Academic literature on the topic 'Probabilistic Power Flows'
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Journal articles on the topic "Probabilistic Power Flows"
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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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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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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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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, we propose a planning procedure that takes the probabilistic elements into account. This procedure was developed to take into account the high variability of power flows caused by the generation of renewable sources and international exchange. Such conditions of the power system operation forced a departure from deterministic planning. The new probabilistic approach uses the existing tools and experience gained during subsequent development projects. As part of the probabilistic approach, simulations were carried out using the Latin Hypercube Sampling and Two Point Estimation Method algorithms. These methods effectively reduce the computation time and, at the same time, give satisfactory results. The verification was carried out on a test grid model developed in accordance with the technical standards used in the Polish Power System. Effects were assessed using a deterministic and probabilistic approach. This analysis confirmed the practical possibility of using the probabilistic approach in planning the development of transmission network in Poland. When using a probabilistic approach to predict power flow, the criteria of technical acceptability for a given development variant and the manner in which the strategy is determined are of particular importance.
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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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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, and Bubble Flow. A new visualization technique for flow pattern maps is proposed to understand the transition zones among flow patterns and provide further information than the flow pattern map boundaries reported in the literature. Following the methodology proposed in this approach, probabilistic flow pattern maps are obtained for oil–water pipes. These maps were determined using an experimental dataset of 11 071 records distributed among 53 authors and a numerical filter with the water cut reported by OLGA Multiphase Toolkit v7.3.3.
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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 selected to form event group of generator outages and each event was analysed by exact load flow.Then total probability theorem was introduced to obtain the probabilistic distributions of node voltages and line flows that considered random factors of loads and generators.The case study of IEEE 39-bus system shows that the random generator outages remarkably affect the probabilistic distributions of state variables. The proposed method can avoid the error caused by generator outages in conventional cumulant method. Furthermore, the result of proposed method is consistent with that of Monte Carlo simulation, while computation speed is much faster.
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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 point of probability density evolution. By using it in an instance, the validity of the model is tested from the aspect of second order statistics such as sampling ensemble power spectrum and sample mean square error. The effects of simulation duration and expansion numbers needed on the simulations efficiency and accuracy are also discussed.
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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-dimensional probabilistic collocation method in the real-time dispatch, for which the spatial correlation between wind farms is also considered. These features are then used to quantify transmission reserve requirements in the transmission constraints at the day-ahead stage. Comparative studies on the IEEE 57-bus system demonstrate that the proposed method outperforms the conventional unit commitment (UC) to enhance the system reliability with wind power integration while leading to more cost-effective operations.
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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 in this system. We have analytical expression of reactive power by Fryze. Numerical calculations for trams type T3D and T4D in regenerative braking modes are done. Probabilistic statistical data processing operation of reactive power expressions is done. It is determined that reactive power changes in the limit of 10…100 kilo-volt ampere reactive with mathematical expectation – 37,0 kilo-volt ampere reactive. Statistical allocation of random power values are different. Numerical calculations of incidental losses, energy of recuperation are done and they range supplementary – 20% from total losses. It is stated that coefficient of reactive power of system route of trams is exceeding permissible value 0,25.
Dissertations / Theses on the topic "Probabilistic Power Flows"
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Valverde, Mora Gustavo Adolfo. "Uncertainty and state estimation of power systems." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/uncertainty-and-state-estimation-of-power-systems(18c48a22-7ea2-4db2-9112-078a1eac6fe7).html.
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The evolving complexity of electric power systems with higher levels of uncertainties is a new challenge faced by system operators. Therefore, new methods for power system prediction, monitoring and state estimation are relevant for the efficient exploitation of renewable energy sources and the secure operation of network assets. In order to estimate all possible operating conditions of power systems, this Thesis proposes the use of Gaussian mixture models to represent non-Gaussian correlated input variables, such as wind power output or aggregated load demands in the probabilistic load flow problem. The formulation, based on multiple Weighted Least Square runs, is also extended to monitor distribution radial networks where the uncertainty of these networks is aggravated by the lack of sufficient real-time measurements. This research also explores reduction techniques to limit the computational demands of the probabilistic load flow and it assesses the impact of the reductions on the resulting probability density functions of power flows and bus voltages. The development of synchronised measurement technology to support monitoring of electric power systems in real-time is also studied in this work. The Thesis presents and compares different formulations for incorporating conventional and synchronised measurements in the state estimation problem. As a result of the study, a new hybrid constrained state estimator is proposed. This constrained formulation makes it possible to take advantage of the information from synchronised phasor measurements of branch currents and bus voltages in polar form. Additionally, the study is extended to assess the advantages of PMU measurements in multi-area state estimators and it explores a new algorithm that minimises the data exchange between local area state estimators. Finally, this research work also presents the advantages of dynamic state estimators supported by Synchronised Measurement Technology. The dynamic state estimator is compared with the static approach in terms of accuracy and performance during sudden changes of states and the presence of bad data. All formulations presented in this Thesis were validated in different IEEE test systems.
