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Journal articles on the topic 'Probabilistic Power Flows'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Demazy, Antonin, Tansu Alpcan, and Iven Mareels. "A Probabilistic Reverse Power Flows Scenario Analysis Framework." IEEE Open Access Journal of Power and Energy 7 (2020): 524–32. http://dx.doi.org/10.1109/oajpe.2020.3032902.

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2

Löschenbrand, Markus. "Stochastic variational inference for probabilistic optimal power flows." Electric Power Systems Research 200 (November 2021): 107465. http://dx.doi.org/10.1016/j.epsr.2021.107465.

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3

Schlapfer, Markus, and Pierluigi Mancarella. "Probabilistic Modeling and Simulation of Transmission Line Temperatures Under Fluctuating Power Flows." IEEE Transactions on Power Delivery 26, no. 4 (2011): 2235–43. http://dx.doi.org/10.1109/tpwrd.2011.2145394.

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4

Przygrodzki, Maksymilian, and Paweł Kubek. "The Polish Practice of Probabilistic Approach in Power System Development Planning." Energies 14, no. 1 (2020): 161. http://dx.doi.org/10.3390/en14010161.

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Power systems can be analyzed using either a deterministic or a probabilistic approach. The deterministic analysis centers on studying the quantities and indicators that characterize the operating states of the power system under strictly defined conditions. However, the long-term horizon of planning analyses, the changes of marketing mechanisms, the development of renewable electricity sources, the leaving from large-scale generation, the growth of smart technology and the increase in consumer awareness make the development of transmission networks a non-deterministic problem. In this article

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Dumas, Jonathan, Antoine Wehenkel, Damien Lanaspeze, Bertrand Cornélusse, and Antonio Sutera. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows." Applied Energy 305 (January 2022): 117871. http://dx.doi.org/10.1016/j.apenergy.2021.117871.

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6

Amaya-Gómez, Rafael, Jorge López, Hugo Pineda, et al. "Probabilistic approach of a flow pattern map for horizontal, vertical, and inclined pipes." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 74 (2019): 67. http://dx.doi.org/10.2516/ogst/2019034.

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A way to predict two-phase liquid-gas flow patterns is presented for horizontal, vertical and inclined pipes. A set of experimental data (7702 points, distributed among 22 authors) and a set of synthetic data generated using OLGA Multiphase Toolkit v.7.3.3 (59 674 points) were gathered. A filtering process based on the experimental void fraction was proposed. Moreover, a classification of the pattern flows based on a supervised classification and a probabilistic flow pattern map is proposed based on a Bayesian approach using four pattern flows: Segregated Flow, Annular Flow, Intermittent Flow,

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Zhang, Chuan Cheng, Cui Hui Yan, Sai Dai, Dan Xu, Yi Zhu, and Wei Dong. "Improved Probabilistic Load Flow Method to Consider Random Generator Outages." Advanced Materials Research 962-965 (June 2014): 2783–88. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2783.

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In conventional cumulant method of probabilistic load flow (PLF), random generator outages are usually simulated by discrete distributions of nodal power injections, but that will lead to significant error in AC load flow model. An improved PLF method base on AC model is proposed in this paper, which considers random generator outages and loads uncertainties. Cumulant and Gram-Charlier series expansion were applied to deal with the random variations of loads, instead of convolution calculations. According to the characteristics and focused aspects of power grid, certain generators were selecte

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Ye, En Li, and Yi Hong Zhou. "Orthogonal Expansion Method of Random Processes for Fluctuating Pressure of Water Flow." Applied Mechanics and Materials 459 (October 2013): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amm.459.619.

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Combining with its auto-correlation function, the stochastic process of water flows fluctuating pressure is decomposed on the trigonometric bases by employing an expansion method based on normalized orthogonal bases which is prescribed, and thus establish an incentive model for the random dynamic response analysis of structures vibrated by water flows fluctuating pressure. By using the model, main probabilistic characters of the flows stochastic process are captured with only a few random variables, and therefore laid the foundation for further random response and reliability analysis from the

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9

Huang, Yu, Qingshan Xu, and Guang Lin. "Congestion Risk-Averse Stochastic Unit Commitment with Transmission Reserves in Wind-Thermal Power Systems." Applied Sciences 8, no. 10 (2018): 1726. http://dx.doi.org/10.3390/app8101726.

