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Modern Power System Analysis Pdf
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This article is an expanded version of our article published in 2021. In International Conference on Electrical, Computer, Communication and Mechatronics Engineering (ICECCME), Mauritius, Mauritius, 2021. 7.-8. October, p. 1-6; and presented at the 21st International Power Electronics Symposium (Ee2021), Novi Sad, Serbia, 2021. October 27-30
Received: 2022 January 31 / Revised: 2022 March 4 / Accepted: 2022 March 7 / Published: 2022 March 10
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The integration of renewable resources through distributed generation (DG) affects the conventional assessment of power system performance and poses serious challenges to deterministic load flow (DLF) analysis. DLF provides a snapshot of the system status without taking into account all the uncertainties arising from continuous DG due to changing weather conditions or uneven consumption. Therefore, probabilistic load flow (PLF) should be used to more accurately monitor and represent system variables. First, this paper provides a literature review of various PLF methods. It focuses on numerical methods, examining them for simple random and Latin hypercube sampling, which have been widely used in previous work, and proposes a method that combines Monte Carlo simulations with Halton quasi-random numbers. Stochastic simulations are performed for solar and wind power output. To compare the methods and validate the applicability of the proposed PLF method to Halton sequences, various IEEE test cases, all modified by the addition of DG, were used. A more accurate evaluation of the method is done by discussing different levels of renewable energy penetration and processing times. The overall simulation results showed that the results of the Halton method are similar in accuracy to the commonly used Latin hypercube method, thus demonstrating the relevance of the proposed method and the possibility of its application in modern system analysis.
The reliable supply of consumers with electricity of a specified quality is one of the main objectives of electric power systems. The segmentation of vertically integrated energy companies and the creation of liberal electricity markets have further emphasized the importance of keeping electricity quality and voltage parameters within specified ranges [1].
Modern trends in the development of power systems have increased the demand for distributed generation (DG) based on renewable energy sources, driven by the demand to slow down climate change by reducing greenhouse gas emissions from outdated traditional fossil fuel-fired power plants. Wind power plants and solar power plants make up the majority of energy sources from the General Directorates of Renewable Energy [2]. Their power generation capacity is highly intermittent and highly dependent on variable meteorological phenomena such as wind speed and solar radiation, making them units that cannot be deployed [3]. Emerging DGs have also created an entirely new category of power system element, consumers (ie consumers who can generate electricity for themselves or perhaps for a neighboring power system).
The COVID-19 crisis and the 2020 blocking measures in Europe have also shown the importance of renewable energy. At a time of extraordinary load-sharing and decline of nuclear and coal power across the continent, renewables have taken the lead and demonstrated their value without major restrictions [4], showing that they are now a very important and inevitable part of electricity. fence.
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When analyzing modern energy systems, electric vehicles (EV) must also be considered in addition to renewable energy sources. Their ability to be consumers that successfully meet environmental requirements and storage systems that can contribute to peak load shaving [5] makes EVs very useful, but also challenging to integrate into energy systems [6].
With all the information mentioned above in mind, it is obvious that with each new day, modern electrical systems have less and less similarities with traditional electrical systems. In this regard, the traditional, fixed-valued, deterministic concept of power flow analysis (DLF) cannot guarantee sufficient accuracy of the obtained results when used in modern power systems. The point is that DLF does not take into account the uncertainty of power generation and consumption, which is inherent in modern power systems with a large proportion of dispatch equipment. In other words, DLF can provide results that are only valid at a certain point in time [7].
The aforementioned innovations, which are gradually gaining an increasing role in the power grid, require more accurate monitoring and reporting of system variables. Applying the Probabilistic Load Flow (PLF) method can solve this problem. Including computational uncertainties, the PLF program provides stochastic ranges of variables (e.g. bus voltages) rather than ranges of fixed values [8].
The structure of this article is as follows: Section 2 explains the basic ideas, methods and objectives of load flow calculation. A detailed overview of the various PLF methods, divided into three broad groups (analytical, numerical, and approximation methods), is presented in Section 3 . It focuses on numerical techniques based on Monte Carlo simulations, investigating their behavior under different sampling methods. First, the simplest method is presented – the simplest random sampling method and one of the stratified sampling methods (Hypercube sampling in Latin), which was widely used in previous PLF analysis works. A quasi-Monte Carlo PLF method is then proposed, combining Monte Carlo simulations with Halton quasi-random numbers, with the aim of demonstrating its value in terms of the relatively smaller number of simulations required for accurate computation. The theoretical background of Halton quasi-random sequences is presented in Chapter 4. Furthermore, Section 5 provides a case study simulation of the proposed methods. First, the procedure for stochastic modeling of system variable uncertainties is shown. Actual historical meteorological records (i.e. solar and wind data) were used and modeled with selected probability distribution functions (which were then sampled accordingly). To compare the methods and validate the applicability of the proposed PLF method, all analyzes were performed in MATLAB using several different IEEE test cases (i.e., 14-, 30-, and 118-bus systems), all modified by adding some of the investigated DGs throughout the network. In addition, different penetration levels of renewable energy sources are taken into account for a more comprehensive evaluation of the method. In addition to evaluating the results in terms of accuracy, they were also discussed in terms of processing time. Finally, our conclusions are presented in Section 6.
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This paper is a continuation of our research previously published at the International Conference on Electrical, Computer, Communication and Mechatronics Technology (ICECCME 2021) in Mauritius [9] and presented at the 21st International Power Electronics Symposium (Ee2021) in Novi Sad. Several new contributions have been added to the previous study:
Load flow analysis is one of the most important and frequently used calculations in the field of energy system analysis. The results of this calculation are very important for the correct planning of new and existing parts of electrical systems. Based on the data obtained during this analysis, the needs for the construction of new system elements, such as optimal additional production units, transformers, power lines, reactive energy compensators, etc., are assessed. place and power. It also ensures that the existing elements can withstand the stresses during continuous operation. The conclusions obtained during the analysis help to find out the optimal operation of the system from the economic dispatcher’s point of view. In addition, although the obtained data are related to the steady state of the system, its algorithm is the basis for dynamic analysis of system stability [10].
The analysis includes determination of bus voltage modules and phase angles and calculation of active and reactive power in transmission (distribution) lines. Required inputs are network configuration and related parameters, energy consumption and rated power, and characteristics of generators.
Where Pi and Qi are the active and reactive power inserted into nodes i, Ui and Uj are the voltage amplitudes at nodes i and j, total n
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