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ARIENTI, VINICIUS LEAL. "PROBABILISTIC POWER FLOW: THEORY AND APPLICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1990. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8479@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Um algoritmo de Fluxo de Potência Probabilístico (FPP)
permite avaliar a probabilidade de ocorrência de certos
eventos em um sistema de potência, baseado em seu
desempenho passado, na previsão das demandas e nas
disponibilidades das unidades geradoras e equipamentos de
transmissão. Tais eventos são sobrecargas,
sobre/subtensões e insuficiência de geração de potência
ativa/reativa. As probabilidades ou riscos associados a
tais eventos são medidas ou índices de adequação
relacionando cargas, equipamentos disponíveis e a política
operativa.
Pode-se dizer que a formulação de FPP mais
realista até hoje proposta possui duas restrições básicas.
A primeira é a dificuldade de se obter simultaneamente e
de forma eficiente, todos os índices de adequação. A
segunda está relacionada com a precisão do algoritmo para
as grandezas reativas, mesmo para níveis de incerteza não
muito elevados.
Neste trabalho, além de ser proposto um novo
algoritmo de solução que resolve as restrições
anteriormente citadas de forma bastante simples e eficaz,
são demonstradas diversas aplicações dos algoritmos de FPP
e também definidas metodologias de utilização desta
ferramenta em estudos de planejamento da operação e da
expansão, ressaltando-se ainda as similaridades com a
análise de confiabilidade global de sistemas de potência.
A aplicação dos métodos de FPP, obtendo
informações adicionais para complementar a abordagem
determinística convencional, tem demonstrado ser uma
ferramenta auxiliar bastente útil no processo de tomada de
decisão. As vantagens da inclusão deste algoritmo no rol
de técnicas imprescindíveis aos engenheiros vem
incentivando o uso desta abordagem de maneira mais ampla.<br>A Probabilistic Power Flow Algorithm (PPF) allows the
practical evaluation of the probability of occurrence of
power system events, based on their historical
performance, load forecast and availability of generating
units and transmission equipment. Such events are
overlads, undervoltages, overvoltages and indufficiency of
active/reactive power generation. The probabilities or
risks associated to these events are adequacy measures, or
indices, linking loads, available equipment and
operational policies.
The practical PPF formulations available in literature
present two main drawbacks. The first one is related with
the computational difficulty in simultaneously obtaining
all basic adequacy indices, within an efficient
computational scheme. The second is related with the
accuracy of calculated reactive figures, even in the case
of modest uncertainty levels.
In this work, a new, simple and efficient PPF
algorithm, which alleviates the previous drawbacks, is
proposed. Several practical applications of the proposed
PPF algorithm are described as well as methodologies for
its use in operational and expansion planning. Also, the
similarities between PPF and composite reliability
evaluation algorithms are emphasized.
The application of PPF methods, providing
additional information to the conventional deterministic
approaches, has shown to be a very important auxiliary
tool in the decision making process, thus contributing for
the broad use of probabilistic methods.
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RIBEIRO, SOLANGE MARIA PINTO. "APPLICATION OF PROBABILISTIC LOAD FLOW THE EXPANSION PLANNING OF POWER SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1990. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9468@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação apresenta uma aplicação das técnicas de
Fluxo de Potência Probabilístico (FPP) no planejamento da
expansão de sistemas de potência. Um breve resumo da
formulação e solução do problema de FPP é incluído para
identificar as diferenças entre esta técnica e outras
ferramentas disponíveis tais como os algoritmos de
avaliação da confiabilidade composta - geração e
transmissão. O potencial das técnicas de FPP será
demonstrado através de um estudo utilizando o Sistema
Norte/Nordeste brasileiro. O planejamento da expansão da
rede elétrica de uma área deste sistema, obtido através de
um algoritmo convencional de fluxo de potência, é
comparado com aquele obtido por um programa de FPP que
modela as indisponibilidades de capacidade de geração, bem
como as incertezas existentes nos picos de cargas. As
diferenças significativas demonstram os benefícios das
técnicas de FPP. Uma ênfase especial é dada à modelagem de
curto e longo prazos das incertezas das cargas.<br>This dissertation presents an application of Probabilistic
Load Flow (PFL) techniques to the expansion planning of
power systems. A brief review of the PFL formulation and
solution is included to identify differences between this
technique and other available tools such as composite
generation and transmission reliability evaluation
algorithms. The potencial of the PLF technique is
demonstrated by a case study using the Brazilian
North/Northeastern system. The network expansion planning
of an area of this system is studied using a conventional
load flow program and the results compared with those
obtained from a PLF program that models generation
capacity unavailabilities and peak load uncertainties. The
significant diffrences demonstrate the benefits of the PLF
technique. Special emphasis is given to short and long
term modeling using analyses of real system load data.