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The great proliferation of wind power generation has brought about great challenges to power system operations. To mitigate the ramifications of wind power uncertainty on operational reliability, predictive scheduling of generation and transmission resources is required in the day-ahead and real-time markets. In this regard, this paper presents a risk-averse stochastic unit commitment model that incorporates transmission reserves to flexibly manage uncertainty-induced congestion. In this two-settlement market framework, the key statistical features of line flows are extracted using a high-dime

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10

Bondar, Oleh, Mikola Kostin, Andrei Mukha, Olha Sheikina, and Svitlana Levytska. "Fryze reactive power of trams in effective stochastic recuperation processes." MATEC Web of Conferences 294 (2019): 01006. http://dx.doi.org/10.1051/matecconf/201929401006.

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Urban electric transport system, particularly tram systems, is not a direct current system not only in traction mode but in regenerative modes as both voltage on a collector and regenerative current are stochastic abruptly variable processes. The above- mentioned facts determine availability of Fryze’s reactive power in this system that flows from a railway substation to trams, leads to incidental losses of energy and significantly reduces its quality. So evaluation of power effectiveness of the system in electrical trams operation is impossible without determining the level of reactive power

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11

Behr, Adam, Paweł Mielcarz, and Dmytro Osiichuk. "Terminal Value Calculation in DCF Valuation Models: An Empirical Verification." e-Finanse 14, no. 1 (2018): 27–38. http://dx.doi.org/10.2478/fiqf-2018-0003.

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AbstractThe paper presents an empirical verification of the main assumptions underlying the calculation of terminal value in DCF valuation models. The test results suggest that the volatility of free cash flows and the dynamism of the operating environment do not allow us to make a reliable long-term forecast of value creation potential of the public companies in Poland. Regardless of their organic growth phase, the overwhelming majority of the sampled firms are evidenced to exhibit extreme year-on-year fluctuations of sales, investments and cash flows over the short- and medium-term observati

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12

D’Amico, Guglielmo, Giovanni Masala, Filippo Petroni, and Robert Adam Sobolewski. "Managing Wind Power Generation via Indexed Semi-Markov Model and Copula." Energies 13, no. 16 (2020): 4246. http://dx.doi.org/10.3390/en13164246.

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Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected po

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13

Yao, Ming Yi, Xing Ying Chen, Jian Liu, Kun Yu, Hao Jie Shi, and Ji Xiang Wang. "Analysis Method of Power Flow in Urban Power Grid with Distribution Generation Based on Dynamic Probability Model." Applied Mechanics and Materials 672-674 (October 2014): 1265–69. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1265.

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The change of wind power generation, photovoltaic power generation and power load reflects the regularity and randomness at the same time, which leads to a casual fluctuation of power flow in urban power grid with multiple generators. The static and dynamic probabilistic power flow model are compared and analyzed in this paper, the dynamic probabilistic model is adopted for simulating the characteristics of power flow in urban power grid with multiple DGs. The results show that the power flow of urban power grid changes not only regularly but also randomly. These two characteristics of power f

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14

Rakhmanov, N. R., V. G. Kurbatsky, H. B. Guliyev, N. V. Tomin, and R. N. Rakhmanov. "Analysis and modeling of harmonic distortions in consumer’s distribution networks, containing a powerful non-linear load." E3S Web of Conferences 58 (2018): 03015. http://dx.doi.org/10.1051/e3sconf/20185803015.

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The paper proposes to simulate a random variability of the nonlinear load in the form of probabilistic characteristics of the amplitudes of harmonic components of powers. The influence of the variability of distribution of amplitude values of the power harmonics on the load flow in the network, power losses and maximum power transmission in lines is studied. The results of design experiments for one of distribution networks of Azerbaijan power system, feeding the non-linear load, are given.

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15

Huynh, Van, Van Ngo, Dinh Le, and Nhi Nguyen. "Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources." Energies 11, no. 10 (2018): 2624. http://dx.doi.org/10.3390/en11102624.

