An Affine Arithmetic method to solve the stochastic power flow problem based on a mixed complementarity formulation

2012 ◽  
Author(s):  
M. Pirnia ◽  
C. A. Canizares ◽  
K. Bhattacharya ◽  
A. Vaccaro
Keyword(s):  
Power Flow ◽  
Flow Problem ◽  
Affine Arithmetic ◽  
Arithmetic Method ◽  
Power Flow Problem

Solving Uncertain Power Flow Problem by Affine Arithmetic

2018 ◽  
Author(s):  
Guido Coletta ◽  
Alfredo Vaccaro ◽  
Domenico Villacci ◽  
Andrea Zollo
Keyword(s):  
Power Flow ◽  
Flow Problem ◽  
Affine Arithmetic ◽  
Power Flow Problem

scholarly journals A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4008
Author(s):  
Hao Wu ◽  
Lin Zhou ◽  
Yihao Wan ◽  
Qiang Liu ◽  
Siyu Zhou
Keyword(s):  
Power Flow ◽  
Large Scale ◽  
Flow Problem ◽  
Power Flow Calculation ◽  
Partial Shading ◽  
Affine Arithmetic ◽  
Interval Algorithm ◽  
Flow Algorithm ◽  
Mixed Power ◽  
Power Flow Problem

With the large-scale centralized PV clusters connected to grid, the grid power flow has certain randomness. Considering the fluctuation of PV output, an improved Krawczyk-Moore algorithm in a mixed coordinate system is proposed to solve the uncertain power flow problem. Firstly, aiming at the special structure of a centralized PV cluster with only load node and no generator node, this paper proposes a power flow calculation in the mixed power flow coordinate, and then the Krawczyk-Moore operator is used to combine interval and affine arithmetic to overcome the shortcoming of over-conservative interval algorithm. Finally, the voltage operating condition under different volatility and different partial shading conditions is studied through the simulation of a practical example, and the out-of-limit voltage problem inside the centralized PV cluster is analyzed. Meanwhile, the effectiveness of the proposed algorithm is verified.

scholarly journals A Full-Newton AC-DC Power Flow Methodology for HVDC Multi-Terminal Systems and Generic DC Network Representation

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1658
Author(s):  
Leandro Almeida Vasconcelos ◽  
João Alberto Passos Filho ◽  
André Luis Marques Marcato ◽  
Giovani Santiago Junqueira
Keyword(s):  
Power Systems ◽  
Direct Current ◽  
Power Flow ◽  
Large Scale ◽  
Flow Problem ◽  
Control Strategies ◽  
Problem Formulation ◽  
Expansion Planning ◽  
Dc Power ◽  
Power Flow Problem

The use of Direct Current (DC) transmission links in power systems is increasing continuously. Thus, it is important to develop new techniques to model the inclusion of these devices in network analysis, in order to allow studies of the operation and expansion planning of large-scale electric power systems. In this context, the main objective of this paper is to present a new methodology for a simultaneous AC-DC power flow for a multi-terminal High Voltage Direct Current (HVDC) system with a generic representation of the DC network. The proposed methodology is based on a full Newton formulation for solving the AC-DC power flow problem. Equations representing the converters and steady-state control strategies are included in a power flow problem formulation, resulting in an expanded Jacobian matrix of the Newton method. Some results are presented based on HVDC test systems to confirm the effectiveness of the proposed approach.

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

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