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Hamon, Camille. "Probabilistic security management for power system operations with large amounts of wind power." Doctoral thesis, KTH, Elektriska energisystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166398.
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Power systems are critical infrastructures for the society. They are therefore planned and operated to provide a reliable eletricity delivery. The set of tools and methods to do so are gathered under security management and are designed to ensure that all operating constraints are fulfilled at all times. During the past decade, raising awareness about issues such as climate change, depletion of fossil fuels and energy security has triggered large investments in wind power. The limited predictability of wind power, in the form of forecast errors, pose a number of challenges for integrating wind power in power systems. This limited predictability increases the uncertainty already existing in power systems in the form of random occurrences of contingencies and load forecast errors. It is widely acknowledged that this added uncertainty due to wind power and other variable renewable energy sources will require new tools for security management as the penetration levels of these energy sources become significant. In this thesis, a set of tools for security management under uncertainty is developed. The key novelty in the proposed tools is that they build upon probabilistic descriptions, in terms of distribution functions, of the uncertainty. By considering the distribution functions of the uncertainty, the proposed tools can consider all possible future operating conditions captured in the probabilistic forecasts, as well as the likeliness of these operating conditions. By contrast, today's tools are based on the deterministic N-1 criterion that only considers one future operating condition and disregards its likelihood. Given a list of contingencies selected by the system operator and probabilitistic forecasts for the load and wind power, an operating risk is defined in this thesis as the sum of the probabilities of the pre- and post-contingency violations of the operating constraints, weighted by the probability of occurrence of the contingencies. For security assessment, this thesis proposes efficient Monte-Carlo methods to estimate the operating risk. Importance sampling is used to substantially reduce the computational time. In addition, sample-free analytical approximations are developed to quickly estimate the operating risk. For security enhancement, the analytical approximations are further embedded in an optimization problem that aims at obtaining the cheapest generation re-dispatch that ensures that the operating risk remains below a certain threshold. The proposed tools build upon approximations, developed in this thesis, of the stable feasible domain where all operating constraints are fulfilled.<br><p>QC 20150508</p>
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Long, Chao. "Voltage performance in residential distribution networks with small wind turbines and battery electric vehicles, through probabilistic power flow analysis." Thesis, Glasgow Caledonian University, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654762.
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Future electrical low voltage (LV) distribution networks are expected to have higher penetration of distributed generation (DG) systems, e.g. small wind turbines (SWTs), and battery electric vehicles (BEVs). The intermittent and time-varying characteristics of wind speed and BEV charging bring difficulties in evaluating the adverse performance, e.g. voltage violation and unbalance, on the residential distribution networks (RDNs). This thesis develops two probabilistic power flow methods, i.e. statistical time series (STS) and point estimate method (PEM), for evaluating voltage violation and voltage unbalance in RDNs caused by integration of SWTs and BEVs. The STS method combines statistical distribution analysis (SDA) and time series analysis (TSA). PEM is an approximation method using deterministic routines for solving probabilistic problems. The STS supports the Distribution Network Operators (DNOs) to obtain daily probability of voltage violations in RDNs, considering the time varying characteristics of network load, wind speed and BEV charging in a statistical manner. In PEM, evaluating the voltage unbalance takes into account the disparity of the loads at the three phases and also the unbalanced distribution of SWT outputs. The PEM calculation can also obtain daily probability of voltage unbalance factor in RDNs. The results presented prove that STS and PEM can provide faster evaluation of the probability of voltage violation and unbalance of a RDN than TSA. Based on the statistics of one year's seasonal load, wind data, at the same level of time granularity, the STS method can reduce computational power by over 98%. The assessment difference is approximately 6%. PEM evaluation, using one year's load and wind speed data, without distinguishing these data into seasonal categories or weekdays and weekends, reduces the computational power required by over 97.8%. The evaluation estimate is within 16%. The proposed methods can provide DNOs with a global picture of the voltage violation and unbalance profiles of RDNs under various SWT and BEV penetrations. For the distribution network planning, the quick evaluation of voltage violation and unbalance can help DNOs determine the maximum capacity of SWTs and BEVs a network can accommodate without voltage violation or unbalance.
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Gallego, Pareja Luis Alfonso [UNESP]. "Fluxo de potência em redes de distribuição de energia elétrica considerando incertezas." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/100319.