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In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an a

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16

Shi, Fei, Yi Jun Yu, Shu Hai Feng, Jin Zhou, Peng Xu, and Li Wen Wang. "Probabilistic Load Flow Based on Cumulant Method Considering Multi-Slack Balance." Advanced Materials Research 1070-1072 (December 2014): 943–51. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.943.

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With constant improvement of intermittent energy source, its agglomeration effect and stochastic volatility will greatly influence the power system operation. It’s an inexorable trend that probabilistic power flow calculation shifts from offline analysis to real-time operation online. Among all probabilistic trend algorithms, probability flow calculation based on cumulant method is the fastest one; it has better prospect of online application. However, it requires random variables independent to each other, so the single slack bus always undertakes all unbalance power in conventional cumulant

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17

Carpenter, Chris. "Applications of Artificial Neural Networks for Seismic Facies Classification." Journal of Petroleum Technology 73, no. 02 (2021): 68–69. http://dx.doi.org/10.2118/0221-0068-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot

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18

Chen, Yue, Zhizhong Guo, Hongbo Li, et al. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources." Sustainability 12, no. 2 (2020): 518. http://dx.doi.org/10.3390/su12020518.

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With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead

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19

Sun, Li Fang, Yu Qin Xu, Li Zhang, Kang Guo, and Jian Fang Yue. "Photovoltaic Planning with Considering its Probabilistic Output in Distribution System." Advanced Materials Research 347-353 (October 2011): 481–86. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.481.

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A large number of PVs can produce security and economic influence for distribution system, for its probabilistic and intermittent output. The solar radiation which PV component receives is easily influenced by clouds. The clearness index can reflect this. So a PV output stochastic model is built based on clearness index. In this paper the probabilistic output of PVs and loads are both considered and expressed by stochastic variables. A random expectation model has been built considering annual saved environment cost. The objective function is the minimum expected value of annual investment and

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20

Sun, Guoqiang, Wenxue Wang, Yi Wu, et al. "A Nonlinear Analytical Algorithm for Predicting the Probabilistic Mass Flow of a Radial District Heating Network." Energies 12, no. 7 (2019): 1215. http://dx.doi.org/10.3390/en12071215.

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This paper develops a nonlinear analytical algorithm for predicting the probabilistic mass flow of radial district heating networks based on the principle of heat transfer and basic pipe network theory. The use of a nonlinear mass flow model provides more accurate probabilistic operation information for district heating networks with stochastic heat demands than existing probabilistic power flow analytical algorithms based on a linear mass flow model. Moreover, the computation is efficient because our approach does not require repeated nonlinear mass flow calculations. Test results on a 23-nod

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21

Kim, Sunoh, and Jin Hur. "Probabilistic Approaches to the Security Analysis of Smart Grid with High Wind Penetration: The Case of Jeju Island’s Power Grids." Energies 13, no. 21 (2020): 5785. http://dx.doi.org/10.3390/en13215785.

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As the importance of renewable generating resources has grown around the world, South Korea is also trying to expand the proportion of renewable generating resources in the power generation sector. Among the various renewable energy sources, wind generating resources are emerging as a key alternative to conventional power generations in the electricity sector in Korea accounted for 17.7 GW of total capacity by 2030. As wind generating resources are gradually replacing traditional generating resources, the system security and reliability are negatively affected because of the variability, due t

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22

Hutterer, Stephan, and Michael Affenzeller. "Probabilistic Electric Vehicle Charging Optimized With Genetic Algorithms and a Two-Stage Sampling Scheme." International Journal of Energy Optimization and Engineering 2, no. 3 (2013): 1–15. http://dx.doi.org/10.4018/ijeoe.2013070101.

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Probabilistic power flow studies represent essential challenges in nowadays power system operation and research. Here, especially the incorporation of intermittent supply plants with optimal control of dispatchable demand like electric vehicle charging power shows nondeterministic aspects. Using simulation-based optimization, such probabilistic and dynamic behavior can be fully integrated within the metaheuristic optimization process, yielding into a generic approach suitable for optimization in uncertain environments. A practical problem scenario is demonstrated that computes optimal charging

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23

Ding, Jiaman, Zhixin Chen, and Yi Du. "Probability box theory-based uncertain power flow calculation for power system with wind power." International Journal of Emerging Electric Power Systems 22, no. 2 (2021): 243–53. http://dx.doi.org/10.1515/ijeeps-2020-0227.

