On a comparison of Newton–Raphson solvers for power flow problems

Sereeter, Baljinnyam; Vuik, C.; Witteveen, Cees

doi:10.1016/j.cam.2019.04.007

Cited by 41 publications

(19 citation statements)

References 37 publications

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“…In this section, we compare the LPF approach using a constant impedance load model to the NPF methods using a constant power load model in terms of the accuracy and speed. The Newton power flow method developed in References [24,25] and a commercial network design software Vision [26], are used for NPF computations. Two small balanced distribution networks (Case33 and Case69: network details can be found in Reference [27]) and a large balanced distribution network of Alliander DNO (case991) are considered for the comparison.…”

Section: Comparison Between Linear and Nonlinear Power Flow Problemsmentioning

confidence: 99%

Linear Power Flow Method Improved With Numerical Analysis Techniques Applied to a Very Large Network

et al. 2019

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In this paper, we propose a fast linear power flow method using a constant impedance load model to simulate both the entire Low Voltage (LV) and Medium Voltage (MV) networks in a single simulation. Accuracy and efficiency of this linear approach are validated by comparing it with the Newton power flow algorithm and a commercial network design tool Vision on various distribution networks including real network data. Results show that our method can be as accurate as classical Nonlinear Power Flow (NPF) methods using a constant power load model and additionally, it is much faster than NPF computations. In our research, it is shown that voltage problems can be identified more efficiently when MV and LV are integrally evaluated. Moreover, Numerical Analysis (NA) techniques are applied to the Large Linear Power Flow (LLPF) problem with 27 million nonzeros in order to improve the computation time by studying the properties of the linear system. Finally, the original computation times of LLPF problems with real and complex components are reduced by 2.8 times and 5.7 times, respectively.Energies 2019, 12, 4078 2 of 15 distribution network, such as radial or weakly meshed structure, high R/X ratio, line's length and unbalanced loads. Many methods [6-9] have been developed on distribution power flow analysis and the most of them are based on the Backward-Forward Sweep (BFS) algorithm. Several reviews on distribution power flow solution methods can be found in References [10][11][12].All iterative power flow solution methods use a direct solver eventually for the linearized NPF problem in every iteration. It has been shown that iterative linear solvers can result in faster performances over sparse direct solvers for very large power flow problems [13][14][15]. In other words, the computational time of NPF computations can be improved by studying the properties of the linear system solved in every iteration and applying Numerical Analysis (NA) techniques such as different reordering schemes, various direct solvers and numerous Krylov subspace methods on them.Another way to ease the calculation and to speed up the computational time is to linearize NPF equations using some approximations and assumptions in order to obtain the Linear Power Flow (LPF) equations. After the linearization, the resulting LPF equations can be computed only once by direct solvers. Therefore, LPF computations are generally faster than NPF computations and are more suitable to be applied on very large networks with millions of cables for real time simulation. The best-known example of the LPF problem is the DC load flow [16] where linear relations are determined between the active power injections P and the voltage angles δ and the reactive power injections Q and the deviations of the unknown voltage magnitudes ∆|V|. Furthermore, the linear power flow formulation is obtained based on a voltage dependent (ZI) load model and some numerical approximations on the imaginary part of the nodal voltages in Reference [17]. Another linear power flow model based ...

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Section: Comparison Between Linear and Nonlinear Power Flow Problemsmentioning

confidence: 99%

Linear Power Flow Method Improved With Numerical Analysis Techniques Applied to a Very Large Network

et al. 2019

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“…This problem, known as the power flow problem, involves the computing of the voltage magnitude |V m | and angle δ m on each bus m of a power system given power generation and load demand. In this context a radial microgrid is considered and the voltage on the buses are estimated by a power flow analysis solved with the Newton-Raphson method (NRM) [32]. For this power flow constraint, the relation between the injected currents I and bus voltages V is described by the admittance matrix Y:…”

Section: ) Power Balance and Limits Constraintmentioning

confidence: 99%

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

et al. 2021

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Controlling distributed energy resources (DERs) in low voltage microgrids is a challenging task for operators. The simultaneous operation of independent small-scale DER owners could compromise the operator's hierarchical and centralized control to reach system stability and cost optimization. Recent Decentralized Energy Management (DEM) approaches provide flexibility for DERs control, but several existing solutions depend on powerful and expensive computer clusters and their ability to deal with a high burden of data in the communication channel. This work is motivated towards a DEM framework that involves independent DER owners while microgrid operator still maintains a hierarchical control philosophy. The framework must include a method to reduce the need of powerful computer clusters and depend on low bandwidth communications channel. Here, a multi-layered framework for every DER, consisting of physical, control, and agent layers for DEM is approached, where the agent layer participates in the energy management task. An Asynchronous Decentralized PSO (ADPSO) algorithm is proposed for the agent layer based on its primal characteristic: it can reach a consensus state between networked computing units by exchanging asynchronously only the state variable through the communications channel. The proposed solution allows the integration of DEM capabilities within the physical controller of the DERs, distinguishing it from other decentralized solutions. Easiness of implementation and low computational requirements are shown by performing DEM tests on single board computers. The tests show improved convergence rate, improved swarm diversity behavior and fast consensus reaching of DEM optimization.

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“…Consequently, the position of every agent can be updated in terms of the whole population, which leads to more random movements through the exploration process. The mathematical behavior of the exploration process is as stated in (10) and (11), with the chaos damping coefficient given in (15)   . In summary, the pseudo-code for the proposed FC-EWOA can be presented in Algorithm 1.…”

Section:  mentioning

confidence: 99%

“…Various optimization techniques have been utilized to handle this problem. Traditional optimization methods, such as Gradient Descent [10] and Newton Raphson [11], seem to be inefficient due to their dependency on initial conditions and differentiating the objective function. Alternatively, metaheuristic methods have been proven to be practical approaches to deal with different parameter identification problems [12]- [25] as well as practical optimization problems [25]- [28].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

2020

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Parameters identification of isolated wind-diesel power systems (WDPS) is a significant issue in stability analysis of the power system as well as guaranteeing the power generation through the control system. In this paper, enhanced whale optimization algorithms (EWOA) are proposed to deal with the parameter identification problem of a WDPS system. The proposed EWOA effectively tackles the premature convergence problem of WOA by splitting the population into two subpopulations and updating the position of each whale according to the position of the best agent in its current subpopulation, the position of the other subpopulation's best agent, and the position of the best neighboring agent. Furthermore, fractional chaotic maps are embedded in the search process of EWOA to increase its performance in terms of accuracy. For validation purposes, the proposed algorithms are applied to identify the unknown parameters of WDPS, where different statistical analyzes and comparisons are carried out with other recent state-of-the-art algorithms. Simulation results confirm that the algorithms have less deviation in parameter estimation, more convergence speed, and higher precision in comparison with other algorithms. INDEX TERMS Optimization, parameter identification, whale optimization algorithm, wind-diesel power system.

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On a comparison of Newton–Raphson solvers for power flow problems

Cited by 41 publications

References 37 publications

Linear Power Flow Method Improved With Numerical Analysis Techniques Applied to a Very Large Network

Linear Power Flow Method Improved With Numerical Analysis Techniques Applied to a Very Large Network

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

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