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gallegopareja_la_dr_ilha.pdf: 1184349 bytes, checksum: 67e1f90a3708a0564704972e31ced51c (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Nesta tese é proposta e avaliada uma metodologia alternativa para o cálculo do fluxo de potência quando são consideradas incertezas no sistema de distribuição de energia elétrica. Especificamente é considerada incerteza na demanda dos usuários de baixa tensão, assim como também nas fases em que os usuários estão ligados no sistema. A demanda das unidades consumidoras é modelada através das funções de distribuição de probabilidades. A metodologia proposta vale-se das curvas de carga diárias típicas que foram estimadas através das curvas de carga medidas em uma campanha de medição. O fluxo de potência proposto emprega o método de simulação de Monte Carlo para gerar múltiplos cenários de demanda do sistema de distribuição. O método de fluxo de potência determinístico empregado é o denominado algoritmo Backward-Forward Sweep. Neste trabalho também é realizado um estudo estatístico para determinar quais distribuições de probabilidade podem representar os dados das curvas de carga diárias obtidas na campanha de medições. Muitos trabalhos apresentados no âmbito acadêmico empregam a priori a função de distribuição de probabilidade normal para realizar os diversos estudos, isto pode levar a conclusões inadequadas. Também é realizada uma análise comparativa entre os resultados obtidos pelo fluxo de potência probabilístico, quando são utilizadas duas funções de distribuição de probabilidade diferentes para estimar as curvas de carga diárias (a função de distribuição de probabilidade que ficou no primeiro lugar na análise estatística e a função normal). São apresentados resultados comparativos para diferentes distribuições de probabilidade, quando é considerada incerteza somente na demanda e quando é considerada conjuntamente incertezas na demanda e na conexão das fases<br>In this thesis an alternative methodology to calculate the power flow considering uncertainty in the electrical distribution system is proposed and validated. Specifically, uncertainty is considered in the demand of the low voltage consumers, as well as the phases in which the users are connected to the system. The demand of the consumer units is modeled by means of probability distribution functions. The proposed methodology uses the daily load curves that were estimated by means of the load curves measured in measuring campaign. The proposed power flow uses the Monte Carlo simulation method to generate multiple demand scenarios of the distribution system. The deterministic power flow method implemented is the so called Backward-Forward Sweep algorithm. In this work it is also implemented a statistical study to determine which distribution functions can represent the data of the daily load curves obtained in the measuring campaign. Many research works found in the academic ambit use a priori the normal distribution function to perform diverse studies; this can lead to wrong conclusions. This thesis also presents a comparative analysis between the results obtained by the probabilistic power flow, when two different probability distribution functions are used to estimate the daily load curves (the probability distribution function that was first in the statistical analysis and the normal function). Comparative results are shown for different distribution functions considering uncertainty only in the demand, and considering uncertainty in the demand and the connection of the phases
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Gallego, Pareja Luis Alfonso. "Fluxo de potência em redes de distribuição de energia elétrica considerando incertezas /." Ilha Solteira : [s.n.], 2009. http://hdl.handle.net/11449/100319.
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Orientador: Antonio Padilha Feltrin<br>Banca: Percival Bueno de Araujo<br>Banca: Anna Diva Plasencia Lotufo<br>Banca: José Manuel Arroyo Sanchez<br>Banca: Paulo Augusto Nepomuceno Garcia<br>Resumo: Nesta tese é proposta e avaliada uma metodologia alternativa para o cálculo do fluxo de potência quando são consideradas incertezas no sistema de distribuição de energia elétrica. Especificamente é considerada incerteza na demanda dos usuários de baixa tensão, assim como também nas fases em que os usuários estão ligados no sistema. A demanda das unidades consumidoras é modelada através das funções de distribuição de probabilidades. A metodologia proposta vale-se das curvas de carga diárias típicas que foram estimadas através das curvas de carga medidas em uma campanha de medição. O fluxo de potência proposto emprega o método de simulação de Monte Carlo para gerar múltiplos cenários de demanda do sistema de distribuição. O método de fluxo de potência determinístico empregado é o denominado algoritmo Backward-Forward Sweep. Neste trabalho também é realizado um estudo estatístico para determinar quais distribuições de probabilidade podem representar os dados das curvas de carga diárias obtidas na campanha de medições. Muitos trabalhos apresentados no âmbito acadêmico empregam a priori a função de distribuição de probabilidade normal para realizar os diversos estudos, isto pode levar a conclusões inadequadas. Também é realizada uma análise comparativa entre os resultados obtidos pelo fluxo de potência probabilístico, quando são utilizadas duas funções de distribuição de probabilidade diferentes para estimar as curvas de carga diárias (a função de distribuição de probabilidade que ficou no primeiro lugar na análise estatística e a função normal). São apresentados resultados comparativos para diferentes distribuições de probabilidade, quando é considerada incerteza somente na demanda e quando é considerada conjuntamente incertezas na demanda e na conexão das fases<br>Abstract: In this thesis an alternative methodology to calculate the power flow considering uncertainty in the electrical distribution system is proposed and validated. Specifically, uncertainty is considered in the demand of the low voltage consumers, as well as the phases in which the users are connected to the system. The demand of the consumer units is modeled by means of probability distribution functions. The proposed methodology uses the daily load curves that were estimated by means of the load curves measured in measuring campaign. The proposed power flow uses the Monte Carlo simulation method to generate multiple demand scenarios of the distribution system. The deterministic power flow method implemented is the so called Backward-Forward Sweep algorithm. In this work it is also implemented a statistical study to determine which distribution functions can represent the data of the daily load curves obtained in the measuring campaign. Many research works found in the academic ambit use a priori the normal distribution function to perform diverse studies; this can lead to wrong conclusions. This thesis also presents a comparative analysis between the results obtained by the probabilistic power flow, when two different probability distribution functions are used to estimate the daily load curves (the probability distribution function that was first in the statistical analysis and the normal function). Comparative results are shown for different distribution functions considering uncertainty only in the demand, and considering uncertainty in the demand and the connection of the phases<br>Doutor
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Dickert, Jörg. "Synthese von Zeitreihen elektrischer Lasten basierend auf technischen und sozialen Kennzahlen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-204629.