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Abstract The uncertainty of wind speed may lead to the deviation and change of wind power output, which influences the stability of wind farm. Therefore, in this paper, a probability box (p-box) based uncertain power flow model for wind power is proposed, which initially introduces p-box to power flow calculation. A probabilistic interval power flow model with both probability and interval is established. Firstly, the drift interval of wind speed is obtained and its p-box model is established by analyzing the distribution of wind speed. Secondly, the wind power output p-box is derived from the

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24

Chen, Wei Hua, and Yan Wei Chen. "Power System Control Considering Line Flow Risk." Applied Mechanics and Materials 687-691 (November 2014): 3203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3203.

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Line flow is one of the key factors which dominate the power system security. To deal with this problem, this paper proposed a method of power system assessment and control based on power system Line flow risk. Probabilistic power flow (PPF) is used to analyze the random of system statuses. The severity function is used to describe the impact of the transmission line load. Line flow risk is defined as the result of the probability multiplying the severity of Line flow. It is an indicator of power system security. This reduces the system line flow risk and increases the system security.

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25

Li, Hang, Zhe Zhang, Xianggen Yin, and Buhan Zhang. "Preventive Security-Constrained Optimal Power Flow with Probabilistic Guarantees." Energies 13, no. 9 (2020): 2344. http://dx.doi.org/10.3390/en13092344.

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The traditional security-constrained optimal power flow (SCOPF) model under the classical N-1 criterion is implemented in the power industry to ensure the secure operation of a power system. However, with increasing uncertainties from renewable energy sources (RES) and loads, the existing SCOPF model has difficulty meeting the practical requirements of the industry. This paper proposed a novel chance-constrained preventive SCOPF model that considers the uncertainty of power injections, including RES and load, and contingency probability. The chance constraint is used to constrain the overall l

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26

Chen, Min Jiang, Yue Qing Chen, Wang Chao Dong, and Bei Wu. "Containing Wind Farm Power System Probabilistic Optimal Power Flow Calculation Using the Method of Combined Cumulants and Gram-Charlier Expansion." Advanced Materials Research 1070-1072 (December 2014): 193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.193.

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This paper uses the optimal probabilistic load flow method for power containing wind farm analysis. Based on Computation of optimal load flow using the Interior point method ,considering the stochasticlal power output of wind generator and the random outage of synchronous generator and the stochastic of load power, calculating the probability distribution of branch power flow and node voltage. This paper uses RTS-24 as the example to analysis the method ,and comparison the results with that of the Monte-Carlo method, to analysis the change of power system after the grid connected of wind turbi

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27

Xu, Nan, Shan Shan Li, and Hao Ming Liu. "Distribution System Fault Recovery with Undispatchable Distributed Generations." Applied Mechanics and Materials 529 (June 2014): 455–59. http://dx.doi.org/10.4028/www.scientific.net/amm.529.455.

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Considering the probabilistic of the wind power and the solar power, a fault recovery method for distribution systems with the wind power and the solar power is presented in this paper. For the wind power, a simplified steady-state equivalent model of an asynchronous wind generator is added into the Jacobian matrix to consider the impact of the wind power on systems. For the solar power, its output is considered as an injected power which is related with solar irradiance. Three-point estimate is employed to solve the probabilistic power flow of distribution systems with the wind power and the

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28

Sivakumar, P., and D. Poornima. "Uncertainty Modelled Power Flow Analysis for DG Sourced Power Systems." Advanced Materials Research 768 (September 2013): 298–300. http://dx.doi.org/10.4028/www.scientific.net/amr.768.298.

