On the use of dynamic reliability for an accurate modelling of renewable power plants

Chiacchio, Francesco; D’Urso, Diego; Famoso, Fabio; Brusca, S.; Aizpurua, Jose Ignacio; Catterson, Victoria M.

doi:10.1016/j.energy.2018.03.101

Cited by 25 publications

(10 citation statements)

References 65 publications

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“…Table 2 resumes the main gates of a DFT model as taken by [53]. Nowadays, Monte Carlo simulation is the best option for solving such models which can offer a good trade-off between precision and accuracy, (i.e., accuracy improves with the number of iterations that causes an increasing of the time of computation) [58]. In point of fact, the simulation approach has further favored the conception of advanced methodologies to improve the realism of a model.…”

Section: B Dynamic and Hybrid Fault Treesmentioning

confidence: 99%

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

2021

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Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not the possibility to treat or use this type of gas. Concerns about global warming led to several initiatives for reducing flaring or even eliminating combustion. Treating flare gas was made possible by the introduction of flare gas recovery systems that have become increasingly obligatory. Most solutions add a flare gas recovery system to an existing flare system. In a typical scenario, after analyzing the existing facility and collecting the necessary data, alternative designs are proposed and criteria are determined to make a choice between the proposed alternatives. In this paper two designs of a gas control system are proposed, and reliability was chosen as the deciding factor. Using repairable dynamic fault trees, the failure models of the two designs have been implemented. Afterwards, a novel hybrid technique, the Stochastic Hybrid Fault Tree Automaton, is used to model the working conditions in which the system operates, with the aim to achieve a more realistic assessment and evaluate the disaster likelihood associated to these failures. It is shown that the latter enables a richer analysis where the effects of failure can be better assessed. This is important for correct choice between design alternatives because, as shown in the case study, the results of the two analyses can lead to contrasting conclusions of the solution to adopt. Further investigations have been carried out focusing on the safety sub-systems and on the basic events in each design. The Importance Measure analysis revealed that some of the components were responsible for most of the critical failures, thus locating some areas of possible design improvement.

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Section: B Dynamic and Hybrid Fault Treesmentioning

confidence: 99%

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

2021

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“…Renewable technologies, such as solar energy [2], biomass energy conversion systems [3][4][5], and especially wind power plants [6], are usually considered to be less efficient than traditional conversion systems because of the inherent random nature of the primary renewable source. This implies some difficulties to forecast energy production of such complex systems.…”

Section: Introductionmentioning

confidence: 99%

On the wake effect in wind farm power forecasting: a new data-driven approach

et al. 2020

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Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.

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“…The model of the case of study has been designed using a Stochastic Hybrid Fault Tree Automaton (SHyFTA) [17][18][19]. One of the main contributions of this paper has been the utilization of this modelling technique that allows for the account of the physical process of energy conversion and for the stochastic process of failure and repair of the system components (like the aging of the battery, inverter, and PV panels as proposed in [20][21][22][23][24]).…”

Section: Introductionmentioning

confidence: 99%

Performance and Economic Assessment of a Grid-Connected Photovoltaic Power Plant with a Storage System: A Comparison between the North and the South of Italy

et al. 2019

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Grid-connected low voltage photovoltaic power plants cover most of the power capacity installed in Italy. They offer an important contribution to the power demand of the utilities connected but, due to the nature of the solar resource, the night-time consumption can be satisfied only withdrawing the energy by the national grid, at the price of the energy distributor. Thanks to the improvement of storage technologies, the installation of a system of battery looks like a promising solution by giving the possibility to increase auto-consumption dramatically. In this paper, a model-based approach to analyze and discuss the performance and the economic feasibility of grid-connected domestic photovoltaic power plants with a storage system is presented. Using as input to the model the historical series (2008–2017) of the main ambient variables, the proposed model, based on Stochastic Hybrid Fault Tree Automaton, allowed us to simulate and compare two alternative technical solutions characterized by different environmental conditions, in the north and in the south of Italy. The performances of these systems were compared and an economic analysis, addressing the convenience of the storage systems was carried out, considering the characteristic useful-life time, 20 years, of a photovoltaic power plant. To this end the Net Present Value and the payback time were evaluated, considering the main characteristics of the Italian market scenario.

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On the use of dynamic reliability for an accurate modelling of renewable power plants

Cited by 25 publications

References 65 publications

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

On the wake effect in wind farm power forecasting: a new data-driven approach

Performance and Economic Assessment of a Grid-Connected Photovoltaic Power Plant with a Storage System: A Comparison between the North and the South of Italy

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