scholarly journals Optimal Power Flow with Considering Voltage Stability using Chaotic Firefly Algorithm

2019 ◽  
Vol 2 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Yun Tonce Kusuma Priyanto ◽  
Vicky Mudeng ◽  
Muhammad Robith ◽  
◽  
◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Firefly Algorithm ◽  
Power Transmission ◽  
Electrical Power ◽  
Optimal Power ◽  
Metaheuristic Methods ◽  
Generation Cost

In transportation technology, the development of electric vehicle is growing rapidly. In the future, the availability of electrical power is crucial because every electric tool needs electrical power. Power plant must provide electrical power for all consumer include an electric vehicle. Sustainability of electrical power transmission and distribution must be considered as critical due to its high power consumption in the community. One of the problem to supply electrical power is how to keep the system’s voltage stability. Several variations on the load pattern and topological can lead to a substantial poor impact on the system. However, generation cost must be considered by utilities’ operator. This paper demonstrates a recently developed metaheuristic method called Chaotic Firefly Algorithm (CFA). Our simulation shows that this method may perform better than several well-known metaheuristic methods. Therefore, CFA may become a promising method to solve optimal power flow considering voltage stability cases.

scholarly journals Optimal Power Flow with Considering Voltage Stability using Chaotic Firefly Algorithm

2019 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Yun Tonce Kusuma Priyanto ◽  
◽  
Vicky Mudeng ◽  
Muhammad Robith ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Firefly Algorithm ◽  
Optimal Power

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.

Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem

2016 ◽  
pp. 177-207 ◽  
Author(s):  
Jirawadee Polprasert ◽  
Weerakorn Ongsakul ◽  
Vo Ngoc Dieu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Stability Index ◽  
Gradient Search ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Optimal Power ◽  
Voltage Deviation

This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudo-gradient search for particle's movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.

scholarly journals Multi-objective optimal power flow in the presence of intermittent renewable energy sources

2018 ◽  
Vol 7 (4) ◽  
pp. 2766 ◽  
Author(s):  
S. Surender Reddy
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Wind Farms ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function. In this paper, three objective functions i.e., total generation cost, transmission losses and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the MO-OPF problems are solved using the multi-objective glowworm swarm optimiza-tion (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 bus system with two wind farms located at two different buses in the system.  

Optimal Power Flow Based Economic Generation Scheduling in Day-ahead Power Market

2017 ◽  
Vol 6 (3) ◽  
pp. 124
Author(s):  
Kshitij Choudhary ◽  
Rahul Kumar ◽  
Dheeresh Upadhyay ◽  
Brijesh Singh
Keyword(s):  
Power System ◽  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Congestion Management ◽  
Test Results ◽  
Loss Minimization ◽  
Optimal Power ◽  
Generation Scheduling ◽  
Generation Cost

The present work deals with the economic rescheduling of the generation in an hour-ahead electricity market. The schedules of various generators in a power system have been optimizing according to active power demand bids by various load buses. In this work, various aspects of power system such as congestion management, voltage stabilization and loss minimization have also taken into consideration for the achievement of the goal. The interior point (IP) based optimal power flow (OPF) methodology has been used to obtain the optimal generation schedule for economic system operation. The IP based OPF methodology has been tested on a modified IEEE-30 bus system. The obtained test results shows that not only the generation cost is reduced also the performance of power system has been improved using proposed methodology.

Security Enhanced OPF with Transmission Cost Solution under Bilateral and Multilateral Transactions

2014 ◽  
Vol 573 ◽  
pp. 734-740
Author(s):  
J. Bastin Solai Nazaran ◽  
K. Selvi
Keyword(s):  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Operating Conditions ◽  
Human Group ◽  
Transmission Cost ◽  
Intelligent Technique ◽  
Optimal Power ◽  
Deregulated Electricity Market ◽  
Generation Cost

In a deregulated electricity market, it is important to dispatch the generation in an economical manner. While dispatching it is also important to ensure security under different operating conditions. In this study intelligent technique based solution for optimal power flow is attempted. Transmission cost is calculated using Bialek’s upstream tracing method. Generation cost, transmission costs are combined together for pre and post contingency periods to form objective function. Different bilateral and multilateral conditions are considered for analysis. A human group optimization algorithm is used to find the solution of the problem. IEEE 30 bus system is taken as test systems.

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