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For growing of electrical demand in the modern world energy requirement is tremendously increased day to day power market. Nowadays the non-conventional energy sources are utilized to meet out the current power demand through PV, wind and other non-conventional resources etc. In this concern the energy drawn from the other non-conventional energy sources is highly variable due to the nature of uncertainties. Hence the optimal load dispatch of the power is highly difficult, one of the attempts is to eradicate this difficulty by adding developed uncertainty model of PV and wind sourced power gen

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29

Wu, Jie, and Bing Chao Yan. "Probabilistic Harmonic Load Flow Calculation Containing Doubly Fed Induction Generator." Advanced Materials Research 383-390 (November 2011): 5928–35. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5928.

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In this paper, based on the analysis of the basic electromagnetic relation for the doubly fed induction generator (DFIG), the fundamental and harmonic equivalent circuits are presented. The harmonic propagation from the stator to the rotor side and in sub-synchronous mode and super-synchronous mode is explained. The model of probabilistic harmonic load flow equivalent circuits for DFIG is presented. Then, the influence of the harmonic from DFIG to a small power system are analysed. Finally, according to the harmonic characteristics of wind turbine, the concept of the probability density functi

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30

Yang, Qi, Meng Cong Liu, Ying Chen, and Yun Xiao Bai. "Distributed Network Power Flow of Distributed Generation." Advanced Materials Research 614-615 (December 2012): 1693–99. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1693.

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With constrains imposed by natural conditions, the power generated from wind power, solar power and other intermittent energy system has great randomness and volatility. Thus, the impact from the intermittent energy to the system voltage and power flow is uncertainty. In view of this problem, this paper studies the characteristics of these double random variables and correlation. By using the typical probabilistic load flow and Monte Carlo simulation method, this paper poses the probability load flow adapted to the intermittent distributed generation. Finally, taking IEEE33 distribution system

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31

Li, Zhong Cheng, Bu Han Zhang, Ye Bin Ge, et al. "Probabilistic Available Transfer Capability Calculation of Wind Farm Incorporated Power System." Advanced Materials Research 724-725 (August 2013): 582–86. http://dx.doi.org/10.4028/www.scientific.net/amr.724-725.582.

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The paper mainly studies the available transfer capability of wind farm incorporated power system, and proposes an on-line calculating method considering many uncertain factors. First based on continuation power flow, an improved algorithm of the key constraint by linear prediction is proposed so as to obtain deterministic ATC with the expansion power flow equation. Then Monte Carlo simulation is used which takes many uncertain factors into considerations, such as wind speed, the random fault of generators and lines, the fluctuation of generators and load, etc. With hierarchical clustering alg

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32

Yokoyama, Ryohei, Koichi Ito, Mitsukuni Sakashita, Yoshikazu Matsumoto, and Yoshiro Yuasa. "Multiobjective Optimal Unit Sizing of a Grid-Connected Photovoltaic System in Consideration of Its Probabilistic Characteristics." Journal of Solar Energy Engineering 119, no. 2 (1997): 134–40. http://dx.doi.org/10.1115/1.2887892.

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A probabilistic approach is presented to optimal unit sizing of a grid-connected photovoltaic system without storage batteries. In consideration of probabilistic characteristics of solar insolation and electricity demand, the surface area of photovoltaic array, capacity of receiving device, and electric contract demand are determined so as to minimize the expected values of annual total cost and annual energy consumption subject to the annual loss of power supply probability. This optimization problem is considered as a multiobjective one, and a discrete set of Pareto optimal solutions is deri

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Luo, Feng Ji, Zhao Yang Dong, Can Wan, Ying Ying Chen, Ke Meng, and Kit Po Wong. "Applying Computational Grid Technology to Power System." Applied Mechanics and Materials 58-60 (June 2011): 1442–47. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1442.

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This paper proposes a computational grid platform for solving the large-scale power system applications. The platform is based on Globus Toolkit middleware and GridWay meta-scheduler. It can enable large-scale sharing of computational resources across institutional boundary. This paper first discusses the architecture and each component of the platform, and then the test bed is described. Finally, the test results of probabilistic load flow (PLF) by Monte-Carlo simulation are presented. The test results show that the computational Grid system can provide comparable performance.

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34

Oh, HyungSeon. "A Unified and Efficient Approach to Power Flow Analysis." Energies 12, no. 12 (2019): 2425. http://dx.doi.org/10.3390/en12122425.

