System Reliability Assessment Method for Wind Power Integration

Vallée, François; Lobry, Jacques; Deblecker, Olivier

doi:10.1109/tpwrs.2008.926090

Cited by 91 publications

(43 citation statements)

References 12 publications

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“…In theory, those simulations can include system effects which may not be possible without excessive approximation in a direct analytical approach and can generate a wide range of indices within a single study [9]. In fact, there are two basic techniques used when Monte Carlo methods are applied to power system reliability evaluation, these methods being known as the sequential and non-sequential techniques [1][2][3][4].…”

Section: The Implemented Sequential Monte Carlo Simulationmentioning

confidence: 99%

“…Practically, there are two ways to execute Monte Carlo algorithms: non-sequential and sequential techniques. Non-sequential Monte Carlo simulations generate a large number of system states to provide statistically reliable results but every state is independent from each other [1]. In order to take into account the chronology of wind speed variations and of load profiles, but also to be able to evaluate the benefits arising from load shifting solutions, sequential Monte Carlo simulations [2][3][4] must be used as they ensure a realistic transition between two successive states for each element of the power system.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Wind Geographical Correlation in Reliability Assessment Studies Using Sequential Monte Carlo Simulations

Vallée¹,

Toubeau²,

Grève³

et al. 2015

REPQJ

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Abstract.Electrical generation based on the use of renewable energies is emerging in modern grids. In that way, one of the most popular solutions as well in transmission as in distribution grids is certainly coming from wind energy. However, wind resources on a given location randomly fluctuate with time and have thus a major impact on the capacity of the electrical system to continuously face the load. In order to evaluate this impact and to consequently adapt required reinforcements, Monte Carlo simulations are often used. Those approach can be either sequential or not. Nowadays, load shifting solutions (storage, demand side management…) are practically set in order to adapt consumption to time varying generation without involving too consequent investments. In that way, sequential approach is currently preferred when it comes to long-term planning evaluation and adapted time series models are developed to characterize wind generation on a given site. The consideration of the geographical correlation between those models has been recently investigated in some references. This paper proposes to complete those contributions by evaluating the impact of wind geographical correlation on classical reliability indices such as the Loss of Load Expectation (LOLE) or the Expected Energy not Served (EENS).

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Section: The Implemented Sequential Monte Carlo Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Wind Geographical Correlation in Reliability Assessment Studies Using Sequential Monte Carlo Simulations

Vallée¹,

Toubeau²,

Grève³

et al. 2015

REPQJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…Correlation between wind power has been analyzed on its own [28] based on methods of simulation of correlated wind speed [29][30][31], on prediction [32], and also applied to different fields [26,33,34]. In this paper a method is presented to consider the correlations between generation, loads and wind power, therefore, correlation data have to be provided or estimated in order to obtain results for the PLF problem.…”

Section: State Of the Artmentioning

confidence: 99%

Probabilistic Load Flow Considering Correlation between Generation, Loads and Wind Power

Villanueva¹,

Feijóo²,

Pazos³

2011

SGRE

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In this paper a procedure is established for solving the Probabilistic Load Flow in an electrical power network, considering correlation between power generated by power plants, loads demanded on each bus and power injected by wind farms. The method proposed is based on the generation of correlated series of power values, which can be used in aMonteCarlo simulation, to obtain the probability density function of the power through branches of an electrical network.

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“…Some researchers developed new techniques to model wind power generation characteristics [12]. Security with the intermittent wind power is explored in [13].…”

Section: Introductionmentioning

confidence: 99%

Steady-state security in distribution networks with large wind farms

Jayaweera

Islam

2014

J. Mod. Power Syst. Clean Energy

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Aging network assets, forced and unforced outages, and the way the networks are operated in a deregulated market are of significant concerns to integrate large wind farms in a distribution network. In many cases, the constrained network capacity is a potential barrier to the large-scale integration of wind power. This paper probabilistically assesses the steady-state security in a distribution network in the presence of large wind farms. The approach incorporates active distribution network operating conditions, including intermittent power outputs, random outages, demand fluctuations, and dynamic interactions and exchanges, and then assesses the steady state security using Monte Carlo simulation. A case study is performed by integrating large wind farms into a distribution network. The results suggest that intermittent outputs of large wind farms in a distribution network can impact the steady-state security considerably. However, the level of impact of wind farms does not necessarily correlate with the installed capacity of them.

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System Reliability Assessment Method for Wind Power Integration

Cited by 91 publications

References 12 publications

Impact of Wind Geographical Correlation in Reliability Assessment Studies Using Sequential Monte Carlo Simulations

Impact of Wind Geographical Correlation in Reliability Assessment Studies Using Sequential Monte Carlo Simulations

Probabilistic Load Flow Considering Correlation between Generation, Loads and Wind Power

Steady-state security in distribution networks with large wind farms

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