scholarly journals Optimal location of UPFC based on sensitivity of CDF with power system’s stochasticity

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Ningyu Zhang ◽  
◽  
Jingbo Zhao ◽  
Qian Zhou ◽  
◽  
...  
Keyword(s):  
Transmission Lines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimal Location ◽  
Cumulative Distribution ◽  
Point Estimate ◽  
Unified Power Flow Controller ◽  
Point Estimate Method ◽  
Operation Cost

In this paper, a stochastic sensitivity algorithm is introduced to optimize the location of unified power flow controller (UPFC) in large scale power grid. The stochastic sensitivities are defined as the total operation cost of power system to control parameters of UPFC. Firstly, probability optimal power flow (POPF) model with power system’s randomness is established. Then point estimate method (PEM) is utilized to solve the above problem, so that the stochastic sensitivities of UPFC in all possible transmission lines could be obtained. Finally, by sorting the influence degree of UPFC at different locations to cumulative distribution function (CDF) of operation cost, the optimal location for UPFC could be selected correspondingly. To this end, IEEE-5 and IEEE-14 systems are employed to verify our proposed approach. The results show that installing UPFC by the method in this paper could significantly reduce the probability distribution of operation cost in higher region.

scholarly journals Optimal location of unified power flow controller genetic algorithm based

2020 ◽  
Vol 11 (2) ◽  
pp. 886
Author(s):  
Sana Khalid Abdul Hassan ◽  
Firas Mohammed Tuaimah
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Power System ◽  
Transmission Line ◽  
Transmission Lines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Optimal Location ◽  
Power Losses

<p>Now-a-days the Flexible AC Transmission Systems (FACTS) technology is very effective in improving the power flow along the transmission lines and makes the power system more flexible and controllable. This paper deals with overload transmission system problems such as (increase the total losses, raise the rate of power generation, and the transmission line may be exposed to shut down when the load demand increase from the thermal limit of transmission line) and how can solve this problem by choosing the optimal location and parameters of Unified Power Flow Controllers (UPFCs). which was specified based on Genetic Algorithm (GA) optimization method, it was utilized to search for optimum FACT parameters setting and location based to achieve the following objectives: improve voltages profile, reduce power losses, treatment of power flow in overloaded transmission lines and reduce power generation. MATLAB was used for running both the GA program and Newton Raphson method for solving the load flow of the system The proposed approach is examined and tested on IEEE 30-bus system. The practical part has been solved through Power System Simulation for Engineers (PSS\E) software Version 32.0 (The Power System Simulator for Engineering (PSS/E) software created from Siemens PTI to provide a system of computer programs and structured data files designed to handle the basic functions of power system performance simulation work, such as power flow, optimal power flow, fault analysis, dynamic simulations...etc.). The Comparative results between the experimental and practical parts obtained from adopting the UPFC where too close and almost the same under different loading conditions, which are (5%, 10%, 15% and 20%) of the total load. can show that the total active power losses for the system reduce at 69.594% at normal case after add the UPFC device to the system. also the reactive power losses reduce by 75.483% at the same case as well as for the rest of the cases. in the other hand can noted the system will not have any overload lines after add UPFC to the system with suitable parameters.</p>

scholarly journals Implementation Optimal Location and Sizing of UPFC on Iraqi Power System Grid (132 kV) Using Genetic Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 1607
Author(s):  
Ghassan Abdullah Salman ◽  
Mohammed Hasan Ali ◽  
Ali Najim Abdullah
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Power System ◽  
Transmission Lines ◽  
Power Flow ◽  
Optimal Location ◽  
Unified Power Flow Controller ◽  
Flexible Ac Transmission System ◽  
Intelligent Methods ◽  
Ac Transmission

Electric power systems required efficient processors and intelligent methods for sustainability therefore, in this paper used Flexible AC Transmission System (FACTS) device specifically Unified Power Flow Controller (UPFC) because of its useful properties on series and shunt devices and used Genetic Algorithm (GA) to determine the optimal location and values of UPFC to achieve the following objectives: improve voltages profile, reduce power losses, treatment of power flow in overloaded transmission lines and reduce power generation. Consequently, all of these goals led to a reduction in the total cost of the power system. GA was applied to an Iraqi local power grid system (Diyala 132 kV) to find the optimal values and locations of UPFC for the purpose of achieving the objectives mentioned above using the MATLAB program. The simulation results showed the effectiveness of GA to calculate the optimum values and locations of UPFC and promising results were obtained for the Diyala power network (132 kV) with regard to the desired objectives.

scholarly journals Optimal power flow for distribution networks with distributed generation

2015 ◽  
Vol 12 (2) ◽  
pp. 145-170 ◽  
Author(s):  
Jordan Radosavljevic ◽  
Miroljub Jevtic ◽  
Dardan Klimenta ◽  
Nebojsa Arsic
Keyword(s):  
Distributed Generation ◽  
Wind Turbines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Distribution Networks ◽  
Point Estimate ◽  
Photovoltaic Systems ◽  
Multi Objective ◽  
Point Estimate Method ◽  
Optimal Power

This paper presents a genetic algorithm (GA) based approach for the solution of the optimal power flow (OPF) in distribution networks with distributed generation (DG) units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs) of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators.

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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