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.

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 Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

2020 ◽  
Vol 12 (2) ◽  
pp. 518
Author(s):  
Yue Chen ◽  
Zhizhong Guo ◽  
Hongbo Li ◽  
Yi Yang ◽  
Abebe Tilahun Tadie ◽  
...  
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Power Sources ◽  
Renewable Power ◽  
Point Estimate Method ◽  
Optimal Power ◽  
Simulation Results ◽  
Point Estimation Method

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

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.

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>

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.

scholarly journals A modified ABC algorithm for solving optimal power flow problem

2020 ◽  
Vol 17 (2) ◽  
pp. 199-211
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Artificial Bee Colony ◽  
Inequality Constraints ◽  
Optimization Method ◽  
Population Based ◽  
Test System ◽  
Bee Colony ◽  
Optimal Power ◽  
Mutation Strategy

This paper presents a modified artificial bee colony (MABC) algorithm for solving the optimal power flow (OPF) problem in power system. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The new algorithm is implemented to the OPF problem so as to minimize the total generation cost when considering the equality and inequality constraints. In order to validate of the proposed algorithm, it is applied to the standard IEEE 30-bus test system. The results show that the proposed technique provides better solutions than other heuristic techniques reported in literature.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

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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