A novel bat algorithm for solving optimal power flow problem

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.

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

International Journal of Engineering Research in Africa ◽

10.4028/www.scientific.net/jera.33.84 ◽

2017 ◽

Vol 33 ◽

pp. 84-99 ◽

Cited By ~ 3

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.

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SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks

Energies ◽

10.3390/en11113164 ◽

2018 ◽

Vol 11 (11) ◽

pp. 3164 ◽

Cited By ~ 3

Author(s):

Yuwei Chen ◽

Ji Xiang ◽

Yanjun Li

Keyword(s):

Power Systems ◽

Power Flow ◽

Optimal Power Flow ◽

Additional Term ◽

Transmission Losses ◽

Stability Margins ◽

Optimal Power ◽

Second Order Cone ◽

Convex Problem ◽

Generation Costs

Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective function of this OPF model has an additional term of current margins of the line besides the traditional transmission losses and generations costs, which contributes to thermal stability margins of power systems. The model is a reformulated bus injection model with clear physical meanings. Second order cone program (SOCP) relaxations for the proposed OPF are made, followed by the over-satisfaction condition guaranteeing the exactness of the SOCP relaxations. A simple 6-node case and several IEEE benchmark systems are studied to illustrate the efficiency of the developed results.

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Determining Countermeasures against Fault Currents Using a Decomposition Method Based on Fuzzy Fault Level Constrained Optimal Power Flow

Applied Sciences ◽

10.3390/app9020274 ◽

2019 ◽

Vol 9 (2) ◽

pp. 274

Author(s):

BenJeMar-Hope Flores ◽

Hwachang Song

Keyword(s):

Power Systems ◽

Decomposition Method ◽

Power Flow ◽

Optimal Power Flow ◽

Fault Currents ◽

Optimal Power ◽

Generation Costs ◽

The Given ◽

Selection Of ◽

Incremental Increase

This study applies a decomposed method to determine adequate countermeasures against excessive fault current levels in power systems. A set of candidate locations for the countermeasures such as bus splitting and current limiting reactors are pre-defined and modeled using variable reactances. A decomposition method is applied for the decision-making on the selection of the location and type of countermeasures. The main problem is to identify the optimal settings of the variable reactances by considering the sensitivities of the bus fault currents and generation costs with respect to the incremental increase in the reactance values of each countermeasure. For the subproblem, the optimization tool of fuzzy fault level constrained optimal power flow (FFLC-OPF) is applied to obtain the optimal operating point for the system with the given reactance settings. The FFLC-OPF incorporates both traditional constraints and fault level constraints in solving for the power flow. In addition, illustrative examples using the modified 28-bus system are included to show the effectiveness of the decomposition method.

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

10.5772/intechopen.93519 ◽

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.

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v1.i3.pp431-445 ◽

2016 ◽

Vol 1 (3) ◽

pp. 431 ◽

Cited By ~ 14

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>

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

Energies ◽

10.3390/en13030516 ◽

2020 ◽

Vol 13 (3) ◽

pp. 516 ◽

Cited By ~ 2

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.

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

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5403 ◽

2020 ◽

Vol 34 (01) ◽

pp. 630-637 ◽

Cited By ~ 2

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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