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Kenntnisse über das prinzipielle Verhalten der Lasten und deren Benutzung durch die Endabnehmer sind im Wesentlichen vorhanden. Viele der aktuell notwendigen Untersuchungen benötigen jedoch Zeitreihen elektrischer Lasten, sogenannte Lastgänge. Mit der Synthese von Zeitreihen elektrischer Lasten können unter Berücksichtigung verschiedenster Anforderungen Lastgänge aufgebaut werden, wobei in dieser Arbeit der Fokus auf Haushaltsabnehmer liegt. Wichtige Eingangsdaten für die Lastgangsynthese sind die technischen Kenngrößen der elektrischen Geräte und die sozialen Kennzahlen zur Benutzung der Geräte durch die Endabnehmer. Anhand dieser Eingangsdaten wird die Lastgangsynthese durchgeführt und werden Anwendungsbeispiele dargestellt. Die Entwicklung von klassischen Versorgungsnetzen hin zu aktiven Verteilungsnetzen ist bedingt durch neue Verbraucher, wie Wärmepumpen, Elektroautos, sowie vielen dezentralen Erzeugungsanlagen. Speziell die fluktuierende Einspeisung durch Photovoltaik-Anlagen ist Anlass zur Forderung nach einem Verbrauchs- und Lastmanagement. Mit dem Verbrauchsmanagement wird die Last an die Einspeisung angepasst und das Lastmanagement berücksichtigt zusätzlich die Versorgungssituation des Netzes. Für die Lastgangsynthese werden die Haushaltsgeräte in fünf Geräteklassen unterteilt, für die spezifische Kennzahlen aus technischer und sozialer Sicht angegeben werden. Diese Kennzahlen sind Leistung pro Gerät oder Energieverbrauch pro Nutzung sowie Ausstattungsgrade, Benutzungshäufigkeiten und Zeiten für das Ein- und Ausschalten der Geräte. Damit wird ein neuer Ansatz gewählt, welcher nicht mehr auf die detaillierte Beschreibung des Bewohnerverhaltens beruht, da die Datenbereitstellung dafür äußerst schwierig war und ist. Vorzugsweise in Niederspannungsnetzen sind mit synthetischen Zeitreihen umfangreiche und umfassende Untersuchungen realisierbar. Es gibt verschiedenste Möglichkeiten, die Zeitreihen zusammenzustellen. Mit Lastgängen je Außenleiter können beispielsweise unsymmetrische Zustände der Netze analysiert werden. Zudem können auch Lastgänge für Geräte bzw. Gerätegruppen erstellt werden, welche für Potenzialanalysen des Verbrauchsmanagement essenziell sind. Der wesentliche Unterschied besteht darin, dass viele Berechnungen nicht mehr auf deterministische Extremwerte beruhen, sondern die stochastischen Eigenschaften der Endabnehmer mit den resultierenden Lastgängen berücksichtigt werden<br>Distributed generation and novel loads such as electric vehicles and heat pumps require the development towards active distribution networks. Load curves are needed for the appropriate design process. This thesis presents a feasible and expandable synthesis of load curves, which is performed exemplary on residential customers with a period under review of 1 year and time steps of as little as 30 s. The data is collected for up-to-date appliances and current statics examining the way of life. The main focus lies on the input data for the synthesis and distinguishes between technical and social factors. Some thirty home appliances have been analyzed and are classified into five appliance classes by incorporating switching operations and power consumptions. The active power is the key figure for the technical perspective and the data is derived from manufacturer information. For the social perspective six different customer types are defined. They differ in sizes of household and housekeeping. The social key figures are appliance penetration rate and depending on the appliance class the turn-on time, turn-off time, operating duration or cycle duration. The elaborated two-stage synthesis is efficiently implemented in Matlab®. First, artificial load curves are created for each appliance of the households under consideration of the appliance class. In the second step, the individual load curves of the appliances are combined to load curves per line conductor. The algorithms have been validated in the implementation process by retracing the input data in the load curves. Also, the feasibility of the results is shown by comparing the key figures maximum load and power consumption to data in literature. The generated load curves allow for unsymmetrical calculations of distribution systems and can be used for probabilistic investigations of the charging of electric vehicles, the sizing of thermal storage combined with heat pumps or the integration of battery storage systems. A main advantage is the possibility to estimate the likelihood of operating conditions. The enhancement to further appliances and the changeability of the input data allows for versatile further possible investigations
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Beisler, Matthias Werner. "Modelling of input data uncertainty based on random set theory for evaluation of the financial feasibility for hydropower projects." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2011. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-71564.