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Highly nonlinear and nonconvex power flow analysis plays a key role in the monitoring, control, and operation of power systems. There is no analytic solution to power flow problems, and therefore, finding a numerical solution is oftentimes an aim of modern computation in power system analysis. An iterative Newton-Raphson method is widely in use. While most times this method finds a solution in a reasonable time, it often involves numerical robustness issues, such as a limited convergence region and an ill-conditioned system. Sometimes, the truncation error may not be small enough to ignore, wh

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35

Deng, Wei Si, Bu Han Zhang, Hong Fa Ding, et al. "A Probabilistic Load Flow Method Based on Markov Chain Wind Power Prediction." Advanced Materials Research 953-954 (June 2014): 561–64. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.561.

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With the wind power installed capacity increased, it brings great challenges to the stable operation of the power grid. In order to take consideration of the wind power impact, this paper proposes a wind power variability model applied to the probabilistic load flow calculation using the method of combined cumulants and improved Von-Mises Expansion. This wind power variability model combines the concept of Markov chain process and scenario tree theory, taking in to account the relationships of the outputs of wind turbines at adjacent moment by considering the coupling interaction at different

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36

Banerjee, Sriparna, Dhiman Banerjee, Provas Kumar Roy, Pradip Kumar Saha, and Goutam Kumar Panda. "A Probabilistic Optimal Power Flow in Wind-Thermal Coordination Considering Intermittency of the Wind." International Journal of Energy Optimization and Engineering 10, no. 1 (2021): 82–110. http://dx.doi.org/10.4018/ijeoe.2021010105.

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This article specifically aims to prove the superiority of the proposed moth swarm algorithm (MSA) in view of wind-thermal coordination. In the present article, a probabilistic optimal power flow (POPF) problem is formulated to reflect the probabilistic nature of wind. Modelling of doubly fed induction generator (DFIG) is included in the proposed POPF to represent the wind energy conversion system (WECS). To reduce DFIG imposed deviation of bus voltage ancillary reactive power support is considered. Moreover, three different optimization techniques, namely, MSA, biogeography-based optimization

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37

Aien, M., R. Ramezani, and S. Mohsen Ghavami. "Probabilistic Load Flow Considering Wind Generation Uncertainty." Engineering, Technology & Applied Science Research 1, no. 5 (2011): 126–32. http://dx.doi.org/10.48084/etasr.64.

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Renewable energy sources, such as wind, solar and hydro, are increasingly incorporated into power grids, as a direct consequence of energy and environmental issues. These types of energies are variable and intermittent by nature and their exploitation introduces uncertainties into the power grid. Therefore, probabilistic analysis of the system performance is of significant interest. This paper describes a new approach to Probabilistic Load Flow (PLF) by modifying the Two Point Estimation Method (2PEM) to cover some drawbacks of other currently used methods. The proposed method is examined usin

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Yao, Jia, Yujia Huang, and Jingwei Hu. "Static Stability Analysis Based on Probabilistic Power Flow Calculation considering P2G Technology." Complexity 2021 (March 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/5536294.

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At present, integrated energy systems have received extensive attention, but there is no basic framework for stability analysis of coupled systems. The injection of a large amount of renewable energy also has a great impact on the stability of the system. This paper focuses on how to analyze the static stability of the coupling system with uncertainty, which mainly considers the uncertainty of wind power generation and photovoltaic power generation and also considers the influence of P2G technology on the whole system. Firstly, this paper analyzes the principles of wind power generation and ph

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Zhao, Jin Quan, Chen Lu Zhang, Wei Hua Luo, and Jun Zhao. "A Probabilistic Optimal Power Flow Calculation Method with Latin Hypercube Sampling." Advanced Materials Research 918 (April 2014): 183–90. http://dx.doi.org/10.4028/www.scientific.net/amr.918.183.

