scholarly journals Analysis of Optimal Steady-State Operation of Power Systems with Embedded FACTS Devices: A Matlab-Based Flexible Approach

2020 ◽  
Author(s):  
Jose Miguel García-Guzman ◽  
Néstor González-Cabrera ◽  
Luis Alberto Contreras-Aguilar ◽  
Jose Merced Lozano-García ◽  
Alejandro Pizano-Martinez
Keyword(s):  
Power Systems ◽  
Mathematical Models ◽  
Optimization Model ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Hessian Matrix ◽  
Inequality Constraints ◽  
Flexible Approach ◽  
Facts Devices ◽  
Equality And Inequality Constraints

This book chapter presents a flexible approach to incorporate mathematical models of FACTS devices into the Power Flow (PF) and the Optimal Power Flow (OPF) analysis tools, as well as into the standard OPF Market-Clearing (OPF-MC) procedure. The proposed approach uses the Matlab Optimization Toolbox because it allows to easily: (a) implement a given optimization model, (b) include different objective functions using distinct equality and inequality constraints and (c) modify and reuse an optimization model that has been previously implemented. The conventional OPF model is the main core of the proposed approach, which is easily implemented and adapted to include the mathematical models of FACTS devices. The resulting implementation of the OPF model featuring FACTS devices can be easily modified and adjusted to obtain the implementation of both the PF and the OPF-MC models which includes such devices. It should be mentioned that with the flexible approach proposed here, the complexity as well as the implementation time of optimized models featuring embedded FACTS devices is significantly reduced, since it is not necessary to define the expressions associated with the hessian matrix and the gradient vector. The flexibility and reliability of the proposed approach are demonstrated by means of several study cases using test as well as real power systems.

scholarly journals A novel bat algorithm for solving optimal power flow problem

2020 ◽  
Vol 41 (2) ◽  
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Inequality Constraints ◽  
Bat Algorithm ◽  
Adaptive Compensation ◽  
Optimal Power ◽  
Equality And Inequality Constraints ◽  
Quantum Behavior ◽  
Generation Costs

This paper presents an application of a novel bat algorithm (NBA) for solving optimal power flow (OPF) problems in power systems. The proposed algorithm combines a bat habitat selection and their self-adaptive compensation for the Doppler effects in echoes into the basic bat algorithm (BA). The selection of the bat habitat is modeled as a selection between their quantum behavior and mechanical behavior. The objective of this paper is to minimize the total generation costs by considering equality and inequality constraints. To validate the proposed algorithm, the standard IEEE 30-bus and 57-bus test systems are applied. The results show that the proposed technique provides a better solution than other heuristic techniques reported in the literature.

scholarly journals Comparing Corrective and Preventive Security-Constrained DCOPF Problems Using Linear Shift-Factors

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 516 ◽  
Author(s):  
Victor H. Hinojosa
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Solution Space ◽  
Inequality Constraints ◽  
Shift Factor ◽  
Advantages And Disadvantages ◽  
Decision Variables ◽  
Equality And Inequality Constraints ◽  
Commercial Solver

This study compares two efficient formulations to solve corrective as well as preventive security-constrained (SC) DC-based optimal power flow (OPF) problems using linear sensitivity factors without sacrificing optimality. Both SCOPF problems are modelled using two frameworks based on these distribution factors. The main advantage of the accomplished formulation is the significant reduction of decision variables and—equality and inequality—constraints in comparison with the traditional DC-based SCOPF formulation. Several test power systems and extensive computational experiments are conducted using a commercial solver to clearly demonstrate the feasibility to carry out the corrective and the preventive SCOPF problems with a reduced solution space. Another point worth noting is the lower simulation time achieved by the introduced methodology. Additionally, this study presents advantages and disadvantages for the proposed shift-factor formulation solving both corrective and preventive formulations.