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The design of hydropower projects requires a comprehensive planning process in order to achieve the objective to maximise exploitation of the existing hydropower potential as well as future revenues of the plant. For this purpose and to satisfy approval requirements for a complex hydropower development, it is imperative at planning stage, that the conceptual development contemplates a wide range of influencing design factors and ensures appropriate consideration of all related aspects. Since the majority of technical and economical parameters that are required for detailed and final design cannot be precisely determined at early planning stages, crucial design parameters such as design discharge and hydraulic head have to be examined through an extensive optimisation process. One disadvantage inherent to commonly used deterministic analysis is the lack of objectivity for the selection of input parameters. Moreover, it cannot be ensured that the entire existing parameter ranges and all possible parameter combinations are covered. Probabilistic methods utilise discrete probability distributions or parameter input ranges to cover the entire range of uncertainties resulting from an information deficit during the planning phase and integrate them into the optimisation by means of an alternative calculation method. The investigated method assists with the mathematical assessment and integration of uncertainties into the rational economic appraisal of complex infrastructure projects. The assessment includes an exemplary verification to what extent the Random Set Theory can be utilised for the determination of input parameters that are relevant for the optimisation of hydropower projects and evaluates possible improvements with respect to accuracy and suitability of the calculated results<br>Die Auslegung von Wasserkraftanlagen stellt einen komplexen Planungsablauf dar, mit dem Ziel das vorhandene Wasserkraftpotential möglichst vollständig zu nutzen und künftige, wirtschaftliche Erträge der Kraftanlage zu maximieren. Um dies zu erreichen und gleichzeitig die Genehmigungsfähigkeit eines komplexen Wasserkraftprojektes zu gewährleisten, besteht hierbei die zwingende Notwendigkeit eine Vielzahl für die Konzepterstellung relevanter Einflussfaktoren zu erfassen und in der Projektplanungsphase hinreichend zu berücksichtigen. In frühen Planungsstadien kann ein Großteil der für die Detailplanung entscheidenden, technischen und wirtschaftlichen Parameter meist nicht exakt bestimmt werden, wodurch maßgebende Designparameter der Wasserkraftanlage, wie Durchfluss und Fallhöhe, einen umfangreichen Optimierungsprozess durchlaufen müssen. Ein Nachteil gebräuchlicher, deterministischer Berechnungsansätze besteht in der zumeist unzureichenden Objektivität bei der Bestimmung der Eingangsparameter, sowie der Tatsache, dass die Erfassung der Parameter in ihrer gesamten Streubreite und sämtlichen, maßgeblichen Parameterkombinationen nicht sichergestellt werden kann. Probabilistische Verfahren verwenden Eingangsparameter in ihrer statistischen Verteilung bzw. in Form von Bandbreiten, mit dem Ziel, Unsicherheiten, die sich aus dem in der Planungsphase unausweichlichen Informationsdefizit ergeben, durch Anwendung einer alternativen Berechnungsmethode mathematisch zu erfassen und in die Berechnung einzubeziehen. Die untersuchte Vorgehensweise trägt dazu bei, aus einem Informationsdefizit resultierende Unschärfen bei der wirtschaftlichen Beurteilung komplexer Infrastrukturprojekte objektiv bzw. mathematisch zu erfassen und in den Planungsprozess einzubeziehen. Es erfolgt eine Beurteilung und beispielhafte Überprüfung, inwiefern die Random Set Methode bei Bestimmung der für den Optimierungsprozess von Wasserkraftanlagen relevanten Eingangsgrößen Anwendung finden kann und in wieweit sich hieraus Verbesserungen hinsichtlich Genauigkeit und Aussagekraft der Berechnungsergebnisse ergeben
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"Application of probabilistic load flow the expansion planning of power systems." Tese, MAXWELL, 1990. http://www.maxwell.lambda.ele.puc-rio.br/cgi-bin/db2www/PRG_0991.D2W/SHOW?Cont=9468:pt&Mat=&Sys=&Nr=&Fun=&CdLinPrg=pt.
