Evaluation of the impact of a large cyclic load on the LILCO power system using time simulation and frequency domain techniques

1994 ◽  
Vol 9 (3) ◽  
pp. 1411-1416 ◽  
Author(s):  
N. Rostamkolai ◽  
R.J. Piwko ◽  
A.S. Matusik
Keyword(s):  
Power System ◽  
Frequency Domain ◽  
Cyclic Load ◽  
Time Simulation ◽  
Frequency Domain Techniques ◽  
The Impact

scholarly journals Frequency-Domain Nonlinear Modeling Approaches for Power Systems Components—A Comparison

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2609
Author(s):  
Marco Faifer ◽  
Christian Laurano ◽  
Roberto Ottoboni ◽  
Sergio Toscani ◽  
Michele Zanoni
Keyword(s):  
Power Systems ◽  
Power System ◽  
Frequency Domain ◽  
Nonlinear Modeling ◽  
Quality Level ◽  
Domain Modeling ◽  
Behavioral Models ◽  
Volterra Models ◽  
Modeling Approaches ◽  
The Impact

Harmonic simulations play a key role in studying and predicting the impact of nonlinear devices on the power quality level of distribution grids. A frequency-domain approach allows higher computational efficiency, which has key importance as long as complex networks have to be studied. However, this requires proper frequency-domain behavioral models able to represent the nonlinear voltage–current relationship characterizing these devices. The Frequency Transfer Matrix (FTM) method is one of the most widespread frequency domain modeling approaches for power system applications. However, others suitable techniques have been developed in the last years, in particular the X-parameters approach, which comes from radiofrequency and microwave applications, and the simplified Volterra models under quasi-sinusoidal conditions, that have been specifically tailored for power system devices. In this paper FTM, X-parameters and simplified Volterra approaches are compared in representing the nonlinear voltage –current relationship of a bridge rectifier feeding an ohmic-capacitive dc load. Results show that the X-parameters model reaches good accuracy, which is slightly better than that achieved by the FTM and simplified Volterra models, but with a considerably larger set of coefficients. Simplified Volterra models under quasi-sinusoidal conditions allows an effective trade-off between accuracy and complexity.

On the Impact of Discrete Secondary Controllers on Power System Dynamics

2021 ◽  
pp. 1-1
Author(s):  
Taulant Kerci ◽  
Mohammed Ahsan Adib Murad ◽  
Ioannis Dassios ◽  
Federico Milano
Keyword(s):  
Power System ◽  
System Dynamics ◽  
Power System Dynamics ◽  
The Impact

scholarly journals Impact of the Generation System Parameters on the Frequency Response of the Power System: A UK Grid Case Study

Electricity ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 143-157
Author(s):  
Jovi Atkinson ◽  
Ibrahim M. Albayati
Keyword(s):  
Power System ◽  
Frequency Response ◽  
Power Grid ◽  
Sudden Increase ◽  
Generation System ◽  
System Parameters ◽  
Primary Frequency ◽  
Primary Frequency Response ◽  
The Impact ◽  
System Inertia

The operation and the development of power system networks introduce new types of stability problems. The effect of the power generation and consumption on the frequency of the power system can be described as a demand/generation imbalance resulting from a sudden increase/decrease in the demand and/or generation. This paper investigates the impact of a loss of generation on the transient behaviour of the power grid frequency. A simplified power system model is proposed to examine the impact of change of the main generation system parameters (system inertia, governor droop setting, load damping constant, and the high-pressure steam turbine power fraction), on the primary frequency response in responding to the disturbance of a 1.32 GW generation loss on the UK power grid. Various rates of primary frequency responses are simulated via adjusting system parameters of the synchronous generators to enable the controlled generators providing a fast-reliable primary frequency response within 10 s after a loss of generation. It is concluded that a generation system inertia and a governor droop setting are the most dominant parameters that effect the system frequency response after a loss of generation. Therefore, for different levels of generation loss, the recovery rate will be dependent on the changes of the governor droop setting values. The proposed model offers a fundamental basis for a further investigation to be carried on how a power system will react during a secondary frequency response.

scholarly journals Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1648
Author(s):  
Marinko Barukčić ◽  
Toni Varga ◽  
Vedrana Jerković Jerković Štil ◽  
Tin Benšić
Keyword(s):  
Power System ◽  
Power Management ◽  
Computational Intelligence ◽  
Input Data ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Distributed Generations ◽  
Data Resolution ◽  
The Impact

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

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