An interior point nonlinear programming for optimal power flow problems with a novel data structure

Wang, Hua; Sasaki, Hiroshi; Kubokawa, Junji; Yokoyama, Ryuichi

doi:10.1109/59.708745

Cited by 332 publications

(27 citation statements)

References 18 publications

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“…Figure 2 shows that the complementary gap is gradually reduced until it becomes less than the tolerance. The centering parameter [16] of the NIPM is set to 0.1, and hence, we can expect the complementary gap to reduce by 90%. Figure 3 shows that in the initial part of the solution process, the maximum mismatch is dramatically reduced below the solution tolerance, but in further iteration, the maximum mismatch increases to Figure 4 shows the change in the active power loss during the solution iteration.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…From the result, an optimization problem with complementarity conditions was envisaged to be reformulated into a multi-objective optimization problem. For practical NIPM applications, a set of reduced correction equations is usually adopted, including the Hessian term and the Jacobian sub-matrix, only for the equality constraints [16,17]. Thus, if the equality formulation of (1) is added to the NIPM formulation, the system size of the correction equations must be increased; hence, a certain amount of effort is required for modification of the CC.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization

Song¹

2013

International Journal of Fuzzy Logic and Intelligent Systems

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This paper presents a fuzzy set method to enforce complementarity constraints (CCs) in a nonlinear interior point method (NIPM)-based optimization. NIPM is a Newton-type approach to nonlinear programming problems, but it adopts log-barrier functions to deal with the obstacle of managing inequality constraints. The fuzzy-enforcement method has been implemented for CCs, which can be incorporated in optimization problems for real-world applications. In this paper, numerical simulations that apply this method to power system optimal power flow problems are included.

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Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization

Song¹

2013

International Journal of Fuzzy Logic and Intelligent Systems

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“…Nonlinear programming is a very common issue in the operation of power systems, including reactive power optimization (RPO) [1], unit commitment (UC) [2], economic dispatch (ED) [3]. In order to tackle this issue, several optimization approaches have been adopted, such as the Newton method [4], quadratic programming [5], interior-point method [6]. However, these methods are essentially gradient-based mathematic optimization methods, which highly depend on an accurate system model.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Zhang

Yang

et al. 2019

Processes

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A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

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“…Traditional analytical methods include gradient descent, interior point methods, quadratic programming, etc. However, due to the nonconvexity of power flow equations, these traditional methods can only guarantee a local optimal solution [13]. Thus, convex programming methods are more desirable.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems

Wang

Jiang

et al. 2019

Applied Sciences

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This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.

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An interior point nonlinear programming for optimal power flow problems with a novel data structure

Cited by 332 publications

References 18 publications

Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization

Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems

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