A Comparison of Load Models for Composite Reliability Evaluation by Nonsequential Monte Carlo Simulation

Veliz, Fabiola Ferreira Clement; Borges, Carmen L. T.; Rei, Andrea M.

doi:10.1109/tpwrs.2009.2032354

Cited by 35 publications

(20 citation statements)

References 9 publications

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“…They are often used in physical and mathematical problems and are mainly used in three distinct problem classes: optimization, numerical integration and estimation... Those simulations are very useful when it is difficult or maybe impossible to have a deterministic model to compute desired parameters. The main idea is to write the parameter to compute as an expectation expression that we can approximate with an average [11]. The algorithm 2 is designed to implement Monte Carlo principles in order to compute introduces KPIs in the previous sub-section:…”

Section: Simulation Algorithmmentioning

confidence: 99%

Statistical Learning Based Framework for Random Networks Knowledge Extraction Applied in Smart Cities

Tigani¹,

Ouzzif²,

Saadane

2016

Lecture Notes in Electrical Engineering

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Smart Cities are a future reality that emerged recently. They became a wide research field around the world. These cities will combine the power of ubiquitous communication networks and wireless sensors with the efficient management systems to solve daily challenges and create exciting services. In this work, we involve the power of artificial intelligence to solve one of the serious challenges in big cities. This concerns the traffic management and prediction. This work proposes a statistical model serving the analysis of a random graph that represents, in reality, roads on map. Using those models and collected data from sensors or human agents, we can extract useful hidden knowledge for the best decision making. To prove the reliability of the approach, a Monte Carlo simulation algorithm is designed and results confirms the added-value of the approach.

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Section: Simulation Algorithmmentioning

confidence: 99%

Statistical Learning Based Framework for Random Networks Knowledge Extraction Applied in Smart Cities

Tigani¹,

Ouzzif²,

Saadane

2016

Lecture Notes in Electrical Engineering

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“…year or a season). To model the dynamic behavior of loads, many multi-state models have been proposed ranging from a single load-aggregated representation up to more complex individual load modeling (Veliz et al 2010). Load-aggregated models (Ding et al 2011, Hegazy et al 2003 resort to the load duration curve (LDC) model to reduce the number of load levels (Billinton and Allan 1996), and consider only one geological area pattern; and divides the sorted load values into states (Fig.…”

Section: Loadmentioning

confidence: 99%

A multi-state model for the reliability assessment of a distributed generation system via universal generating function

Li¹,

Zio²

2012

Reliability Engineering & System Safety

136

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International audienceThe current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology (e.g. wind or solar, etc.) whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation (DG). The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation/failure behavior of the generation systems. The universal generating function (UGF) technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices (e.g. loss of load expectation (LOLE), expected energy not supplied (EENS)) are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder

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“…Considering the DG units of Figure 1, this compound power output is: In literature, Monte Carlo simulation (MCS) is the mainstream tool for adequacy assessment studies (Billinton et al 2009, El-Khattam et al 2006, Hegazy et al 2003. (Veliz et al 2010). The non-sequential MCS samples the state of all components and combines them to form the system state; it is most efficient, providing comparable accuracy to sequential MCS in shorter execution time (Veliz et al 2010).…”

Section: Distributed Generation System Modelmentioning

confidence: 99%

“…(Veliz et al 2010). The non-sequential MCS samples the state of all components and combines them to form the system state; it is most efficient, providing comparable accuracy to sequential MCS in shorter execution time (Veliz et al 2010). In our work, the non-sequential MCS is used.…”

Section: Distributed Generation System Modelmentioning

confidence: 99%

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Uncertainty analysis of the adequacy assessment model of a distributed generation system

Zio

2012

Renewable Energy

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Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels.2

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A Comparison of Load Models for Composite Reliability Evaluation by Nonsequential Monte Carlo Simulation

Cited by 35 publications

References 9 publications

Statistical Learning Based Framework for Random Networks Knowledge Extraction Applied in Smart Cities

Statistical Learning Based Framework for Random Networks Knowledge Extraction Applied in Smart Cities

A multi-state model for the reliability assessment of a distributed generation system via universal generating function

Uncertainty analysis of the adequacy assessment model of a distributed generation system

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