

Section: ) Mixed-integer Nonlinear Programming (Minlp)mentioning

confidence: 99%

Review of reactive power planning: objectives, constraints, and algorithms

Zhang

Tolbert

2008

2008 IEEE/PES Transmission and Distribution Conference and Exposition

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Optimal VAr planning by approximation method for recursive mixed-integer linear programming

Cited by 180 publications

References 7 publications

A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

Sensitivity-Based Approaches for Handling Discrete Variables in Optimal Power Flow Computations

Review of reactive power planning: objectives, constraints, and algorithms

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