Full textBook chapters on the topic "Probabilistic Power Flows"
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Tuinema, Bart W., José L. Rueda Torres, Alexandru I. Stefanov, Francisco M. Gonzalez-Longatt, and Mart A. M. M. van der Meijden. "Probabilistic Power Flow Analysis." In Probabilistic Reliability Analysis of Power Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43498-4_6.
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Zuluaga, Carlos D., and Mauricio A. Álvarez. "Approximate Probabilistic Power Flow." In Data Analytics for Renewable Energy Integration. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50947-1_5.
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Teimourzadeh, Saeed, and Behnam Mohammadi-Ivatloo. "Probabilistic Power Flow Module for PowerFactory DIgSILENT." In PowerFactory Applications for Power System Analysis. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12958-7_3.
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Jabari, Farkhondeh, Maryam Shamizadeh, and Behnam Mohammadi-Ivatloo. "Probabilistic Power Flow Analysis of Distribution Systems Using Monte Carlo Simulations." In Studies in Systems, Decision and Control. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34050-6_10.
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Li, Xue, Jia Cao, and Dajun Du. "Two-Point Estimate Method for Probabilistic Optimal Power Flow Computation Including Wind Farms with Correlated Parameters." In Intelligent Computing for Sustainable Energy and Environment. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37105-9_46.
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Li, Xue, Jianxia Pei, and Dajun Du. "A Combined Iteration Method for Probabilistic Load Flow Calculation Applied to Grid-Connected Induction Wind Power System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15621-2_32.
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LEITE DA SILVA, A. M., V. L. ARIENTI, and M. H. BARBOSA. "Probabilistic Techniques in Load Flow Analysis — A Practical Application." In Probabilistic Methods Applied to Electric Power Systems. Elsevier, 1987. http://dx.doi.org/10.1016/b978-0-08-031874-5.50071-1.
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Hasan, Osman, Awais Mahmood, and Syed Rafay Hasan. "Load Flow Analysis in Smart Grids." In Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch271.
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Load flow analysis is widely used to estimate the flow of various electrical parameters, such as the voltage, current and power, in power grids. These estimates allow us to effectively and reliably manage the given grid under random and uncertain conditions. Given the enormous amount of randomness and uncertainties in the factors that affect the smart grids, compared to traditional power grids, a complete and rigorous load flow analysis holds a vital role in ensuring the reliability of this safety-critical domain. In this chapter, we describe smart grids in terms of their basic components and then categorize the factors that affect the loads in smart grids. This is followed by a comprehensive survey of various existing load flow analysis techniques, i.e., numerical, computational intelligence and probabilistic.
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Hasan, Osman, Awais Mahmood, and Syed Rafay Hasan. "Load Flow Analysis in Smart Grids." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7359-3.ch014.
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Load flow analysis is widely used to estimate the flow of various electrical parameters such as the voltage, current, and power in power grids. These estimates allow us to effectively and reliably manage the given grid under random and uncertain conditions. Given the enormous amount of randomness and uncertainties in the factors that affect the smart grids, compared to traditional power grids, a complete and rigorous load flow analysis holds a vital role in ensuring the reliability of this safety-critical domain. In this chapter, the authors describe smart grids in terms of their basic components and then categorize the factors that affect the loads in smart grids. This is followed by a comprehensive survey of various existing load flow analysis techniques (i.e., numerical, computational intelligence, and probabilistic).
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Chicco, Gianfranco, Andrea Mazza, Angela Russo, Valeria Cocina, and Filippo Spertino. "Probabilistic Harmonic Power Flow Calculations with Uncertain and Correlated Data." In Smart and Sustainable Power Systems. CRC Press, 2015. http://dx.doi.org/10.1201/b18605-4.
Full textConference papers on the topic "Probabilistic Power Flows"
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Marah, B., and A. O. Ekwue. "Probabilistic load flows." In 2015 50th International Universities Power Engineering Conference (UPEC). IEEE, 2015. http://dx.doi.org/10.1109/upec.2015.7339770.
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Coroiu, F., C. Velicescu, and C. Barbulescu. "Probabilistic and deterministic load flows methods in power systems reliability estimation." In IEEE EUROCON 2011 - International Conference on Computer as a Tool. IEEE, 2011. http://dx.doi.org/10.1109/eurocon.2011.5929279.
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Khatavkar, V. V., Heramb Mayadeo, Poonam Dhabai, and A. A. Dharme. "Impact of probabilistic nature and location of wind generation on transmission power flows." In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016. http://dx.doi.org/10.1109/icacdot.2016.7877576.