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Among the solving methods of probabilistic optimal power flow (P-OPF), Monte Carlo Simulation (MCS) combined with random sampling (RS) is widely used due to its high accuracy. In order to further improve that, this paper proposes a way of using Monte Carlo Simulation with Latin hypercube sampling (LHS) to calculate the consumption of generating cost under many random variables. Numerical results of IEEE 14-bus and IEEE 118-bus systems show that the Latin hypercube sampling method provides more accurate performance in dealing with POPF under the condition of a smaller sample size, comparing wit

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Le, Duong Dinh, Duong Van Ngo, Nhi Thi Ai Nguyen, and Ky Van Huynh. "Probabilistic Assessment of Power Systems with Renewable Energy Sources based on an Improved Analytical Approach." International Journal of Renewable Energy Development 10, no. 4 (2021): 811–18. http://dx.doi.org/10.14710/ijred.2021.38226.

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The increasing penetration of renewable energy sources has introduced great uncertainties and challenges into computation and analysis of electric power systems. To deal with uncertainties, probabilistic approaches need to be used. In this paper, we propose a new framework for probabilistic assessment of power systems taking into account uncertainties from input random variables such as load demands and renewable energy sources. It is based on the cumulant-based Probabilistic Power Flow (PPF) in combination with an improved clustering technique. The improved clustering technique is also develo

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Nogueira, Wallisson C., Lina Paola Garcés Negrete, and Jesús M. López-Lezama. "Interval Load Flow for Uncertainty Consideration in Power Systems Analysis." Energies 14, no. 3 (2021): 642. http://dx.doi.org/10.3390/en14030642.

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Modern power systems must deal with a greater degree of uncertainty in power flow calculation due to variations in load and generation introduced by new technologies. This scenario poses new challenges to power system operators which require new tools for an accurate assessment of the system state. This paper presents an interval load flow (ILF) approach for dealing with uncertainty in power system analysis. A probabilistic load flow (PLF), based on Monte Carlo Simulation (MCS), was also implemented for comparative purposes. The ILF and PLF are used to estimate the network states. Both methods

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Li, Quan, Xin Wang, and Shuaiang Rong. "Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling." Energies 11, no. 11 (2018): 3171. http://dx.doi.org/10.3390/en11113171.

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The growing amount of distributed generation has brought great uncertainty to power grids. Traditional probabilistic load flow (PLF) algorithms, such as the Monte-Carlo method (MCM), can no longer meet the needs of efficiency and accuracy in large-scale power grids. Latin Hypercube Sampling (LHS) develops a sampling efficiency and solves the correlation problem of distributed generation (DG) access nodes for accuracy analyses. In this paper, a modified Latin Hypercube-Important Sampling method is proposed for higher efficiency and precision by using the importance sampling method before LHS an

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Groth, Katrina M., Matthew R. Denman, Michael C. Darling, Thomas B. Jones, and George F. Luger. "Building and using dynamic risk-informed diagnosis procedures for complex system accidents." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (2018): 193–207. http://dx.doi.org/10.1177/1748006x18803836.

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Accidents pose unique challenges for operating crews in complex systems such as nuclear power plants, presenting limitations in plant status information and lack of detailed monitoring, diagnosis, and response planning support. Advances in severe accident simulation and dynamic probabilistic risk assessment provide an opportunity to garner detailed insight into accident scenarios. In this article, we demonstrate how to build and use a framework which leverages dynamic probabilistic risk assessment, simulation, and dynamic Bayesian networks to provide real-time monitoring and diagnostic support

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Yeh, Harry, and Shinji Sato. "Special Issue on Uncertainties in Tsunami Effects." Journal of Disaster Research 11, no. 4 (2016): 613–14. http://dx.doi.org/10.20965/jdr.2016.p0613.

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The 2011 Heisei tsunami far exceeded the level previously anticipated, resulting in devastating impacts in Japan. This event made it clear that preparation for tsunami hazards, based on past historical data alone, is inadequate. It is because tsunami hazards are characterized by a lack of historical data – due to the fact tsunamis are rare, high impact phenomena. Hence, it is important to populate a dataset with more data by including events that might have occurred outside the recorded historical timeframe, such as those inferred from geologic evidence. The dataset can also be expanded with “

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VENU, V. VIJAY, and A. K. VERMA. "REINFORCEMENT OF POWER SYSTEM RELIABILITY MEASURES THROUGH JOINT DETERMINISTIC AND PROBABILISTIC APPROACHES." International Journal of Reliability, Quality and Safety Engineering 16, no. 06 (2009): 551–66. http://dx.doi.org/10.1142/s0218539309003575.

