A New Universal Optimization Approach for Maximum Flows in Power Systems

2019 ◽  
Author(s):  
Simona Kirilova Filipova-Petrakieva
Keyword(s):  
Power Systems ◽  
Optimization Approach ◽  
Maximum Flows

scholarly journals Comparative Assessment of Priority Listing and Mixed Integer Linear Programming Unit Commitment Methods for Non-Interconnected Island Systems

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 657 ◽  
Author(s):  
Georgios Psarros ◽  
Stavros Papathanassiou
Keyword(s):  
Linear Programming ◽  
Power Systems ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Droop Characteristic

The generation management concept for non-interconnected island (NII) systems is traditionally based on simple, semi-empirical operating rules dating back to the era before the massive deployment of renewable energy sources (RES), which do not achieve maximum RES penetration, optimal dispatch of thermal units and satisfaction of system security criteria. Nowadays, more advanced unit commitment (UC) and economic-dispatch (ED) approaches based on optimization techniques are gradually introduced to safeguard system operation against severe disturbances, to prioritize RES participation and to optimize dispatch of the thermal generation fleet. The main objective of this paper is to comparatively assess the traditionally applied priority listing (PL) UC method and a more sophisticated mixed integer linear programming (MILP) UC optimization approach, dedicated to NII power systems. Additionally, to facilitate the comparison of the UC approaches and quantify their impact on systems security, a first attempt is made to relate the primary reserves capability of each unit to the maximum acceptable frequency deviation at steady state conditions after a severe disturbance and the droop characteristic of the unit’s speed governor. The fundamental differences between the two approaches are presented and discussed, while daily and annual simulations are performed and the results obtained are further analyzed.

scholarly journals An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1231-1237
Author(s):  
S T Suganthi ◽  
D Devaraj
Keyword(s):  
Power Systems ◽  
Congestion Management ◽  
Test System ◽  
Photovoltaic System ◽  
Management Technique ◽  
Optimization Approach ◽  
Solar Photovoltaic ◽  
Teaching Learning Based Optimization ◽  
Solar Photovoltaic System ◽  
Teaching Learning

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

A New Short-Term Load Forecasting in Power Systems

2014 ◽  
Vol 494-495 ◽  
pp. 1631-1635
Author(s):  
Hui Li ◽  
Hong Bin Sun
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Load Forecasting ◽  
Reference Points ◽  
Optimization Approach ◽  
New Approach ◽  
Forecasting Accuracy ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Daily Load

For realizing highly accuracy load forecasting, a new method is proposed. Power load time series belongs to chaotic series. Firstly, for obtaining three parameters in chaotic theory, namely time delay, embedding dimension and the number of the nearest neighbors, self-correlation function method and G-P algorithm are used to reconstruct the phase space of chaotic time series. Secondly, ant colony optimization approach is introduced to more accurately acquire forecasting reference points, considering distance and relativity of phase points evolution in this paper. Finally, GM (1, 1) Model is applied to forecast daily load data. The actual forecasting results prove that the new approach has better forecasting accuracy and convergence.

scholarly journals A Parallel Implementation of Unscheduled Flow Control in Interconnected Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
G. Ozdemir Dag ◽  
Mustafa Bagriyanik
Keyword(s):  
Power Systems ◽  
Power System ◽  
Parallel Computation ◽  
High Performance ◽  
Power Flow ◽  
Parallel Implementation ◽  
Computation Time ◽  
Test System ◽  
Fuzzy Decision Making ◽  
Optimization Approach

The unscheduled power flow problem needs to be minimized or controlled as soon as possible in a deregulated power system since the transmission systems are mostly operated at their power-carrying limits or very close to it. The time spent for simulations to determine the current states of all the system and control variables of the interconnected power system is important. Taking necessary action in case of any failure of equipment or any other occurrence of an undesired situation could be critical. Using supercomputing facilities and parallel computing techniques together decreases the computation time greatly. In this study, a parallel implementation of a multiobjective optimization approach based on both genetic algorithms and fuzzy decision making to manage unscheduled flows is presented. Parallel computation techniques are applied using supercomputers (high-performance computers). The proposed method is applied to the IEEE 300 bus test system. Two different cases for some parameters of GA are considered to see the power of parallel computation technique. Then the simulation results are presented.

scholarly journals Reliability optimization on power systems network using genetic algorithm

2020 ◽  
Vol 2 ◽  
pp. 18-29
Author(s):  
A. E. Airoboman ◽  
Emmanuel A. Ogujor
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Inequality Constraints ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Equality And Inequality Constraints ◽  
The Mean ◽  
Mean Time ◽  
Reliability And Availability ◽  
Selection Of

In this study, reliability optimization of a non-linear transmission network using Genetic Algorithm (GA) based optimization approach is presented and proposed. A GA based algorithm was developed for Koko, Guinness, Nekpenekpen, Ikpoba-Dam, Switch station, Etete and GRA 33kV tertiary transmission feeders within Benin Metropolis, Nigeria and was used to determine the optimal performance of the feeders’ reliability and availability through the minimization of downtime and the Mean Time between Failure (MTBF) by the appropriate selection of the objective functions and constraints. The equality and inequality constraints for each feeder on the network were defined, thereafter, codes were written on the Matlab 2016a environment to optimize the selected parameters. The results from the study showed a reduction in downtime of 5.63%, 26.87%, 34.20%, 5.42% 8.37%, 5.18% and 10.97% and an increment increased in MTBF by 4.95%, 19.87%, 4.58%, 3.85%, 4.88%, 5.77% and 13.56% for Guinness, Etete, Nekpenekpen, GRA, Switch station and Ikpoba-Dam feeders respectively. The obtained results, therefore, yielded an average corresponding improvement on the network’s reliability and availability by 1.85% and 2.83% respectively. Conclusively, the desired result reached in this paper validates the robustness of the GA tool in reliability studies. However, conscious effort must be geared concerning the ways and manners the system is operated in order to achieve desired results.

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