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Tong, Michael T. "A Probabilistic Approach to Aeropropulsion System Assessment." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0001.
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A probabilistic approach is described for aeropropulsion system assessment. To demonstrate this approach, the technical performance of a wave rotor-enhanced gas turbine engine (i.e. engine net thrust, specific fuel consumption, and engine weight) is assessed. The assessment accounts for the uncertainties in component efficiencies/flows and mechanical design variables, using probability distributions. The results are presented in the form of cumulative distribution functions (CDFs) and sensitivity analyses, and are compared with those from the traditional deterministic approach. The comparison shows that the probabilistic approach provides a more realistic and systematic way to assess an aeropropulsion system.
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Tecza, Joseph, Peter Menegay, and Jay Koch. "Modeling and Evaluation of Centrifugal Compressor Performance Variations Using Probabilistic Analysis." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68464.
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This paper presents a methodology for analyzing the variation in compressor stage performance due to component dimensional variations for a range of flows, speeds, gas compositions and scale factors. Due to the large number of input parameters involved, a Design of Experiments (DOE) approach was used to develop key variables, and to develop response surface models of head coefficient and efficiency in terms of these key variables. These response surface-based performance models then are used for a probabilistic analysis of head coefficient and efficiency as functions of dimensional variations, for a range of compressor sizes. The variations in dimensions are expressed as probability distributions and evaluated using a Monte-Carlo integration technique. The techniques for developing the response surfaces and performing the probabilistic analysis are described, as are methods for evaluating both the effects of dimensional variation on performance and for evaluating how much dimensional variation can be tolerated before the variation exceeds established limits.
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Gupta, Neeraj. "A complete probabilistic power flow solution for transmission system." In 2012 IEEE Fifth Power India Conference. IEEE, 2012. http://dx.doi.org/10.1109/poweri.2012.6479469.
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Heinze, Sebastian, Kerstin Tageman, A˚ke Klang, and Anna Molker. "Quantitative Risk Assessment for the Performance Estimation of a Gas Turbine Upgrade." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22487.
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This paper presents a study that has been conducted within a gas turbine development project aiming at an upgrade of an existing twin-shaft gas turbine. There are a number of uncertainties, both in the original turbine as well as in the introduced modifications. These uncertainties must be taken into consideration when setting performance guarantee margins for the turbine. In order to quantify the impact of those uncertainties on the turbine performance, a probabilistic risk assessment was performed. Uncertain parameters for the compressor turbine were defined and upper and lower bounds as well as probability distributions were estimated. The MINITAB software was used to determine an experimental plan resulting in test points that were investigated with aerodynamic and secondary air flow tools. Based on results from this analysis, a second-order metamodel was determined. The metamodel was fed with 20000 random parameter combinations according to the parameter probability distributions, and efficiency, swallowing capacity and cooling air flow probability distributions were calculated. In a second step, the resulting values were used as uncertain input parameters to the gas turbine performance analysis considering the entire gas turbine including compressor and combustor. An experimental plan was determined based on calculated bounds for the compressor turbine efficiency, swallowing capacity and cooling air flows. A metamodel was calculated, and again 20000 parameter combinations were randomly generated based on the found parameter distributions. The parameters were fed to the metamodel of the entire gas turbine, and probability distributions for the gas turbine overall power and efficiency were found. Results from the investigations were used to set guarantee margins for power and efficiency.
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Ramanath, Vinay, and Gene E. Wiggs. "DACE Based Probabilistic Optimization of Mechanical Components." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91024.
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Application of DACE (Design and Analysis of Computer Experiments) methods for probabilistic design space exploration and optimization to the design of a mechanical component is demonstrated. The key part of the paper is focused on the problem formulation and process flow for performing a probabilistic optimization. The authors have shown that for computationally intensive problems, probabilistic optimization can be carried out efficiently within a DACE framework. For problems that are not costly to compute, direct probabilistic optimization can be carried out by the efficient integration of probabilistic analysis and global optimization (such as Genetic Algorithms). The strategy in the paper proves to be especially beneficial for those organizations that are reluctant to move to probabilistic methods and also for the current practitioners of probabilistics. The methodology is illustrated with examples from both simple and computationally intensive engineering problems.
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Paunescu, Doru, Oana Pop, Petru Andea, Eugen Raduca, and Cristian Craciun. "Power flow computing probabilistic approach." In 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2011. http://dx.doi.org/10.1109/saci.2011.5873076.
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Zhang, Haotian, Chun Sing Lai, Fangyuan Xu, and Loi Lei Lai. "Comparison between Probabilistic Optimal Power Flow and Probabilistic Power Flow with Carbon Emission Consideration." In 2015 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.123.
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