scholarly journals Contingency-Constrained Optimal Power Flow Using Simplex-Based Chaotic-PSO Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Zwe-Lee Gaing ◽  
Chia-Hung Lin
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transmission Loss ◽  
Chaotic Map ◽  
Pso Algorithm ◽  
Voltage Profile ◽  
Solution Quality ◽  
Optimal Power ◽  
Loading Effects

This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems.

scholarly journals Computational Analysis of Different Artificial Intelligence Based Optimization Techniques for Optimal Power Flow and Economic Load Dispatch Problem

2013 ◽  
Vol 4 (1) ◽  
pp. 82-87
Author(s):  
Netra M Lokhande ◽  
Debirupa Hore
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Computational Analysis ◽  
Transmission Loss ◽  
Optimization Techniques ◽  
Bacterial Foraging Optimization ◽  
Economic Market ◽  
Optimal Power

The purpose of this paper is to present a computational Analysis of various Artificial Intelligence based optimization Techniques used to solve OPF problems. The various Artificial Intelligence methods such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Bacterial Foraging Optimization(BFO), ANN are studied and analyzed in detail. The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost and transmission loss etc. or maximizes social welfare, load ability etc. while maintaining an acceptable system performance in terms of limits on generators’ real and reactive powers, power flow limits, output of various compensating devices etc. Traditionally, Classical optimization methods were used effectively to solve optimal power flow. But, recently due to the incorporation of FACTS devices and deregulation of power sector the traditional concepts and practices of power systems are superimposed by an economic market management and hence OPF have become more complex. So, in recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex OPF problems at faster rate.

scholarly journals A Fast Procedure for Transmission Loss Allocation of Power Systems by Using DC-OPF

1970 ◽  
Vol 42 (3) ◽  
pp. 249-256
Author(s):  
MH Kabir
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transmission Loss ◽  
Loss Allocation ◽  
Optimal Power ◽  
Small Model ◽  
Transmission Loss Allocation ◽  
Power Outputs ◽  
Fast Procedure

Deregulation of power industry has made us think over the transmission loss allocation to network users and to various interconnected transactions including bilateral one. It is an important issue to be solved exactly. Because of the non-linear nature of power flow and power loss, it is very difficult to segregate and to allocate losses among the participants properly. This paper presents an instantaneous loss allocation algorithm including DC-Optimal Power Flow. Using Incremental Transmission Loss (ITLs) factors and the power outputs of generators, we have calculated preliminary loss to each generator. Calculating loss allocation rates from the preliminary losses, the final loss allocation has been done (according to loss allocation rate) to meet the total loss calculated by DC-OPF. The effectiveness of this procedure has been studied for a small model power system and also for the IEEE-118-bus system. Keywords: Transmission loss allocation, DC optimal power flow, Deregulated power market, Bilateral transactions. Bangladesh J. Sci. Ind. Res. 42(3), 249-256, 2007

scholarly journals A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

2021 ◽  
Vol 13 (9) ◽  
pp. 4979
Author(s):  
Hatem Diab ◽  
Mahmoud Abdelsalam ◽  
Alaa Abdelbary
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Control Technique ◽  
Heuristic Optimization ◽  
Multi Objective ◽  
Optimal Power

Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.

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

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3164 ◽  
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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