An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

2017 ◽  
Vol 33 ◽  
pp. 84-99 ◽  
Author(s):  
Bachir Bentouati ◽  
Saliha Chettih ◽  
Rabah Djekidel ◽  
Ragab Abdel-Aziz El-Sehiemy
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Inequality Constraints ◽  
Cuckoo Search ◽  
Test System ◽  
Optimal Power ◽  
Equality And Inequality Constraints ◽  
Elitism Scheme ◽  
Complicated Task

The optimal power flow (OPF) problem is a very complicated task in power systems. OPF problem has a set of equality and inequality constraints. This paper looks at a chaotic cuckoo search (CCS) algorithm for solving non-convex OPF problem. The proposed CCS is a bio-inspired optimization calculation that is inspired by the behaviour of cuckoos people in nature. The chaotic guide is a variation of qualities combined with CS. A sinusoidal chaotic is integrated with CS algorithm and tested on standard IEEE 30-bus test system to the point of improving its global speed of convergence and enhancing its performance. The elitism scheme is also serves to save the best cuckoo during amid the procedure when updating the cuckoo. The results show clearly the superiority of CCS in searching for the best function values results when compared with well-known metaheuristic search algorithms.

Optimal TCSC placement for optimal power flow

2012 ◽  
Vol 63 (5) ◽  
pp. 316-321 ◽  
Author(s):  
Fatiha Lakdja ◽  
Fatima Zohra Gherbi ◽  
Redouane Berber ◽  
Houari Boudjella
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Analysis ◽  
Test System ◽  
Facts Devices ◽  
Optimal Power ◽  
Current Transmission ◽  
Flexible Alternating Current Transmission

Very few publications have been focused on the mathematical modeling of Flexible Alternating Current Transmission Systems (FACTS) -devices in optimal power flow analysis. A Thyristor Controlled Series Capacitors (TCSC) model has been proposed, and the model has been implemented in a successive QP. The mathematical models for TCSC have been established, and the Optimal Power Flow (OPF) problem with these FACTS-devices is solved by Newtons method. This article employs the Newton- based OPF-TCSC solver of MATLAB Simulator, thus it is essential to understand the development of OPF and the suitability of Newton-based algorithms for solving OPF-TCSC problem. The proposed concept was tested and validated with TCSC in twenty six-bus test system. Result shows that, when TCSC is used to relieve congestion in the system and the investment on TCSC can be recovered, with a new and original idea of integration.

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 Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1043 ◽  
Author(s):  
Arsalan Abdollahi ◽  
Ali Ghadimi ◽  
Mohammad Miveh ◽  
Fazel Mohammadi ◽  
Francisco Jurado
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Wind Power Generation ◽  
Krill Herd Algorithm ◽  
Facts Devices ◽  
Optimal Power ◽  
Krill Herd

This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature.

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

2016 ◽  
Vol 1 (3) ◽  
pp. 431 ◽  
Author(s):  
Bachir Bentouati ◽  
Lakhdar Chaib ◽  
Saliha Chettih
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Inequality Constraints ◽  
Interconnected Power System ◽  
Bee Colony ◽  
Optimal Power ◽  
Equality And Inequality Constraints ◽  
A New Technique

<p>In this paper, a new technique of optimization known as Moth-Flam Optimizer (MFO) has been proposed to solve the problem of the Optimal Power Flow (OPF) in the interconnected power system, taking into account the set of equality and inequality constraints. The proposed algorithm has been presented to the Algerian power system network for a variety of objectives. The obtained results are compared with recently published algorithms such as; as the Artificial Bee Colony (ABC), and other meta-heuristics. Simulation results clearly reveal the effectiveness and the robustness of the proposed algorithm for solving the OPF problem. </p>

FACTS Devices Allocation for Power Transfer Capability Enhancement and Power System Losses Reduction

2013 ◽  
Vol 2 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Peerapol Jirapong
Keyword(s):  
Tabu Search ◽  
Power Systems ◽  
Power Loss ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Evolutionary Programming ◽  
Optimal Placement ◽  
Facts Devices ◽  
Transfer Capability

In this paper, a hybrid evolutionary algorithm (HEA) is proposed to determine the optimal placement of multi-type flexible AC transmission system (FACTS) devices to simultaneously maximize the total transfer capability (TTC) and minimize the system real power loss of power transfers in deregulated power systems. Multi-objective optimal power flow (OPF) with FACTS devices including TTC, power losses, and penalty functions is used to evaluate the feasible maximum TTC value and minimum power loss within real and reactive power generation limits, thermal limits, voltage limits, stability limits, and FACTS devices operation limits. Test results on the modified IEEE 30-bus system indicate that optimally placed OPF with FACTS by the HEA approach could enhance TTC far more than those from evolutionary programming (EP), tabu search (TS), hybrid tabu search and simulated annealing (TS/SA), and improved evolutionary programming (IEP) algorithms, leading to much efficient utilization of the existing transmission 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 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.

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