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By adapting the emerging conceptualization of reliability network equivalents, originally developed to obtain reliability indices in the restructured scenario, a suitable framework encapsulating relatively independent modularity could be evolved that shall enable the application of joint deterministic- probabilistic well being-analysis for the composite restructured power systems. Reliability management forms the core component of successful transition from vertical integration of power systems to their deregulation. Timely reinforcements to the resource adequacy will ensure reliably operating

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Filipovska, Monika, and Hani S. Mahmassani. "Traffic Flow Breakdown Prediction using Machine Learning Approaches." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (2020): 560–70. http://dx.doi.org/10.1177/0361198120934480.

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Traffic flow breakdown is the abrupt shift from operation at free-flow conditions to congested conditions and is typically the result of complex interactions in traffic dynamics. Because of its stochastic nature, breakdown is commonly predicted only in a probabilistic manner. This paper focuses on using stationary aggregated traffic data to capture traffic dynamics, developing and testing machine learning (ML) approaches for traffic breakdown prediction and comparing them with the traditionally used probabilistic approaches. The contribution of this study is three-fold: it explores the usefuln

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Xiao, Qing, and Shaowu Zhou. "Comparing unscented transformation and point estimate method for probabilistic power flow computation." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 3 (2018): 1290–303. http://dx.doi.org/10.1108/compel-09-2017-0393.

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Purpose Unscented transformation (UT) and point estimate method (PEM) are two efficient algorithms for probabilistic power flow (PPF) computation. This paper aims to show the relevance between UT and PEM and to derive a rule to determine the accuracy controlling parameters for UT method. Design/methodology/approach The authors derive the underlying sampling strategies of UT and PEM and check them in different probability spaces, where quadrature nodes are selected. Findings Gauss-type quadrature rule can be used to determine the accuracy controlling parameters of UT. If UT method and PEM selec

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Wang, Jianxue, Yanlin Cui, and Minghui Zhu. "Probabilistic Harmonic Calculation in Distribution Networks with Electric Vehicle Charging Stations." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/167565.

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Integrating EV charging station into power grid will bring impacts on power system, among which the most significant one is the harmonic pollution on distribution networks. Due to the uncertainty of the EV charging process, the harmonic currents brought by EV charging stations have a random nature. This paper proposed a mathematical simulation method for studying the working status of charging stations, which considers influencing factors including random leaving factor, electricity price, and waiting time. Based on the proposed simulation method, the probability distribution of the harmonic c

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Zolfaghari, Saeed Zolfaghari, Mohammad Hassan Hashemi, and Azam Zolfaghari. "Reserve Management in Restructured Power System Considering Loads Reliability." European Journal of Engineering Research and Science 2, no. 12 (2017): 34. http://dx.doi.org/10.24018/ejers.2017.2.12.531.

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Determining the spinning reserve capacity is amongst the most important tasks of system operator to insure safe and reliable operation of power system. So far, various methods are proposed to determine reserve capacity which are based on deterministic or probabilistic criteria. Due to considering the stochastic nature of system events, probabilistic approaches are more preferable in comparison to deterministic approaches. In practice, the value of interrupted loads are not the same for all consumers and some of them like industrial ones have higher value of lost load (VOLL). Thus, consumes wit

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Qin, Fang, Ma, and Li. "Probabilistic Energy Flow Calculation through the Nataf Transformation and Point Estimation." Applied Sciences 9, no. 16 (2019): 3291. http://dx.doi.org/10.3390/app9163291.

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With the increasing capacity of renewable energy sources, uncertainties regarding renewable energy and other dynamic loads in integrated energy systems (IESs) are increasing. Thus, it is necessary to study the probabilistic energy flow (PEF) of IESs. However, existing PEF calculation methods such as the point estimate method (PEM) are computationally inefficient when there are many random variables and estimated points; moreover, relatively large errors can occur when the estimated points are outside their limits. Hence, this paper presents a calculation method that addresses these problems. B

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