scholarly journals A Practical Algorithm for Optimal Power Flow Based on Successive approximation Technique for Large-Scale Power systems

1999 ◽  
Vol 119 (12) ◽  
pp. 1402-1411
Author(s):  
Ryuya Tanabe ◽  
Yasuyuki Tada ◽  
Hiroshi Okamoto ◽  
Hideki Suzuki
Keyword(s):  
Power Systems ◽  
Successive Approximation ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Approximation Technique ◽  
Optimal Power ◽  
Practical Algorithm

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

scholarly journals Probabilistic Optimal Power Flow Using Linear Fuzzy Relation

2021 ◽  
Author(s):  
Inderdeep S. Arneja
Keyword(s):  
Power Systems ◽  
Stochastic Models ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Hessian Matrix ◽  
Optimal Solution ◽  
Fuzzy Relation ◽  
Monte Carlo Simulation Technique ◽  
Optimal Power

Optimal Power Flow (OPF) is a very important tool for planning and analysis of power systems. In the recent times, uncertain renewable energy is being integrated into power systems in a large scale. Appropriate modeling of renewables in optimal power flow requires using stochastic models. Using stochastic models of renewables in optimal power flow is numerically and algorithmically challenging due to the complexity of stochastic models and nonlinear nature of bus power balance equations. Hitherto, Monte Carlo simulation technique and Cumulant technique have been proposed, but they are not computationally viable for large systems. In this thesis, we propose the use of linear fuzzy relation technique to relate stochastic models of dependent variables of optimal power flow formulation in terms of control variables that include power output of renewables. This fuzzy relation uses Hessian matrix of the LaGrangian of the optimal power flow formulation at optimal solution point. The technique is tested on a six bus system and results are reported. One can intuitively see that this technique can be easily extended to larger systems.

scholarly journals Probabilistic Optimal Power Flow Using Linear Fuzzy Relation

2021 ◽  
Author(s):  
Inderdeep S. Arneja
Keyword(s):  
Power Systems ◽  
Stochastic Models ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Hessian Matrix ◽  
Optimal Solution ◽  
Fuzzy Relation ◽  
Monte Carlo Simulation Technique ◽  
Optimal Power

Optimal Power Flow (OPF) is a very important tool for planning and analysis of power systems. In the recent times, uncertain renewable energy is being integrated into power systems in a large scale. Appropriate modeling of renewables in optimal power flow requires using stochastic models. Using stochastic models of renewables in optimal power flow is numerically and algorithmically challenging due to the complexity of stochastic models and nonlinear nature of bus power balance equations. Hitherto, Monte Carlo simulation technique and Cumulant technique have been proposed, but they are not computationally viable for large systems. In this thesis, we propose the use of linear fuzzy relation technique to relate stochastic models of dependent variables of optimal power flow formulation in terms of control variables that include power output of renewables. This fuzzy relation uses Hessian matrix of the LaGrangian of the optimal power flow formulation at optimal solution point. The technique is tested on a six bus system and results are reported. One can intuitively see that this technique can be easily extended to larger systems.

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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